From 15155ceae9e88f405c7cbdcc70cc6993b73082dd Mon Sep 17 00:00:00 2001 From: DamnPat Date: Mon, 6 Jan 2025 18:43:35 +0800 Subject: [PATCH] update latest code --- agents/maigctester/.evn.example | 0 agents/maigctester/.gitignore | 2 - agents/maigctester/README.md | 54 - agents/maigctester/more.md | 0 agents/maigctester/pyproject.toml | 20 - .../maigctester/src/maigctester/__init__.py | 0 .../src/maigctester/config/agents.yaml | 19 - .../src/maigctester/config/tasks.yaml | 17 - agents/maigctester/src/maigctester/crew.py | 62 - .../maigctester/src/maigctester/fi/README.md | 0 .../src/maigctester/fi/__init__.py | 0 .../src/maigctester/fi/agents/fundamentals.py | 167 - .../src/maigctester/fi/agents/market_data.py | 74 - .../fi/agents/portfolio_manager.py | 117 - .../src/maigctester/fi/agents/risk_manager.py | 159 - .../src/maigctester/fi/agents/sentiment.py | 59 - .../src/maigctester/fi/agents/state.py | 51 - .../src/maigctester/fi/agents/technicals.py | 652 ---- .../src/maigctester/fi/backtester.py | 153 - agents/maigctester/src/maigctester/fi/main.py | 100 - .../src/maigctester/fi/tools/api.py | 152 - agents/maigctester/src/maigctester/main.py | 58 - .../src/maigctester/tools/__init__.py | 0 .../src/maigctester/tools/custom_tool.py | 19 - agents/promptize/.gitignore | 12 - agents/promptize/intro.md | 0 agents/promptize/src/promptize/app.py | 8 - .../promptize/src/promptize/conf/__init__.py | 0 .../src/promptize/providers/__init__.py | 10 - .../promptize/providers/anthropic_gradio.py | 76 - .../src/promptize/providers/cohere_gradio.py | 120 - .../src/promptize/providers/crewai_gradio.py | 363 --- .../promptize/providers/deepseek_gradio.py | 120 - .../promptize/providers/fireworks_gradio.py | 257 -- .../src/promptize/providers/gemini_gradio.py | 760 ----- .../promptize/providers/hyperbolic_gradio.py | 326 -- .../src/promptize/providers/lumaai_gradio.py | 164 - .../src/promptize/providers/openai_gradio.py | 235 -- .../src/promptize/providers/qwen_gradio.py | 109 - .../promptize/providers/sambanova_gradio.py | 120 - .../promptize/providers/together_gradio.py | 120 - .../src/promptize/providers/xai_gradio.py | 105 - .../qpyapp/src/qpycases/auto_cut/__init__.py | 0 datafarm/primary/README.md | 38 +- datafarm/primary/pyproject.toml | 1 + .../primary/src/primary/adspower/README.md | 201 ++ .../src/primary/adspower/_api_entity.py | 2 + .../src/primary/adspower/_base_category.py | 5 + .../src/primary/adspower/_base_group.py | 8 +- .../src/primary/adspower/_base_http_client.py | 2 +- .../src/primary/adspower/_base_profile_api.py | 6 +- .../adspower/async_api/_base_profile.py | 2 +- .../primary/adspower/async_api/category.py | 2 +- .../src/primary/adspower/async_api/group.py | 2 +- .../primary/adspower/async_api/http_client.py | 4 +- .../adspower/async_api/playwright/profile.py | 8 +- .../primary/adspower/async_api/profile_api.py | 6 +- .../src/primary/adspower/exceptions.py | 2 +- .../adspower/sync_api/_base_profile.py | 2 +- .../src/primary/adspower/sync_api/category.py | 2 +- .../src/primary/adspower/sync_api/group.py | 2 +- .../primary/adspower/sync_api/http_client.py | 4 +- .../adspower/sync_api/playwright/profile.py | 6 +- .../primary/adspower/sync_api/profile_api.py | 4 +- .../adspower/sync_api/selenium/profile.py | 8 +- .../primary/src/primary/adspower/types.py | 6 +- pyproject.toml | 2 +- uv.lock | 2831 ++++++++++++----- 68 files changed, 2327 insertions(+), 5669 deletions(-) delete mode 100644 agents/maigctester/.evn.example delete mode 100644 agents/maigctester/.gitignore delete mode 100644 agents/maigctester/README.md delete mode 100644 agents/maigctester/more.md delete mode 100644 agents/maigctester/pyproject.toml delete mode 100644 agents/maigctester/src/maigctester/__init__.py delete mode 100644 agents/maigctester/src/maigctester/config/agents.yaml delete mode 100644 agents/maigctester/src/maigctester/config/tasks.yaml delete mode 100644 agents/maigctester/src/maigctester/crew.py delete mode 100644 agents/maigctester/src/maigctester/fi/README.md delete mode 100644 agents/maigctester/src/maigctester/fi/__init__.py delete mode 100644 agents/maigctester/src/maigctester/fi/agents/fundamentals.py delete mode 100644 agents/maigctester/src/maigctester/fi/agents/market_data.py delete mode 100644 agents/maigctester/src/maigctester/fi/agents/portfolio_manager.py delete mode 100644 agents/maigctester/src/maigctester/fi/agents/risk_manager.py delete mode 100644 agents/maigctester/src/maigctester/fi/agents/sentiment.py delete mode 100644 agents/maigctester/src/maigctester/fi/agents/state.py delete mode 100644 agents/maigctester/src/maigctester/fi/agents/technicals.py delete mode 100644 agents/maigctester/src/maigctester/fi/backtester.py delete mode 100644 agents/maigctester/src/maigctester/fi/main.py delete mode 100644 agents/maigctester/src/maigctester/fi/tools/api.py delete mode 100644 agents/maigctester/src/maigctester/main.py delete mode 100644 agents/maigctester/src/maigctester/tools/__init__.py delete mode 100644 agents/maigctester/src/maigctester/tools/custom_tool.py delete mode 100644 agents/promptize/.gitignore delete mode 100644 agents/promptize/intro.md delete mode 100644 agents/promptize/src/promptize/app.py delete mode 100644 agents/promptize/src/promptize/conf/__init__.py delete mode 100644 agents/promptize/src/promptize/providers/__init__.py delete mode 100644 agents/promptize/src/promptize/providers/anthropic_gradio.py delete mode 100644 agents/promptize/src/promptize/providers/cohere_gradio.py delete mode 100644 agents/promptize/src/promptize/providers/crewai_gradio.py delete mode 100644 agents/promptize/src/promptize/providers/deepseek_gradio.py delete mode 100644 agents/promptize/src/promptize/providers/fireworks_gradio.py delete mode 100644 agents/promptize/src/promptize/providers/gemini_gradio.py delete mode 100644 agents/promptize/src/promptize/providers/hyperbolic_gradio.py delete mode 100644 agents/promptize/src/promptize/providers/lumaai_gradio.py delete mode 100644 agents/promptize/src/promptize/providers/openai_gradio.py delete mode 100644 agents/promptize/src/promptize/providers/qwen_gradio.py delete mode 100644 agents/promptize/src/promptize/providers/sambanova_gradio.py delete mode 100644 agents/promptize/src/promptize/providers/together_gradio.py delete mode 100644 agents/promptize/src/promptize/providers/xai_gradio.py delete mode 100644 appkits/qpyapp/src/qpycases/auto_cut/__init__.py diff --git a/agents/maigctester/.evn.example b/agents/maigctester/.evn.example deleted file mode 100644 index e69de29..0000000 diff --git a/agents/maigctester/.gitignore b/agents/maigctester/.gitignore deleted file mode 100644 index d50a09f..0000000 --- a/agents/maigctester/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -.env -__pycache__/ diff --git a/agents/maigctester/README.md b/agents/maigctester/README.md deleted file mode 100644 index 3a30966..0000000 --- a/agents/maigctester/README.md +++ /dev/null @@ -1,54 +0,0 @@ -# Maigctester Crew - -Welcome to the Maigctester Crew project, powered by [crewAI](https://crewai.com). This template is designed to help you set up a multi-agent AI system with ease, leveraging the powerful and flexible framework provided by crewAI. Our goal is to enable your agents to collaborate effectively on complex tasks, maximizing their collective intelligence and capabilities. - -## Installation - -Ensure you have Python >=3.10 <=3.13 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience. - -First, if you haven't already, install uv: - -```bash -pip install uv -``` - -Next, navigate to your project directory and install the dependencies: - -(Optional) Lock the dependencies and install them by using the CLI command: -```bash -crewai install -``` -### Customizing - -**Add your `OPENAI_API_KEY` into the `.env` file** - -- Modify `src/maigctester/config/agents.yaml` to define your agents -- Modify `src/maigctester/config/tasks.yaml` to define your tasks -- Modify `src/maigctester/crew.py` to add your own logic, tools and specific args -- Modify `src/maigctester/main.py` to add custom inputs for your agents and tasks - -## Running the Project - -To kickstart your crew of AI agents and begin task execution, run this from the root folder of your project: - -```bash -$ crewai run -``` - -This command initializes the maigctester Crew, assembling the agents and assigning them tasks as defined in your configuration. - -This example, unmodified, will run the create a `report.md` file with the output of a research on LLMs in the root folder. - -## Understanding Your Crew - -The maigctester Crew is composed of multiple AI agents, each with unique roles, goals, and tools. These agents collaborate on a series of tasks, defined in `config/tasks.yaml`, leveraging their collective skills to achieve complex objectives. The `config/agents.yaml` file outlines the capabilities and configurations of each agent in your crew. - -## Support - -For support, questions, or feedback regarding the Maigctester Crew or crewAI. -- Visit our [documentation](https://docs.crewai.com) -- Reach out to us through our [GitHub repository](https://github.com/joaomdmoura/crewai) -- [Join our Discord](https://discord.com/invite/X4JWnZnxPb) -- [Chat with our docs](https://chatg.pt/DWjSBZn) - -Let's create wonders together with the power and simplicity of crewAI. diff --git a/agents/maigctester/more.md b/agents/maigctester/more.md deleted file mode 100644 index e69de29..0000000 diff --git a/agents/maigctester/pyproject.toml b/agents/maigctester/pyproject.toml deleted file mode 100644 index c201a95..0000000 --- a/agents/maigctester/pyproject.toml +++ /dev/null @@ -1,20 +0,0 @@ -[project] -name = "maigctester" -version = "0.1.0" -description = "maigctester using crewAI" -authors = [{ name = "Your Name", email = "you@example.com" }] -requires-python = ">=3.10" -dependencies = [ - "crewai[tools]>=0.86.0,<1.0.0" -] - -[project.scripts] -maigctester = "maigctester.main:run" -run_crew = "maigctester.main:run" -train = "maigctester.main:train" -replay = "maigctester.main:replay" -test = "maigctester.main:test" - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" diff --git a/agents/maigctester/src/maigctester/__init__.py b/agents/maigctester/src/maigctester/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/agents/maigctester/src/maigctester/config/agents.yaml b/agents/maigctester/src/maigctester/config/agents.yaml deleted file mode 100644 index 72ed693..0000000 --- a/agents/maigctester/src/maigctester/config/agents.yaml +++ /dev/null @@ -1,19 +0,0 @@ -researcher: - role: > - {topic} Senior Data Researcher - goal: > - Uncover cutting-edge developments in {topic} - backstory: > - You're a seasoned researcher with a knack for uncovering the latest - developments in {topic}. Known for your ability to find the most relevant - information and present it in a clear and concise manner. - -reporting_analyst: - role: > - {topic} Reporting Analyst - goal: > - Create detailed reports based on {topic} data analysis and research findings - backstory: > - You're a meticulous analyst with a keen eye for detail. You're known for - your ability to turn complex data into clear and concise reports, making - it easy for others to understand and act on the information you provide. \ No newline at end of file diff --git a/agents/maigctester/src/maigctester/config/tasks.yaml b/agents/maigctester/src/maigctester/config/tasks.yaml deleted file mode 100644 index f308208..0000000 --- a/agents/maigctester/src/maigctester/config/tasks.yaml +++ /dev/null @@ -1,17 +0,0 @@ -research_task: - description: > - Conduct a thorough research about {topic} - Make sure you find any interesting and relevant information given - the current year is 2024. - expected_output: > - A list with 10 bullet points of the most relevant information about {topic} - agent: researcher - -reporting_task: - description: > - Review the context you got and expand each topic into a full section for a report. - Make sure the report is detailed and contains any and all relevant information. - expected_output: > - A fully fledge reports with the mains topics, each with a full section of information. - Formatted as markdown without '```' - agent: reporting_analyst diff --git a/agents/maigctester/src/maigctester/crew.py b/agents/maigctester/src/maigctester/crew.py deleted file mode 100644 index ef682f0..0000000 --- a/agents/maigctester/src/maigctester/crew.py +++ /dev/null @@ -1,62 +0,0 @@ -from crewai import Agent, Crew, Process, Task -from crewai.project import CrewBase, agent, crew, task - -# If you want to run a snippet of code before or after the crew starts, -# you can use the @before_kickoff and @after_kickoff decorators -# https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators - -@CrewBase -class Maigctester(): - """Maigctester crew""" - - # Learn more about YAML configuration files here: - # Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended - # Tasks: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended - agents_config = 'config/agents.yaml' - tasks_config = 'config/tasks.yaml' - - # If you would like to add tools to your agents, you can learn more about it here: - # https://docs.crewai.com/concepts/agents#agent-tools - @agent - def researcher(self) -> Agent: - return Agent( - config=self.agents_config['researcher'], - verbose=True - ) - - @agent - def reporting_analyst(self) -> Agent: - return Agent( - config=self.agents_config['reporting_analyst'], - verbose=True - ) - - # To learn more about structured task outputs, - # task dependencies, and task callbacks, check out the documentation: - # https://docs.crewai.com/concepts/tasks#overview-of-a-task - @task - def research_task(self) -> Task: - return Task( - config=self.tasks_config['research_task'], - ) - - @task - def reporting_task(self) -> Task: - return Task( - config=self.tasks_config['reporting_task'], - output_file='report.md' - ) - - @crew - def crew(self) -> Crew: - """Creates the Maigctester crew""" - # To learn how to add knowledge sources to your crew, check out the documentation: - # https://docs.crewai.com/concepts/knowledge#what-is-knowledge - - return Crew( - agents=self.agents, # Automatically created by the @agent decorator - tasks=self.tasks, # Automatically created by the @task decorator - process=Process.sequential, - verbose=True, - # process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/ - ) diff --git a/agents/maigctester/src/maigctester/fi/README.md b/agents/maigctester/src/maigctester/fi/README.md deleted file mode 100644 index e69de29..0000000 diff --git a/agents/maigctester/src/maigctester/fi/__init__.py b/agents/maigctester/src/maigctester/fi/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/agents/maigctester/src/maigctester/fi/agents/fundamentals.py b/agents/maigctester/src/maigctester/fi/agents/fundamentals.py deleted file mode 100644 index 8b5dc04..0000000 --- a/agents/maigctester/src/maigctester/fi/agents/fundamentals.py +++ /dev/null @@ -1,167 +0,0 @@ - -from langchain_core.messages import HumanMessage - -from agents.state import AgentState, show_agent_reasoning - -import json - -##### Fundamental Agent ##### -def fundamentals_agent(state: AgentState): - """Analyzes fundamental data and generates trading signals.""" - show_reasoning = state["metadata"]["show_reasoning"] - data = state["data"] - metrics = data["financial_metrics"][0] - financial_line_item = data["financial_line_items"][0] - market_cap = data["market_cap"] - - # Initialize signals list for different fundamental aspects - signals = [] - reasoning = {} - - # 1. Profitability Analysis - profitability_score = 0 - if metrics["return_on_equity"] > 0.15: # Strong ROE above 15% - profitability_score += 1 - if metrics["net_margin"] > 0.20: # Healthy profit margins - profitability_score += 1 - if metrics["operating_margin"] > 0.15: # Strong operating efficiency - profitability_score += 1 - - signals.append('bullish' if profitability_score >= 2 else 'bearish' if profitability_score == 0 else 'neutral') - reasoning["Profitability"] = { - "signal": signals[0], - "details": f"ROE: {metrics['return_on_equity']:.2%}, Net Margin: {metrics['net_margin']:.2%}, Op Margin: {metrics['operating_margin']:.2%}" - } - - # 2. Growth Analysis - growth_score = 0 - if metrics["revenue_growth"] > 0.10: # 10% revenue growth - growth_score += 1 - if metrics["earnings_growth"] > 0.10: # 10% earnings growth - growth_score += 1 - if metrics["book_value_growth"] > 0.10: # 10% book value growth - growth_score += 1 - - signals.append('bullish' if growth_score >= 2 else 'bearish' if growth_score == 0 else 'neutral') - reasoning["Growth"] = { - "signal": signals[1], - "details": f"Revenue Growth: {metrics['revenue_growth']:.2%}, Earnings Growth: {metrics['earnings_growth']:.2%}" - } - - # 3. Financial Health - health_score = 0 - if metrics["current_ratio"] > 1.5: # Strong liquidity - health_score += 1 - if metrics["debt_to_equity"] < 0.5: # Conservative debt levels - health_score += 1 - if metrics["free_cash_flow_per_share"] > metrics["earnings_per_share"] * 0.8: # Strong FCF conversion - health_score += 1 - - signals.append('bullish' if health_score >= 2 else 'bearish' if health_score == 0 else 'neutral') - reasoning["Financial_Health"] = { - "signal": signals[2], - "details": f"Current Ratio: {metrics['current_ratio']:.2f}, D/E: {metrics['debt_to_equity']:.2f}" - } - - # 4. Price to X ratios - pe_ratio = metrics["price_to_earnings_ratio"] - pb_ratio = metrics["price_to_book_ratio"] - ps_ratio = metrics["price_to_sales_ratio"] - - price_ratio_score = 0 - if pe_ratio < 25: # Reasonable P/E ratio - price_ratio_score += 1 - if pb_ratio < 3: # Reasonable P/B ratio - price_ratio_score += 1 - if ps_ratio < 5: # Reasonable P/S ratio - price_ratio_score += 1 - - signals.append('bullish' if price_ratio_score >= 2 else 'bearish' if price_ratio_score == 0 else 'neutral') - reasoning["Price_Ratios"] = { - "signal": signals[3], - "details": f"P/E: {pe_ratio:.2f}, P/B: {pb_ratio:.2f}, P/S: {ps_ratio:.2f}" - } - - # 5. Calculate intrinsic value and compare to market cap - free_cash_flow = financial_line_item.get('free_cash_flow') - intrinsic_value = calculate_intrinsic_value( - free_cash_flow=free_cash_flow, - growth_rate=metrics["earnings_growth"], - discount_rate=0.10, - terminal_growth_rate=0.03, - num_years=5, - ) - if market_cap < intrinsic_value: - signals.append('bullish') - else: - signals.append('bearish') - - reasoning["Intrinsic_Value"] = { - "signal": signals[4], - "details": f"Intrinsic Value: ${intrinsic_value:,.2f}, Market Cap: ${market_cap:,.2f}" - } - - # Determine overall signal - bullish_signals = signals.count('bullish') - bearish_signals = signals.count('bearish') - - if bullish_signals > bearish_signals: - overall_signal = 'bullish' - elif bearish_signals > bullish_signals: - overall_signal = 'bearish' - else: - overall_signal = 'neutral' - - # Calculate confidence level - total_signals = len(signals) - confidence = max(bullish_signals, bearish_signals) / total_signals - - message_content = { - "signal": overall_signal, - "confidence": f"{round(confidence * 100)}%", - "reasoning": reasoning - } - - # Create the fundamental analysis message - message = HumanMessage( - content=json.dumps(message_content), - name="fundamentals_agent", - ) - - # Print the reasoning if the flag is set - if show_reasoning: - show_agent_reasoning(message_content, "Fundamental Analysis Agent") - - return { - "messages": [message], - "data": data, - } - -def calculate_intrinsic_value( - free_cash_flow: float, - growth_rate: float = 0.05, - discount_rate: float = 0.10, - terminal_growth_rate: float = 0.02, - num_years: int = 5, -) -> float: - """ - Computes the discounted cash flow (DCF) for a given company based on the current free cash flow. - Use this function to calculate the intrinsic value of a stock. - """ - # Estimate the future cash flows based on the growth rate - cash_flows = [free_cash_flow * (1 + growth_rate) ** i for i in range(num_years)] - - # Calculate the present value of projected cash flows - present_values = [] - for i in range(num_years): - present_value = cash_flows[i] / (1 + discount_rate) ** (i + 1) - present_values.append(present_value) - - # Calculate the terminal value - terminal_value = cash_flows[-1] * (1 + terminal_growth_rate) / (discount_rate - terminal_growth_rate) - terminal_present_value = terminal_value / (1 + discount_rate) ** num_years - - # Sum up the present values and terminal value - dcf_value = sum(present_values) + terminal_present_value - - return dcf_value \ No newline at end of file diff --git a/agents/maigctester/src/maigctester/fi/agents/market_data.py b/agents/maigctester/src/maigctester/fi/agents/market_data.py deleted file mode 100644 index 735ce2a..0000000 --- a/agents/maigctester/src/maigctester/fi/agents/market_data.py +++ /dev/null @@ -1,74 +0,0 @@ - -from langchain_openai.chat_models import ChatOpenAI - -from agents.state import AgentState -from tools.api import search_line_items, get_financial_metrics, get_insider_trades, get_market_cap, get_prices - -from datetime import datetime - -llm = ChatOpenAI(model="gpt-4o") - -def market_data_agent(state: AgentState): - """Responsible for gathering and preprocessing market data""" - messages = state["messages"] - data = state["data"] - - # Set default dates - end_date = data["end_date"] or datetime.now().strftime('%Y-%m-%d') - if not data["start_date"]: - # Calculate 3 months before end_date - end_date_obj = datetime.strptime(end_date, '%Y-%m-%d') - start_date = end_date_obj.replace(month=end_date_obj.month - 3) if end_date_obj.month > 3 else \ - end_date_obj.replace(year=end_date_obj.year - 1, month=end_date_obj.month + 9) - start_date = start_date.strftime('%Y-%m-%d') - else: - start_date = data["start_date"] - - # Get the historical price data - prices = get_prices( - ticker=data["ticker"], - start_date=start_date, - end_date=end_date, - ) - - # Get the financial metrics - financial_metrics = get_financial_metrics( - ticker=data["ticker"], - report_period=end_date, - period='ttm', - limit=1, - ) - - # Get the insider trades - insider_trades = get_insider_trades( - ticker=data["ticker"], - end_date=end_date, - limit=5, - ) - - # Get the market cap - market_cap = get_market_cap( - ticker=data["ticker"], - ) - - # Get the line_items - financial_line_items = search_line_items( - ticker=data["ticker"], - line_items=["free_cash_flow"], - period='ttm', - limit=1, - ) - - return { - "messages": messages, - "data": { - **data, - "prices": prices, - "start_date": start_date, - "end_date": end_date, - "financial_metrics": financial_metrics, - "insider_trades": insider_trades, - "market_cap": market_cap, - "financial_line_items": financial_line_items, - } - } \ No newline at end of file diff --git a/agents/maigctester/src/maigctester/fi/agents/portfolio_manager.py b/agents/maigctester/src/maigctester/fi/agents/portfolio_manager.py deleted file mode 100644 index 01960a0..0000000 --- a/agents/maigctester/src/maigctester/fi/agents/portfolio_manager.py +++ /dev/null @@ -1,117 +0,0 @@ - -from langchain_core.messages import HumanMessage -from langchain_core.prompts import ChatPromptTemplate -from langchain_openai.chat_models import ChatOpenAI - -from agents.state import AgentState, show_agent_reasoning - - -##### Portfolio Management Agent ##### -def portfolio_management_agent(state: AgentState): - """Makes final trading decisions and generates orders""" - show_reasoning = state["metadata"]["show_reasoning"] - portfolio = state["data"]["portfolio"] - - # Get the technical analyst, fundamentals agent, and risk management agent messages - technical_message = next(msg for msg in state["messages"] if msg.name == "technical_analyst_agent") - fundamentals_message = next(msg for msg in state["messages"] if msg.name == "fundamentals_agent") - sentiment_message = next(msg for msg in state["messages"] if msg.name == "sentiment_agent") - risk_message = next(msg for msg in state["messages"] if msg.name == "risk_management_agent") - - # Create the prompt template - template = ChatPromptTemplate.from_messages( - [ - ( - "system", - """You are a portfolio manager making final trading decisions. - Your job is to make a trading decision based on the team's analysis while strictly adhering - to risk management constraints. - - RISK MANAGEMENT CONSTRAINTS: - - You MUST NOT exceed the max_position_size specified by the risk manager - - You MUST follow the trading_action (buy/sell/hold) recommended by risk management - - These are hard constraints that cannot be overridden by other signals - - When weighing the different signals for direction and timing: - 1. Fundamental Analysis (50% weight) - - Primary driver of trading decisions - - Should determine overall direction - - 2. Technical Analysis (35% weight) - - Secondary confirmation - - Helps with entry/exit timing - - 3. Sentiment Analysis (15% weight) - - Final consideration - - Can influence sizing within risk limits - - The decision process should be: - 1. First check risk management constraints - 2. Then evaluate fundamental outlook - 3. Use technical analysis for timing - 4. Consider sentiment for final adjustment - - Provide the following in your output: - - "action": "buy" | "sell" | "hold", - - "quantity": - - "confidence": - - "agent_signals": - - "reasoning": - - Trading Rules: - - Never exceed risk management position limits - - Only buy if you have available cash - - Only sell if you have shares to sell - - Quantity must be ≤ current position for sells - - Quantity must be ≤ max_position_size from risk management""" - ), - ( - "human", - """Based on the team's analysis below, make your trading decision. - - Technical Analysis Trading Signal: {technical_message} - Fundamental Analysis Trading Signal: {fundamentals_message} - Sentiment Analysis Trading Signal: {sentiment_message} - Risk Management Trading Signal: {risk_message} - - Here is the current portfolio: - Portfolio: - Cash: {portfolio_cash} - Current Position: {portfolio_stock} shares - - Only include the action, quantity, reasoning, confidence, and agent_signals in your output as JSON. Do not include any JSON markdown. - - Remember, the action must be either buy, sell, or hold. - You can only buy if you have available cash. - You can only sell if you have shares in the portfolio to sell. - """ - ), - ] - ) - - # Generate the prompt - prompt = template.invoke( - { - "technical_message": technical_message.content, - "fundamentals_message": fundamentals_message.content, - "sentiment_message": sentiment_message.content, - "risk_message": risk_message.content, - "portfolio_cash": f"{portfolio['cash']:.2f}", - "portfolio_stock": portfolio["stock"] - } - ) - # Invoke the LLM - llm = ChatOpenAI(model="gpt-4o") - result = llm.invoke(prompt) - - # Create the portfolio management message - message = HumanMessage( - content=result.content, - name="portfolio_management", - ) - - # Print the decision if the flag is set - if show_reasoning: - show_agent_reasoning(message.content, "Portfolio Management Agent") - - return {"messages": state["messages"] + [message]} \ No newline at end of file diff --git a/agents/maigctester/src/maigctester/fi/agents/risk_manager.py b/agents/maigctester/src/maigctester/fi/agents/risk_manager.py deleted file mode 100644 index af3d14f..0000000 --- a/agents/maigctester/src/maigctester/fi/agents/risk_manager.py +++ /dev/null @@ -1,159 +0,0 @@ -import math - -from langchain_core.messages import HumanMessage - -from agents.state import AgentState, show_agent_reasoning -from tools.api import prices_to_df - -import json -import ast - -##### Risk Management Agent ##### -def risk_management_agent(state: AgentState): - """Evaluates portfolio risk and sets position limits based on comprehensive risk analysis.""" - show_reasoning = state["metadata"]["show_reasoning"] - portfolio = state["data"]["portfolio"] - data = state["data"] - - prices_df = prices_to_df(data["prices"]) - - # Fetch messages from other agents - technical_message = next(msg for msg in state["messages"] if msg.name == "technical_analyst_agent") - fundamentals_message = next(msg for msg in state["messages"] if msg.name == "fundamentals_agent") - sentiment_message = next(msg for msg in state["messages"] if msg.name == "sentiment_agent") - - try: - fundamental_signals = json.loads(fundamentals_message.content) - technical_signals = json.loads(technical_message.content) - sentiment_signals = json.loads(sentiment_message.content) - except Exception as e: - fundamental_signals = ast.literal_eval(fundamentals_message.content) - technical_signals = ast.literal_eval(technical_message.content) - sentiment_signals = ast.literal_eval(sentiment_message.content) - - agent_signals = { - "fundamental": fundamental_signals, - "technical": technical_signals, - "sentiment": sentiment_signals - } - - # 1. Calculate Risk Metrics - returns = prices_df['close'].pct_change().dropna() - daily_vol = returns.std() - volatility = daily_vol * (252 ** 0.5) # Annualized volatility approximation - var_95 = returns.quantile(0.05) # Simple historical VaR at 95% confidence - max_drawdown = (prices_df['close'] / prices_df['close'].cummax() - 1).min() - - # 2. Market Risk Assessment - market_risk_score = 0 - - # Volatility scoring - if volatility > 0.30: # High volatility - market_risk_score += 2 - elif volatility > 0.20: # Moderate volatility - market_risk_score += 1 - - # VaR scoring - # Note: var_95 is typically negative. The more negative, the worse. - if var_95 < -0.03: - market_risk_score += 2 - elif var_95 < -0.02: - market_risk_score += 1 - - # Max Drawdown scoring - if max_drawdown < -0.20: # Severe drawdown - market_risk_score += 2 - elif max_drawdown < -0.10: - market_risk_score += 1 - - # 3. Position Size Limits - # Consider total portfolio value, not just cash - current_stock_value = portfolio['stock'] * prices_df['close'].iloc[-1] - total_portfolio_value = portfolio['cash'] + current_stock_value - - base_position_size = total_portfolio_value * 0.25 # Start with 25% max position of total portfolio - - if market_risk_score >= 4: - # Reduce position for high risk - max_position_size = base_position_size * 0.5 - elif market_risk_score >= 2: - # Slightly reduce for moderate risk - max_position_size = base_position_size * 0.75 - else: - # Keep base size for low risk - max_position_size = base_position_size - - # 4. Stress Testing - stress_test_scenarios = { - "market_crash": -0.20, - "moderate_decline": -0.10, - "slight_decline": -0.05 - } - - stress_test_results = {} - current_position_value = current_stock_value - - for scenario, decline in stress_test_scenarios.items(): - potential_loss = current_position_value * decline - portfolio_impact = potential_loss / (portfolio['cash'] + current_position_value) if (portfolio['cash'] + current_position_value) != 0 else math.nan - stress_test_results[scenario] = { - "potential_loss": potential_loss, - "portfolio_impact": portfolio_impact - } - - # 5. Risk-Adjusted Signals Analysis - # Convert all confidences to numeric for proper comparison - def parse_confidence(conf_str): - return float(conf_str.replace('%', '')) / 100.0 - low_confidence = any(parse_confidence(signal['confidence']) < 0.30 for signal in agent_signals.values()) - - # Check the diversity of signals. If all three differ, add to risk score - # (signal divergence can be seen as increased uncertainty) - unique_signals = set(signal['signal'] for signal in agent_signals.values()) - signal_divergence = (2 if len(unique_signals) == 3 else 0) - - risk_score = (market_risk_score * 2) # Market risk contributes up to ~6 points total when doubled - if low_confidence: - risk_score += 4 # Add penalty if any signal confidence < 30% - risk_score += signal_divergence - - # Cap risk score at 10 - risk_score = min(round(risk_score), 10) - - # 6. Generate Trading Action - # If risk is very high, hold. If moderately high, consider reducing. - # Else, follow fundamental signal as a baseline. - if risk_score >= 8: - trading_action = "hold" - elif risk_score >= 6: - trading_action = "reduce" - else: - trading_action = agent_signals['fundamental']['signal'] - - message_content = { - "max_position_size": float(max_position_size), - "risk_score": risk_score, - "trading_action": trading_action, - "risk_metrics": { - "volatility": float(volatility), - "value_at_risk_95": float(var_95), - "max_drawdown": float(max_drawdown), - "market_risk_score": market_risk_score, - "stress_test_results": stress_test_results - }, - "reasoning": f"Risk Score {risk_score}/10: Market Risk={market_risk_score}, " - f"Volatility={volatility:.2%}, VaR={var_95:.2%}, " - f"Max Drawdown={max_drawdown:.2%}" - } - - # Create the risk management message - message = HumanMessage( - content=json.dumps(message_content), - name="risk_management_agent", - ) - - if show_reasoning: - show_agent_reasoning(message_content, "Risk Management Agent") - - return {"messages": state["messages"] + [message]} - diff --git a/agents/maigctester/src/maigctester/fi/agents/sentiment.py b/agents/maigctester/src/maigctester/fi/agents/sentiment.py deleted file mode 100644 index d5bd3b3..0000000 --- a/agents/maigctester/src/maigctester/fi/agents/sentiment.py +++ /dev/null @@ -1,59 +0,0 @@ - -from langchain_core.messages import HumanMessage - -from agents.state import AgentState, show_agent_reasoning - -import json - -##### Sentiment Agent ##### -def sentiment_agent(state: AgentState): - """Analyzes market sentiment and generates trading signals.""" - data = state["data"] - insider_trades = data["insider_trades"] - show_reasoning = state["metadata"]["show_reasoning"] - - # Loop through the insider trades, if transaction_shares is negative, then it is a sell, which is bearish, if positive, then it is a buy, which is bullish - signals = [] - for trade in insider_trades: - transaction_shares = trade["transaction_shares"] - if not transaction_shares: - continue - if transaction_shares < 0: - signals.append("bearish") - else: - signals.append("bullish") - - # Determine overall signal - bullish_signals = signals.count("bullish") - bearish_signals = signals.count("bearish") - if bullish_signals > bearish_signals: - overall_signal = "bullish" - elif bearish_signals > bullish_signals: - overall_signal = "bearish" - else: - overall_signal = "neutral" - - # Calculate confidence level based on the proportion of indicators agreeing - total_signals = len(signals) - confidence = max(bullish_signals, bearish_signals) / total_signals - - message_content = { - "signal": overall_signal, - "confidence": f"{round(confidence * 100)}%", - "reasoning": f"Bullish signals: {bullish_signals}, Bearish signals: {bearish_signals}" - } - - # Print the reasoning if the flag is set - if show_reasoning: - show_agent_reasoning(message_content, "Sentiment Analysis Agent") - - # Create the sentiment message - message = HumanMessage( - content=json.dumps(message_content), - name="sentiment_agent", - ) - - return { - "messages": [message], - "data": data, - } diff --git a/agents/maigctester/src/maigctester/fi/agents/state.py b/agents/maigctester/src/maigctester/fi/agents/state.py deleted file mode 100644 index 2a281b4..0000000 --- a/agents/maigctester/src/maigctester/fi/agents/state.py +++ /dev/null @@ -1,51 +0,0 @@ -from typing import Annotated, Any, Dict, Sequence, TypedDict - -import operator -from langchain_core.messages import BaseMessage - - -import json - - -def merge_dicts(a: Dict[str, Any], b: Dict[str, Any]) -> Dict[str, Any]: - return {**a, **b} - -# Define agent state -class AgentState(TypedDict): - messages: Annotated[Sequence[BaseMessage], operator.add] - data: Annotated[Dict[str, Any], merge_dicts] - metadata: Annotated[Dict[str, Any], merge_dicts] - - - -def show_agent_reasoning(output, agent_name): - print(f"\n{'=' * 10} {agent_name.center(28)} {'=' * 10}") - - def convert_to_serializable(obj): - if hasattr(obj, 'to_dict'): # Handle Pandas Series/DataFrame - return obj.to_dict() - elif hasattr(obj, '__dict__'): # Handle custom objects - return obj.__dict__ - elif isinstance(obj, (int, float, bool, str)): - return obj - elif isinstance(obj, (list, tuple)): - return [convert_to_serializable(item) for item in obj] - elif isinstance(obj, dict): - return {key: convert_to_serializable(value) for key, value in obj.items()} - else: - return str(obj) # Fallback to string representation - - if isinstance(output, (dict, list)): - # Convert the output to JSON-serializable format - serializable_output = convert_to_serializable(output) - print(json.dumps(serializable_output, indent=2)) - else: - try: - # Parse the string as JSON and pretty print it - parsed_output = json.loads(output) - print(json.dumps(parsed_output, indent=2)) - except json.JSONDecodeError: - # Fallback to original string if not valid JSON - print(output) - - print("=" * 48) \ No newline at end of file diff --git a/agents/maigctester/src/maigctester/fi/agents/technicals.py b/agents/maigctester/src/maigctester/fi/agents/technicals.py deleted file mode 100644 index 1e5fcd7..0000000 --- a/agents/maigctester/src/maigctester/fi/agents/technicals.py +++ /dev/null @@ -1,652 +0,0 @@ -import math -from typing import Dict - -from langchain_core.messages import HumanMessage - -from agents.state import AgentState, show_agent_reasoning - -import json -import pandas as pd -import numpy as np - -from tools.api import prices_to_df - - -##### Technical Analyst ##### -def technical_analyst_agent(state: AgentState): - """ - Sophisticated technical analysis system that combines multiple trading strategies: - 1. Trend Following - 2. Mean Reversion - 3. Momentum - 4. Volatility Analysis - 5. Statistical Arbitrage Signals - """ - show_reasoning = state["metadata"]["show_reasoning"] - data = state["data"] - prices = data["prices"] - prices_df = prices_to_df(prices) - - # Calculate indicators - # 1. MACD (Moving Average Convergence Divergence) - macd_line, signal_line = calculate_macd(prices_df) - - # 2. RSI (Relative Strength Index) - rsi = calculate_rsi(prices_df) - - # 3. Bollinger Bands (Bollinger Bands) - upper_band, lower_band = calculate_bollinger_bands(prices_df) - - # 4. OBV (On-Balance Volume) - obv = calculate_obv(prices_df) - - # Generate individual signals - signals = [] - - # MACD signal - if macd_line.iloc[-2] < signal_line.iloc[-2] and macd_line.iloc[-1] > signal_line.iloc[-1]: - signals.append('bullish') - elif macd_line.iloc[-2] > signal_line.iloc[-2] and macd_line.iloc[-1] < signal_line.iloc[-1]: - signals.append('bearish') - else: - signals.append('neutral') - - # RSI signal - if rsi.iloc[-1] < 30: - signals.append('bullish') - elif rsi.iloc[-1] > 70: - signals.append('bearish') - else: - signals.append('neutral') - - # Bollinger Bands signal - current_price = prices_df['close'].iloc[-1] - if current_price < lower_band.iloc[-1]: - signals.append('bullish') - elif current_price > upper_band.iloc[-1]: - signals.append('bearish') - else: - signals.append('neutral') - - # OBV signal - obv_slope = obv.diff().iloc[-5:].mean() - if obv_slope > 0: - signals.append('bullish') - elif obv_slope < 0: - signals.append('bearish') - else: - signals.append('neutral') - - # Add reasoning collection - reasoning = { - "MACD": { - "signal": signals[0], - "details": f"MACD Line crossed {'above' if signals[0] == 'bullish' else 'below' if signals[0] == 'bearish' else 'neither above nor below'} Signal Line" - }, - "RSI": { - "signal": signals[1], - "details": f"RSI is {rsi.iloc[-1]:.2f} ({'oversold' if signals[1] == 'bullish' else 'overbought' if signals[1] == 'bearish' else 'neutral'})" - }, - "Bollinger": { - "signal": signals[2], - "details": f"Price is {'below lower band' if signals[2] == 'bullish' else 'above upper band' if signals[2] == 'bearish' else 'within bands'}" - }, - "OBV": { - "signal": signals[3], - "details": f"OBV slope is {obv_slope:.2f} ({signals[3]})" - } - } - - # Determine overall signal - bullish_signals = signals.count('bullish') - bearish_signals = signals.count('bearish') - - if bullish_signals > bearish_signals: - overall_signal = 'bullish' - elif bearish_signals > bullish_signals: - overall_signal = 'bearish' - else: - overall_signal = 'neutral' - - # Calculate confidence level based on the proportion of indicators agreeing - total_signals = len(signals) - confidence = max(bullish_signals, bearish_signals) / total_signals - - # Generate the message content - message_content = { - "signal": overall_signal, - "confidence": f"{round(confidence * 100)}%", - "reasoning": { - "MACD": reasoning["MACD"], - "RSI": reasoning["RSI"], - "Bollinger": reasoning["Bollinger"], - "OBV": reasoning["OBV"] - } - } - - # 1. Trend Following Strategy - trend_signals = calculate_trend_signals(prices_df) - - # 2. Mean Reversion Strategy - mean_reversion_signals = calculate_mean_reversion_signals(prices_df) - - # 3. Momentum Strategy - momentum_signals = calculate_momentum_signals(prices_df) - - # 4. Volatility Strategy - volatility_signals = calculate_volatility_signals(prices_df) - - # 5. Statistical Arbitrage Signals - stat_arb_signals = calculate_stat_arb_signals(prices_df) - - # Combine all signals using a weighted ensemble approach - strategy_weights = { - 'trend': 0.25, - 'mean_reversion': 0.20, - 'momentum': 0.25, - 'volatility': 0.15, - 'stat_arb': 0.15 - } - - combined_signal = weighted_signal_combination({ - 'trend': trend_signals, - 'mean_reversion': mean_reversion_signals, - 'momentum': momentum_signals, - 'volatility': volatility_signals, - 'stat_arb': stat_arb_signals - }, strategy_weights) - - # Generate detailed analysis report - analysis_report = { - "signal": combined_signal['signal'], - "confidence": f"{round(combined_signal['confidence'] * 100)}%", - "strategy_signals": { - "trend_following": { - "signal": trend_signals['signal'], - "confidence": f"{round(trend_signals['confidence'] * 100)}%", - "metrics": normalize_pandas(trend_signals['metrics']) - }, - "mean_reversion": { - "signal": mean_reversion_signals['signal'], - "confidence": f"{round(mean_reversion_signals['confidence'] * 100)}%", - "metrics": normalize_pandas(mean_reversion_signals['metrics']) - }, - "momentum": { - "signal": momentum_signals['signal'], - "confidence": f"{round(momentum_signals['confidence'] * 100)}%", - "metrics": normalize_pandas(momentum_signals['metrics']) - }, - "volatility": { - "signal": volatility_signals['signal'], - "confidence": f"{round(volatility_signals['confidence'] * 100)}%", - "metrics": normalize_pandas(volatility_signals['metrics']) - }, - "statistical_arbitrage": { - "signal": stat_arb_signals['signal'], - "confidence": f"{round(stat_arb_signals['confidence'] * 100)}%", - "metrics": normalize_pandas(stat_arb_signals['metrics']) - } - } - } - - # Create the technical analyst message - message = HumanMessage( - content=json.dumps(analysis_report), - name="technical_analyst_agent", - ) - - if show_reasoning: - show_agent_reasoning(analysis_report, "Technical Analyst") - - return { - "messages": [message], - "data": data, - } - -def calculate_trend_signals(prices_df): - """ - Advanced trend following strategy using multiple timeframes and indicators - """ - # Calculate EMAs for multiple timeframes - ema_8 = calculate_ema(prices_df, 8) - ema_21 = calculate_ema(prices_df, 21) - ema_55 = calculate_ema(prices_df, 55) - - # Calculate ADX for trend strength - adx = calculate_adx(prices_df, 14) - - # Calculate Ichimoku Cloud - ichimoku = calculate_ichimoku(prices_df) - - # Determine trend direction and strength - short_trend = ema_8 > ema_21 - medium_trend = ema_21 > ema_55 - - # Combine signals with confidence weighting - trend_strength = adx['adx'].iloc[-1] / 100.0 - - if short_trend.iloc[-1] and medium_trend.iloc[-1]: - signal = 'bullish' - confidence = trend_strength - elif not short_trend.iloc[-1] and not medium_trend.iloc[-1]: - signal = 'bearish' - confidence = trend_strength - else: - signal = 'neutral' - confidence = 0.5 - - return { - 'signal': signal, - 'confidence': confidence, - 'metrics': { - 'adx': float(adx['adx'].iloc[-1]), - 'trend_strength': float(trend_strength), - # 'ichimoku': ichimoku - } - } - -def calculate_mean_reversion_signals(prices_df): - """ - Mean reversion strategy using statistical measures and Bollinger Bands - """ - # Calculate z-score of price relative to moving average - ma_50 = prices_df['close'].rolling(window=50).mean() - std_50 = prices_df['close'].rolling(window=50).std() - z_score = (prices_df['close'] - ma_50) / std_50 - - # Calculate Bollinger Bands - bb_upper, bb_lower = calculate_bollinger_bands(prices_df) - - # Calculate RSI with multiple timeframes - rsi_14 = calculate_rsi(prices_df, 14) - rsi_28 = calculate_rsi(prices_df, 28) - - # Mean reversion signals - extreme_z_score = abs(z_score.iloc[-1]) > 2 - price_vs_bb = (prices_df['close'].iloc[-1] - bb_lower.iloc[-1]) / (bb_upper.iloc[-1] - bb_lower.iloc[-1]) - - # Combine signals - if z_score.iloc[-1] < -2 and price_vs_bb < 0.2: - signal = 'bullish' - confidence = min(abs(z_score.iloc[-1]) / 4, 1.0) - elif z_score.iloc[-1] > 2 and price_vs_bb > 0.8: - signal = 'bearish' - confidence = min(abs(z_score.iloc[-1]) / 4, 1.0) - else: - signal = 'neutral' - confidence = 0.5 - - return { - 'signal': signal, - 'confidence': confidence, - 'metrics': { - 'z_score': float(z_score.iloc[-1]), - 'price_vs_bb': float(price_vs_bb), - 'rsi_14': float(rsi_14.iloc[-1]), - 'rsi_28': float(rsi_28.iloc[-1]) - } - } - -def calculate_momentum_signals(prices_df): - """ - Multi-factor momentum strategy - """ - # Price momentum - returns = prices_df['close'].pct_change() - mom_1m = returns.rolling(21).sum() - mom_3m = returns.rolling(63).sum() - mom_6m = returns.rolling(126).sum() - - # Volume momentum - volume_ma = prices_df['volume'].rolling(21).mean() - volume_momentum = prices_df['volume'] / volume_ma - - # Relative strength - # (would compare to market/sector in real implementation) - - # Calculate momentum score - momentum_score = ( - 0.4 * mom_1m + - 0.3 * mom_3m + - 0.3 * mom_6m - ).iloc[-1] - - # Volume confirmation - volume_confirmation = volume_momentum.iloc[-1] > 1.0 - - if momentum_score > 0.05 and volume_confirmation: - signal = 'bullish' - confidence = min(abs(momentum_score) * 5, 1.0) - elif momentum_score < -0.05 and volume_confirmation: - signal = 'bearish' - confidence = min(abs(momentum_score) * 5, 1.0) - else: - signal = 'neutral' - confidence = 0.5 - - return { - 'signal': signal, - 'confidence': confidence, - 'metrics': { - 'momentum_1m': float(mom_1m.iloc[-1]), - 'momentum_3m': float(mom_3m.iloc[-1]), - 'momentum_6m': float(mom_6m.iloc[-1]), - 'volume_momentum': float(volume_momentum.iloc[-1]) - } - } - -def calculate_volatility_signals(prices_df): - """ - Volatility-based trading strategy - """ - # Calculate various volatility metrics - returns = prices_df['close'].pct_change() - - # Historical volatility - hist_vol = returns.rolling(21).std() * math.sqrt(252) - - # Volatility regime detection - vol_ma = hist_vol.rolling(63).mean() - vol_regime = hist_vol / vol_ma - - # Volatility mean reversion - vol_z_score = (hist_vol - vol_ma) / hist_vol.rolling(63).std() - - # ATR ratio - atr = calculate_atr(prices_df) - atr_ratio = atr / prices_df['close'] - - # Generate signal based on volatility regime - current_vol_regime = vol_regime.iloc[-1] - vol_z = vol_z_score.iloc[-1] - - if current_vol_regime < 0.8 and vol_z < -1: - signal = 'bullish' # Low vol regime, potential for expansion - confidence = min(abs(vol_z) / 3, 1.0) - elif current_vol_regime > 1.2 and vol_z > 1: - signal = 'bearish' # High vol regime, potential for contraction - confidence = min(abs(vol_z) / 3, 1.0) - else: - signal = 'neutral' - confidence = 0.5 - - return { - 'signal': signal, - 'confidence': confidence, - 'metrics': { - 'historical_volatility': float(hist_vol.iloc[-1]), - 'volatility_regime': float(current_vol_regime), - 'volatility_z_score': float(vol_z), - 'atr_ratio': float(atr_ratio.iloc[-1]) - } - } - -def calculate_stat_arb_signals(prices_df): - """ - Statistical arbitrage signals based on price action analysis - """ - # Calculate price distribution statistics - returns = prices_df['close'].pct_change() - - # Skewness and kurtosis - skew = returns.rolling(63).skew() - kurt = returns.rolling(63).kurt() - - # Test for mean reversion using Hurst exponent - hurst = calculate_hurst_exponent(prices_df['close']) - - # Correlation analysis - # (would include correlation with related securities in real implementation) - - # Generate signal based on statistical properties - if hurst < 0.4 and skew.iloc[-1] > 1: - signal = 'bullish' - confidence = (0.5 - hurst) * 2 - elif hurst < 0.4 and skew.iloc[-1] < -1: - signal = 'bearish' - confidence = (0.5 - hurst) * 2 - else: - signal = 'neutral' - confidence = 0.5 - - return { - 'signal': signal, - 'confidence': confidence, - 'metrics': { - 'hurst_exponent': float(hurst), - 'skewness': float(skew.iloc[-1]), - 'kurtosis': float(kurt.iloc[-1]) - } - } - -def weighted_signal_combination(signals, weights): - """ - Combines multiple trading signals using a weighted approach - """ - # Convert signals to numeric values - signal_values = { - 'bullish': 1, - 'neutral': 0, - 'bearish': -1 - } - - weighted_sum = 0 - total_confidence = 0 - - for strategy, signal in signals.items(): - numeric_signal = signal_values[signal['signal']] - weight = weights[strategy] - confidence = signal['confidence'] - - weighted_sum += numeric_signal * weight * confidence - total_confidence += weight * confidence - - # Normalize the weighted sum - if total_confidence > 0: - final_score = weighted_sum / total_confidence - else: - final_score = 0 - - # Convert back to signal - if final_score > 0.2: - signal = 'bullish' - elif final_score < -0.2: - signal = 'bearish' - else: - signal = 'neutral' - - return { - 'signal': signal, - 'confidence': abs(final_score) - } - -def normalize_pandas(obj): - """Convert pandas Series/DataFrames to primitive Python types""" - if isinstance(obj, pd.Series): - return obj.tolist() - elif isinstance(obj, pd.DataFrame): - return obj.to_dict('records') - elif isinstance(obj, dict): - return {k: normalize_pandas(v) for k, v in obj.items()} - elif isinstance(obj, (list, tuple)): - return [normalize_pandas(item) for item in obj] - return obj - -def calculate_macd(prices_df: pd.DataFrame) -> tuple[pd.Series, pd.Series]: - ema_12 = prices_df['close'].ewm(span=12, adjust=False).mean() - ema_26 = prices_df['close'].ewm(span=26, adjust=False).mean() - macd_line = ema_12 - ema_26 - signal_line = macd_line.ewm(span=9, adjust=False).mean() - return macd_line, signal_line - -def calculate_rsi(prices_df: pd.DataFrame, period: int = 14) -> pd.Series: - delta = prices_df['close'].diff() - gain = (delta.where(delta > 0, 0)).fillna(0) - loss = (-delta.where(delta < 0, 0)).fillna(0) - avg_gain = gain.rolling(window=period).mean() - avg_loss = loss.rolling(window=period).mean() - rs = avg_gain / avg_loss - rsi = 100 - (100 / (1 + rs)) - return rsi - -def calculate_bollinger_bands( - prices_df: pd.DataFrame, - window: int = 20 -) -> tuple[pd.Series, pd.Series]: - sma = prices_df['close'].rolling(window).mean() - std_dev = prices_df['close'].rolling(window).std() - upper_band = sma + (std_dev * 2) - lower_band = sma - (std_dev * 2) - return upper_band, lower_band - -def calculate_ema(df: pd.DataFrame, window: int) -> pd.Series: - """ - Calculate Exponential Moving Average - - Args: - df: DataFrame with price data - window: EMA period - - Returns: - pd.Series: EMA values - """ - return df['close'].ewm(span=window, adjust=False).mean() - -def calculate_adx(df: pd.DataFrame, period: int = 14) -> pd.DataFrame: - """ - Calculate Average Directional Index (ADX) - - Args: - df: DataFrame with OHLC data - period: Period for calculations - - Returns: - DataFrame with ADX values - """ - # Calculate True Range - df['high_low'] = df['high'] - df['low'] - df['high_close'] = abs(df['high'] - df['close'].shift()) - df['low_close'] = abs(df['low'] - df['close'].shift()) - df['tr'] = df[['high_low', 'high_close', 'low_close']].max(axis=1) - - # Calculate Directional Movement - df['up_move'] = df['high'] - df['high'].shift() - df['down_move'] = df['low'].shift() - df['low'] - - df['plus_dm'] = np.where( - (df['up_move'] > df['down_move']) & (df['up_move'] > 0), - df['up_move'], - 0 - ) - df['minus_dm'] = np.where( - (df['down_move'] > df['up_move']) & (df['down_move'] > 0), - df['down_move'], - 0 - ) - - # Calculate ADX - df['+di'] = 100 * (df['plus_dm'].ewm(span=period).mean() / - df['tr'].ewm(span=period).mean()) - df['-di'] = 100 * (df['minus_dm'].ewm(span=period).mean() / - df['tr'].ewm(span=period).mean()) - df['dx'] = 100 * abs(df['+di'] - df['-di']) / (df['+di'] + df['-di']) - df['adx'] = df['dx'].ewm(span=period).mean() - - return df[['adx', '+di', '-di']] - -def calculate_ichimoku(df: pd.DataFrame) -> Dict[str, pd.Series]: - """ - Calculate Ichimoku Cloud indicators - - Args: - df: DataFrame with OHLC data - - Returns: - Dictionary containing Ichimoku components - """ - # Tenkan-sen (Conversion Line): (9-period high + 9-period low)/2 - period9_high = df['high'].rolling(window=9).max() - period9_low = df['low'].rolling(window=9).min() - tenkan_sen = (period9_high + period9_low) / 2 - - # Kijun-sen (Base Line): (26-period high + 26-period low)/2 - period26_high = df['high'].rolling(window=26).max() - period26_low = df['low'].rolling(window=26).min() - kijun_sen = (period26_high + period26_low) / 2 - - # Senkou Span A (Leading Span A): (Conversion Line + Base Line)/2 - senkou_span_a = ((tenkan_sen + kijun_sen) / 2).shift(26) - - # Senkou Span B (Leading Span B): (52-period high + 52-period low)/2 - period52_high = df['high'].rolling(window=52).max() - period52_low = df['low'].rolling(window=52).min() - senkou_span_b = ((period52_high + period52_low) / 2).shift(26) - - # Chikou Span (Lagging Span): Close shifted back 26 periods - chikou_span = df['close'].shift(-26) - - return { - 'tenkan_sen': tenkan_sen, - 'kijun_sen': kijun_sen, - 'senkou_span_a': senkou_span_a, - 'senkou_span_b': senkou_span_b, - 'chikou_span': chikou_span - } - -def calculate_atr(df: pd.DataFrame, period: int = 14) -> pd.Series: - """ - Calculate Average True Range - - Args: - df: DataFrame with OHLC data - period: Period for ATR calculation - - Returns: - pd.Series: ATR values - """ - high_low = df['high'] - df['low'] - high_close = abs(df['high'] - df['close'].shift()) - low_close = abs(df['low'] - df['close'].shift()) - - ranges = pd.concat([high_low, high_close, low_close], axis=1) - true_range = ranges.max(axis=1) - - return true_range.rolling(period).mean() - -def calculate_hurst_exponent(price_series: pd.Series, max_lag: int = 20) -> float: - """ - Calculate Hurst Exponent to determine long-term memory of time series - H < 0.5: Mean reverting series - H = 0.5: Random walk - H > 0.5: Trending series - - Args: - price_series: Array-like price data - max_lag: Maximum lag for R/S calculation - - Returns: - float: Hurst exponent - """ - lags = range(2, max_lag) - # Add small epsilon to avoid log(0) - tau = [max(1e-8, np.sqrt(np.std(np.subtract(price_series[lag:], price_series[:-lag])))) for lag in lags] - - # Return the Hurst exponent from linear fit - try: - reg = np.polyfit(np.log(lags), np.log(tau), 1) - return reg[0] # Hurst exponent is the slope - except (ValueError, RuntimeWarning): - # Return 0.5 (random walk) if calculation fails - return 0.5 - -def calculate_obv(prices_df: pd.DataFrame) -> pd.Series: - obv = [0] - for i in range(1, len(prices_df)): - if prices_df['close'].iloc[i] > prices_df['close'].iloc[i - 1]: - obv.append(obv[-1] + prices_df['volume'].iloc[i]) - elif prices_df['close'].iloc[i] < prices_df['close'].iloc[i - 1]: - obv.append(obv[-1] - prices_df['volume'].iloc[i]) - else: - obv.append(obv[-1]) - prices_df['OBV'] = obv - return prices_df['OBV'] diff --git a/agents/maigctester/src/maigctester/fi/backtester.py b/agents/maigctester/src/maigctester/fi/backtester.py deleted file mode 100644 index bcbb7b2..0000000 --- a/agents/maigctester/src/maigctester/fi/backtester.py +++ /dev/null @@ -1,153 +0,0 @@ -from datetime import datetime, timedelta - -import matplotlib.pyplot as plt -import pandas as pd - -from main import run_hedge_fund -from tools.api import get_price_data - -class Backtester: - def __init__(self, agent, ticker, start_date, end_date, initial_capital): - self.agent = agent - self.ticker = ticker - self.start_date = start_date - self.end_date = end_date - self.initial_capital = initial_capital - self.portfolio = {"cash": initial_capital, "stock": 0} - self.portfolio_values = [] - - def parse_action(self, agent_output): - try: - # Expect JSON output from agent - import json - decision = json.loads(agent_output) - return decision["action"], decision["quantity"] - except: - print(f"Error parsing action: {agent_output}") - return "hold", 0 - - def execute_trade(self, action, quantity, current_price): - """Validate and execute trades based on portfolio constraints""" - if action == "buy" and quantity > 0: - cost = quantity * current_price - if cost <= self.portfolio["cash"]: - self.portfolio["stock"] += quantity - self.portfolio["cash"] -= cost - return quantity - else: - # Calculate maximum affordable quantity - max_quantity = self.portfolio["cash"] // current_price - if max_quantity > 0: - self.portfolio["stock"] += max_quantity - self.portfolio["cash"] -= max_quantity * current_price - return max_quantity - return 0 - elif action == "sell" and quantity > 0: - quantity = min(quantity, self.portfolio["stock"]) - if quantity > 0: - self.portfolio["cash"] += quantity * current_price - self.portfolio["stock"] -= quantity - return quantity - return 0 - return 0 - - def run_backtest(self): - dates = pd.date_range(self.start_date, self.end_date, freq="B") - - print("\nStarting backtest...") - print(f"{'Date':<12} {'Ticker':<6} {'Action':<6} {'Quantity':>8} {'Price':>8} {'Cash':>12} {'Stock':>8} {'Total Value':>12}") - print("-" * 100) - - for current_date in dates: - lookback_start = (current_date - timedelta(days=30)).strftime("%Y-%m-%d") - current_date_str = current_date.strftime("%Y-%m-%d") - - agent_output = self.agent( - ticker=self.ticker, - start_date=lookback_start, - end_date=current_date_str, - portfolio=self.portfolio - ) - - action, quantity = self.parse_action(agent_output) - df = get_price_data(self.ticker, lookback_start, current_date_str) - current_price = df.iloc[-1]['close'] - - # Execute the trade with validation - executed_quantity = self.execute_trade(action, quantity, current_price) - - # Update total portfolio value - total_value = self.portfolio["cash"] + self.portfolio["stock"] * current_price - self.portfolio["portfolio_value"] = total_value - - # Log the current state with executed quantity - print( - f"{current_date.strftime('%Y-%m-%d'):<12} {self.ticker:<6} {action:<6} {executed_quantity:>8} {current_price:>8.2f} " - f"{self.portfolio['cash']:>12.2f} {self.portfolio['stock']:>8} {total_value:>12.2f}" - ) - - # Record the portfolio value - self.portfolio_values.append( - {"Date": current_date, "Portfolio Value": total_value} - ) - - def analyze_performance(self): - # Convert portfolio values to DataFrame - performance_df = pd.DataFrame(self.portfolio_values).set_index("Date") - - # Calculate total return - total_return = ( - self.portfolio["portfolio_value"] - self.initial_capital - ) / self.initial_capital - print(f"Total Return: {total_return * 100:.2f}%") - - # Plot the portfolio value over time - performance_df["Portfolio Value"].plot( - title="Portfolio Value Over Time", figsize=(12, 6) - ) - plt.ylabel("Portfolio Value ($)") - plt.xlabel("Date") - plt.show() - - # Compute daily returns - performance_df["Daily Return"] = performance_df["Portfolio Value"].pct_change() - - # Calculate Sharpe Ratio (assuming 252 trading days in a year) - mean_daily_return = performance_df["Daily Return"].mean() - std_daily_return = performance_df["Daily Return"].std() - sharpe_ratio = (mean_daily_return / std_daily_return) * (252 ** 0.5) - print(f"Sharpe Ratio: {sharpe_ratio:.2f}") - - # Calculate Maximum Drawdown - rolling_max = performance_df["Portfolio Value"].cummax() - drawdown = performance_df["Portfolio Value"] / rolling_max - 1 - max_drawdown = drawdown.min() - print(f"Maximum Drawdown: {max_drawdown * 100:.2f}%") - - return performance_df - -### 4. Run the Backtest ##### -if __name__ == "__main__": - import argparse - - # Set up argument parser - parser = argparse.ArgumentParser(description='Run backtesting simulation') - parser.add_argument('--ticker', type=str, help='Stock ticker symbol (e.g., AAPL)') - parser.add_argument('--end_date', type=str, default=datetime.now().strftime('%Y-%m-%d'), help='End date in YYYY-MM-DD format') - parser.add_argument('--start_date', type=str, default=(datetime.now() - timedelta(days=90)).strftime('%Y-%m-%d'), help='Start date in YYYY-MM-DD format') - parser.add_argument('--initial_capital', type=float, default=100000, help='Initial capital amount (default: 100000)') - - args = parser.parse_args() - - # Create an instance of Backtester - backtester = Backtester( - agent=run_hedge_fund, - ticker=args.ticker, - start_date=args.start_date, - end_date=args.end_date, - initial_capital=args.initial_capital, - ) - - # Run the backtesting process - backtester.run_backtest() - performance_df = backtester.analyze_performance() diff --git a/agents/maigctester/src/maigctester/fi/main.py b/agents/maigctester/src/maigctester/fi/main.py deleted file mode 100644 index 5ea4af5..0000000 --- a/agents/maigctester/src/maigctester/fi/main.py +++ /dev/null @@ -1,100 +0,0 @@ - -from langchain_core.messages import HumanMessage -from langgraph.graph import END, StateGraph - -from agents.fundamentals import fundamentals_agent -from agents.market_data import market_data_agent -from agents.portfolio_manager import portfolio_management_agent -from agents.technicals import technical_analyst_agent -from agents.risk_manager import risk_management_agent -from agents.sentiment import sentiment_agent -from agents.state import AgentState - -import argparse -from datetime import datetime - - -##### Run the Hedge Fund ##### -def run_hedge_fund(ticker: str, start_date: str, end_date: str, portfolio: dict, show_reasoning: bool = False): - final_state = app.invoke( - { - "messages": [ - HumanMessage( - content="Make a trading decision based on the provided data.", - ) - ], - "data": { - "ticker": ticker, - "portfolio": portfolio, - "start_date": start_date, - "end_date": end_date, - }, - "metadata": { - "show_reasoning": show_reasoning, - } - }, - ) - return final_state["messages"][-1].content - -# Define the new workflow -workflow = StateGraph(AgentState) - -# Add nodes -workflow.add_node("market_data_agent", market_data_agent) -workflow.add_node("technical_analyst_agent", technical_analyst_agent) -workflow.add_node("fundamentals_agent", fundamentals_agent) -workflow.add_node("sentiment_agent", sentiment_agent) -workflow.add_node("risk_management_agent", risk_management_agent) -workflow.add_node("portfolio_management_agent", portfolio_management_agent) - -# Define the workflow -workflow.set_entry_point("market_data_agent") -workflow.add_edge("market_data_agent", "technical_analyst_agent") -workflow.add_edge("market_data_agent", "fundamentals_agent") -workflow.add_edge("market_data_agent", "sentiment_agent") -workflow.add_edge("technical_analyst_agent", "risk_management_agent") -workflow.add_edge("fundamentals_agent", "risk_management_agent") -workflow.add_edge("sentiment_agent", "risk_management_agent") -workflow.add_edge("risk_management_agent", "portfolio_management_agent") -workflow.add_edge("portfolio_management_agent", END) - -app = workflow.compile() - -# Add this at the bottom of the file -if __name__ == "__main__": - parser = argparse.ArgumentParser(description='Run the hedge fund trading system') - parser.add_argument('--ticker', type=str, required=True, help='Stock ticker symbol') - parser.add_argument('--start-date', type=str, help='Start date (YYYY-MM-DD). Defaults to 3 months before end date') - parser.add_argument('--end-date', type=str, help='End date (YYYY-MM-DD). Defaults to today') - parser.add_argument('--show-reasoning', action='store_true', help='Show reasoning from each agent') - - args = parser.parse_args() - - # Validate dates if provided - if args.start_date: - try: - datetime.strptime(args.start_date, '%Y-%m-%d') - except ValueError: - raise ValueError("Start date must be in YYYY-MM-DD format") - - if args.end_date: - try: - datetime.strptime(args.end_date, '%Y-%m-%d') - except ValueError: - raise ValueError("End date must be in YYYY-MM-DD format") - - # Sample portfolio - you might want to make this configurable too - portfolio = { - "cash": 100000.0, # $100,000 initial cash - "stock": 0 # No initial stock position - } - - result = run_hedge_fund( - ticker=args.ticker, - start_date=args.start_date, - end_date=args.end_date, - portfolio=portfolio, - show_reasoning=args.show_reasoning - ) - print("\nFinal Result:") - print(result) \ No newline at end of file diff --git a/agents/maigctester/src/maigctester/fi/tools/api.py b/agents/maigctester/src/maigctester/fi/tools/api.py deleted file mode 100644 index 5d24135..0000000 --- a/agents/maigctester/src/maigctester/fi/tools/api.py +++ /dev/null @@ -1,152 +0,0 @@ -import os -from typing import Dict, Any, List -import pandas as pd -import requests - -import requests - -def get_financial_metrics( - ticker: str, - report_period: str, - period: str = 'ttm', - limit: int = 1 -) -> List[Dict[str, Any]]: - """Fetch financial metrics from the API.""" - headers = {"X-API-KEY": os.environ.get("FINANCIAL_DATASETS_API_KEY")} - url = ( - f"https://api.financialdatasets.ai/financial-metrics/" - f"?ticker={ticker}" - f"&report_period_lte={report_period}" - f"&limit={limit}" - f"&period={period}" - ) - response = requests.get(url, headers=headers) - if response.status_code != 200: - raise Exception( - f"Error fetching data: {response.status_code} - {response.text}" - ) - data = response.json() - financial_metrics = data.get("financial_metrics") - if not financial_metrics: - raise ValueError("No financial metrics returned") - return financial_metrics - -def search_line_items( - ticker: str, - line_items: List[str], - period: str = 'ttm', - limit: int = 1 -) -> List[Dict[str, Any]]: - """Fetch cash flow statements from the API.""" - headers = {"X-API-KEY": os.environ.get("FINANCIAL_DATASETS_API_KEY")} - url = "https://api.financialdatasets.ai/financials/search/line-items" - - body = { - "tickers": [ticker], - "line_items": line_items, - "period": period, - "limit": limit - } - response = requests.post(url, headers=headers, json=body) - if response.status_code != 200: - raise Exception( - f"Error fetching data: {response.status_code} - {response.text}" - ) - data = response.json() - search_results = data.get("search_results") - if not search_results: - raise ValueError("No search results returned") - return search_results - -def get_insider_trades( - ticker: str, - end_date: str, - limit: int = 5, -) -> List[Dict[str, Any]]: - """ - Fetch insider trades for a given ticker and date range. - """ - headers = {"X-API-KEY": os.environ.get("FINANCIAL_DATASETS_API_KEY")} - url = ( - f"https://api.financialdatasets.ai/insider-trades/" - f"?ticker={ticker}" - f"&filing_date_lte={end_date}" - f"&limit={limit}" - ) - response = requests.get(url, headers=headers) - if response.status_code != 200: - raise Exception( - f"Error fetching data: {response.status_code} - {response.text}" - ) - data = response.json() - insider_trades = data.get("insider_trades") - if not insider_trades: - raise ValueError("No insider trades returned") - return insider_trades - -def get_market_cap( - ticker: str, -) -> List[Dict[str, Any]]: - """Fetch market cap from the API.""" - headers = {"X-API-KEY": os.environ.get("FINANCIAL_DATASETS_API_KEY")} - url = ( - f'https://api.financialdatasets.ai/company/facts' - f'?ticker={ticker}' - ) - - response = requests.get(url, headers=headers) - if response.status_code != 200: - raise Exception( - f"Error fetching data: {response.status_code} - {response.text}" - ) - data = response.json() - company_facts = data.get('company_facts') - if not company_facts: - raise ValueError("No company facts returned") - return company_facts.get('market_cap') - -def get_prices( - ticker: str, - start_date: str, - end_date: str -) -> List[Dict[str, Any]]: - """Fetch price data from the API.""" - headers = {"X-API-KEY": os.environ.get("FINANCIAL_DATASETS_API_KEY")} - url = ( - f"https://api.financialdatasets.ai/prices/" - f"?ticker={ticker}" - f"&interval=day" - f"&interval_multiplier=1" - f"&start_date={start_date}" - f"&end_date={end_date}" - ) - response = requests.get(url, headers=headers) - if response.status_code != 200: - raise Exception( - f"Error fetching data: {response.status_code} - {response.text}" - ) - data = response.json() - prices = data.get("prices") - if not prices: - raise ValueError("No price data returned") - return prices - -def prices_to_df(prices: List[Dict[str, Any]]) -> pd.DataFrame: - """Convert prices to a DataFrame.""" - df = pd.DataFrame(prices) - df["Date"] = pd.to_datetime(df["time"]) - df.set_index("Date", inplace=True) - numeric_cols = ["open", "close", "high", "low", "volume"] - for col in numeric_cols: - df[col] = pd.to_numeric(df[col], errors="coerce") - df.sort_index(inplace=True) - return df - -# Update the get_price_data function to use the new functions -def get_price_data( - ticker: str, - start_date: str, - end_date: str -) -> pd.DataFrame: - prices = get_prices(ticker, start_date, end_date) - return prices_to_df(prices) diff --git a/agents/maigctester/src/maigctester/main.py b/agents/maigctester/src/maigctester/main.py deleted file mode 100644 index fdca9e8..0000000 --- a/agents/maigctester/src/maigctester/main.py +++ /dev/null @@ -1,58 +0,0 @@ -#!/usr/bin/env python -import sys -import warnings - -from maigctester.crew import Maigctester - -warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd") - -# This main file is intended to be a way for you to run your -# crew locally, so refrain from adding unnecessary logic into this file. -# Replace with inputs you want to test with, it will automatically -# interpolate any tasks and agents information - -def run(): - """ - Run the crew. - """ - inputs = { - 'topic': 'AI LLMs' - } - Maigctester().crew().kickoff(inputs=inputs) - - -def train(): - """ - Train the crew for a given number of iterations. - """ - inputs = { - "topic": "AI LLMs" - } - try: - Maigctester().crew().train(n_iterations=int(sys.argv[1]), filename=sys.argv[2], inputs=inputs) - - except Exception as e: - raise Exception(f"An error occurred while training the crew: {e}") - -def replay(): - """ - Replay the crew execution from a specific task. - """ - try: - Maigctester().crew().replay(task_id=sys.argv[1]) - - except Exception as e: - raise Exception(f"An error occurred while replaying the crew: {e}") - -def test(): - """ - Test the crew execution and returns the results. - """ - inputs = { - "topic": "AI LLMs" - } - try: - Maigctester().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs) - - except Exception as e: - raise Exception(f"An error occurred while replaying the crew: {e}") diff --git a/agents/maigctester/src/maigctester/tools/__init__.py b/agents/maigctester/src/maigctester/tools/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/agents/maigctester/src/maigctester/tools/custom_tool.py b/agents/maigctester/src/maigctester/tools/custom_tool.py deleted file mode 100644 index 50bffa5..0000000 --- a/agents/maigctester/src/maigctester/tools/custom_tool.py +++ /dev/null @@ -1,19 +0,0 @@ -from crewai.tools import BaseTool -from typing import Type -from pydantic import BaseModel, Field - - -class MyCustomToolInput(BaseModel): - """Input schema for MyCustomTool.""" - argument: str = Field(..., description="Description of the argument.") - -class MyCustomTool(BaseTool): - name: str = "Name of my tool" - description: str = ( - "Clear description for what this tool is useful for, you agent will need this information to use it." - ) - args_schema: Type[BaseModel] = MyCustomToolInput - - def _run(self, argument: str) -> str: - # Implementation goes here - return "this is an example of a tool output, ignore it and move along." diff --git a/agents/promptize/.gitignore b/agents/promptize/.gitignore deleted file mode 100644 index 83a867c..0000000 --- a/agents/promptize/.gitignore +++ /dev/null @@ -1,12 +0,0 @@ -# Python cache files -__pycache__/ -*.pyc - -# Virtual environment -env/ -.venv/ - -# Package artifacts -dist/ -build/ -*.egg-info/ \ No newline at end of file diff --git a/agents/promptize/intro.md b/agents/promptize/intro.md deleted file mode 100644 index e69de29..0000000 diff --git a/agents/promptize/src/promptize/app.py b/agents/promptize/src/promptize/app.py deleted file mode 100644 index 838970e..0000000 --- a/agents/promptize/src/promptize/app.py +++ /dev/null @@ -1,8 +0,0 @@ -import gradio as gr -import ai_gradio - -gr.load( - name='hyperbolic:deepseek-ai/DeepSeek-V3', - src=ai_gradio.registry, - coder=True -).launch() diff --git a/agents/promptize/src/promptize/conf/__init__.py b/agents/promptize/src/promptize/conf/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/agents/promptize/src/promptize/providers/__init__.py b/agents/promptize/src/promptize/providers/__init__.py deleted file mode 100644 index 16dd87d..0000000 --- a/agents/promptize/src/promptize/providers/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -from importlib.metadata import version - -try: - __version__ = version("ai-gradio") -except Exception: - __version__ = "unknown" - -from .providers import registry - -__all__ = ["registry"] diff --git a/agents/promptize/src/promptize/providers/anthropic_gradio.py b/agents/promptize/src/promptize/providers/anthropic_gradio.py deleted file mode 100644 index 70fe127..0000000 --- a/agents/promptize/src/promptize/providers/anthropic_gradio.py +++ /dev/null @@ -1,76 +0,0 @@ -import os -import anthropic -import gradio as gr -from typing import Callable - -__version__ = "0.0.1" - - -def get_fn(model_name: str, preprocess: Callable, postprocess: Callable, api_key: str): - def fn(message, history): - inputs = preprocess(message, history) - client = anthropic.Anthropic(api_key=api_key) - with client.messages.stream( - model=model_name, - max_tokens=1000, - messages=inputs["messages"] - ) as stream: - response_text = "" - for chunk in stream: - if chunk.type == "content_block_delta": - delta = chunk.delta.text - response_text += delta - yield postprocess(response_text) - - return fn - - -def get_interface_args(pipeline): - if pipeline == "chat": - inputs = None - outputs = None - - def preprocess(message, history): - messages = [] - for user_msg, assistant_msg in history: - messages.append({"role": "user", "content": [{"type": "text", "text": user_msg}]}) - messages.append({"role": "assistant", "content": [{"type": "text", "text": assistant_msg}]}) - messages.append({"role": "user", "content": [{"type": "text", "text": message}]}) - return {"messages": messages} - - postprocess = lambda x: x # No post-processing needed - else: - # Add other pipeline types when they will be needed - raise ValueError(f"Unsupported pipeline type: {pipeline}") - return inputs, outputs, preprocess, postprocess - - -def get_pipeline(model_name): - # Determine the pipeline type based on the model name - # For simplicity, assuming all models are chat models at the moment - return "chat" - - -def registry(name: str, token: str | None = None, **kwargs): - """ - Create a Gradio Interface for a model on Anthropic. - - Parameters: - - name (str): The name of the Anthropic model. - - token (str, optional): The API key for Anthropic. - """ - api_key = token or os.environ.get("ANTHROPIC_API_KEY") - if not api_key: - raise ValueError("ANTHROPIC_API_KEY environment variable is not set.") - - pipeline = get_pipeline(name) - inputs, outputs, preprocess, postprocess = get_interface_args(pipeline) - fn = get_fn(name, preprocess, postprocess, api_key) - - if pipeline == "chat": - interface = gr.ChatInterface(fn=fn, **kwargs) - else: - # For other pipelines, create a standard Interface (not implemented yet) - interface = gr.Interface(fn=fn, inputs=inputs, outputs=outputs, **kwargs) - - return interface \ No newline at end of file diff --git a/agents/promptize/src/promptize/providers/cohere_gradio.py b/agents/promptize/src/promptize/providers/cohere_gradio.py deleted file mode 100644 index f216a4f..0000000 --- a/agents/promptize/src/promptize/providers/cohere_gradio.py +++ /dev/null @@ -1,120 +0,0 @@ -import os -import cohere -import gradio as gr -from typing import Callable -import base64 - -__version__ = "0.0.3" - - -def get_fn(model_name: str, preprocess: Callable, postprocess: Callable, api_key: str): - def fn(message, history): - inputs = preprocess(message, history) - client = cohere.Client(api_key=api_key) - stream = client.chat_stream( - model=model_name, - message=inputs["message"], - chat_history=inputs["chat_history"], - ) - response_text = "" - for chunk in stream: - if chunk.event_type == "text-generation": - delta = chunk.text - response_text += delta - yield postprocess(response_text) - - return fn - - -def get_image_base64(url: str, ext: str): - with open(url, "rb") as image_file: - encoded_string = base64.b64encode(image_file.read()).decode('utf-8') - return "data:image/" + ext + ";base64," + encoded_string - - -def handle_user_msg(message: str): - if type(message) is str: - return message - elif type(message) is dict: - if message["files"] is not None and len(message["files"]) > 0: - ext = os.path.splitext(message["files"][-1])[1].strip(".") - if ext.lower() in ["png", "jpg", "jpeg", "gif", "pdf"]: - encoded_str = get_image_base64(message["files"][-1], ext) - else: - raise NotImplementedError(f"Not supported file type {ext}") - return { - "message": message["text"], - "attachments": [ - { - "source": encoded_str, - "type": "image", - } - ] - } - else: - return message["text"] - else: - raise NotImplementedError - - -def get_interface_args(pipeline): - if pipeline == "chat": - inputs = None - outputs = None - - def preprocess(message, history): - chat_history = [] - files = None - for user_msg, assistant_msg in history: - if assistant_msg is not None: - chat_history.append({"role": "USER", "message": handle_user_msg(user_msg)}) - chat_history.append({"role": "ASSISTANT", "message": assistant_msg}) - else: - files = user_msg - if type(message) is str and files is not None: - message = {"text": message, "files": files} - elif type(message) is dict and files is not None: - if message["files"] is None or len(message["files"]) == 0: - message["files"] = files - - return { - "message": handle_user_msg(message), - "chat_history": chat_history - } - - postprocess = lambda x: x - else: - # Add other pipeline types when they will be needed - raise ValueError(f"Unsupported pipeline type: {pipeline}") - return inputs, outputs, preprocess, postprocess - - -def get_pipeline(model_name): - # Determine the pipeline type based on the model name - # For simplicity, assuming all models are chat models at the moment - return "chat" - - -def registry(name: str, token: str | None = None, **kwargs): - """ - Create a Gradio Interface for a model on Cohere. - - Parameters: - - name (str): The name of the Cohere model. - - token (str, optional): The API key for Cohere. - """ - api_key = token or os.environ.get("COHERE_API_KEY") - if not api_key: - raise ValueError("COHERE_API_KEY environment variable is not set.") - - pipeline = get_pipeline(name) - inputs, outputs, preprocess, postprocess = get_interface_args(pipeline) - fn = get_fn(name, preprocess, postprocess, api_key) - - if pipeline == "chat": - interface = gr.ChatInterface(fn=fn, multimodal=True, **kwargs) - else: - # For other pipelines, create a standard Interface (not implemented yet) - interface = gr.Interface(fn=fn, inputs=inputs, outputs=outputs, **kwargs) - - return interface \ No newline at end of file diff --git a/agents/promptize/src/promptize/providers/crewai_gradio.py b/agents/promptize/src/promptize/providers/crewai_gradio.py deleted file mode 100644 index 383c639..0000000 --- a/agents/promptize/src/promptize/providers/crewai_gradio.py +++ /dev/null @@ -1,363 +0,0 @@ -import os -from typing import List, Dict, Generator -import gradio as gr -from crewai import Agent, Task, Crew -from crewai_tools import ScrapeWebsiteTool -import queue -import threading -import asyncio - -class MessageQueue: - def __init__(self): - self.message_queue = queue.Queue() - self.last_agent = None - - def add_message(self, message: Dict): - print(f"Adding message to queue: {message}") - self.message_queue.put(message) - - def get_messages(self) -> List[Dict]: - messages = [] - while not self.message_queue.empty(): - messages.append(self.message_queue.get()) - return messages - -class CrewFactory: - @staticmethod - def create_crew_config(crew_type: str, topic: str) -> dict: - configs = { - "article": { - "agents": [ - { - "role": "Content Planner", - "goal": f"Plan engaging and factually accurate content on {topic}", - "backstory": "Expert content planner with focus on creating engaging outlines", - "tasks": [ - """Create a detailed content plan for an article by: - 1. Prioritizing the latest trends and key players - 2. Identifying the target audience - 3. Developing a detailed content outline - 4. Including SEO keywords and sources""" - ] - }, - { - "role": "Content Writer", - "goal": f"Write insightful piece about {topic}", - "backstory": "Expert content writer with focus on engaging articles", - "tasks": [ - """1. Use the content plan to craft a compelling blog post - 2. Incorporate SEO keywords naturally - 3. Create proper structure with introduction, body, and conclusion""" - ] - }, - { - "role": "Editor", - "goal": "Polish and refine the content", - "backstory": "Expert editor with eye for detail and clarity", - "tasks": [ - """1. Review for clarity and coherence - 2. Correct any errors - 3. Ensure consistent tone and style""" - ] - } - ] - }, - "support": { - "agents": [ - { - "role": "Senior Support Representative", - "goal": "Be the most helpful support representative", - "backstory": "Expert at analyzing questions and providing support", - "tasks": [ - f"""Analyze this inquiry thoroughly: {topic} - Provide detailed support response.""" - ] - }, - { - "role": "Support Quality Assurance", - "goal": "Ensure highest quality of support responses", - "backstory": "Expert at reviewing and improving support responses", - "tasks": [ - """Review and improve the support response to ensure it's: - 1. Comprehensive and helpful - 2. Properly formatted with clear structure""" - ] - } - ] - } - } - return configs.get(crew_type, configs["article"]) - -class CrewManager: - def __init__(self, api_key: str = None): - self.api_key = api_key - self.message_queue = MessageQueue() - self.agents = [] - self.current_agent = None - self.scrape_tool = None - - def initialize_agents(self, crew_type: str, topic: str, website_url: str = None): - if not self.api_key: - raise ValueError("OpenAI API key is required") - - os.environ["OPENAI_API_KEY"] = self.api_key - if website_url: - self.scrape_tool = ScrapeWebsiteTool(website_url=website_url) - - # Get crew configuration - config = CrewFactory.create_crew_config(crew_type, topic) - - # Initialize agents from configuration - self.agents = [] - for agent_config in config["agents"]: - agent = Agent( - role=agent_config["role"], - goal=agent_config["goal"], - backstory=agent_config["backstory"], - allow_delegation=False, - verbose=True - ) - self.agents.append((agent, agent_config["tasks"])) - - def create_tasks(self, topic: str) -> List[Task]: - tasks = [] - for agent, task_descriptions in self.agents: - for task_desc in task_descriptions: - task = Task( - description=task_desc, - expected_output="Detailed and well-formatted response", - agent=agent, - tools=[self.scrape_tool] if self.scrape_tool else [] - ) - tasks.append(task) - return tasks - - async def process_support(self, inquiry: str, website_url: str, crew_type: str) -> Generator[List[Dict], None, None]: - def add_agent_messages(agent_name: str, tasks: str, emoji: str = "🤖"): - self.message_queue.add_message({ - "role": "assistant", - "content": agent_name, - "metadata": {"title": f"{emoji} {agent_name}"} - }) - - self.message_queue.add_message({ - "role": "assistant", - "content": tasks, - "metadata": {"title": f"📋 Task for {agent_name}"} - }) - - def setup_next_agent(current_agent: str): - if crew_type == "support": - if current_agent == "Senior Support Representative": - self.current_agent = "Support Quality Assurance Specialist" - add_agent_messages( - "Support Quality Assurance Specialist", - "Review and improve the support response" - ) - elif crew_type == "article": - if current_agent == "Content Planner": - self.current_agent = "Content Writer" - add_agent_messages( - "Content Writer", - "Write the article based on the content plan" - ) - elif current_agent == "Content Writer": - self.current_agent = "Editor" - add_agent_messages( - "Editor", - "Review and polish the article" - ) - - def task_callback(task_output): - raw_output = task_output.raw - if "## Final Answer:" in raw_output: - content = raw_output.split("## Final Answer:")[1].strip() - else: - content = raw_output.strip() - - if self.current_agent == "Support Quality Assurance Specialist": - self.message_queue.add_message({ - "role": "assistant", - "content": "Final response is ready!", - "metadata": {"title": "✅ Final Response"} - }) - - formatted_content = content - formatted_content = formatted_content.replace("\n#", "\n\n#") - formatted_content = formatted_content.replace("\n-", "\n\n-") - formatted_content = formatted_content.replace("\n*", "\n\n*") - formatted_content = formatted_content.replace("\n1.", "\n\n1.") - formatted_content = formatted_content.replace("\n\n\n", "\n\n") - - self.message_queue.add_message({ - "role": "assistant", - "content": formatted_content - }) - else: - self.message_queue.add_message({ - "role": "assistant", - "content": content, - "metadata": {"title": f"✨ Output from {self.current_agent}"} - }) - setup_next_agent(self.current_agent) - - try: - self.initialize_agents(crew_type, inquiry, website_url) - # Set initial agent based on crew type - self.current_agent = "Senior Support Representative" if crew_type == "support" else "Content Planner" - - yield [{ - "role": "assistant", - "content": "Starting to process your inquiry...", - "metadata": {"title": "🚀 Process Started"} - }] - - # Set initial task message based on crew type - if crew_type == "support": - add_agent_messages( - "Senior Support Representative", - "Analyze inquiry and provide comprehensive support" - ) - else: - add_agent_messages( - "Content Planner", - "Create a detailed content plan for the article" - ) - - crew = Crew( - agents=[agent for agent, _ in self.agents], - tasks=self.create_tasks(inquiry), - verbose=True, - task_callback=task_callback - ) - - def run_crew(): - try: - crew.kickoff() - except Exception as e: - print(f"Error in crew execution: {str(e)}") - self.message_queue.add_message({ - "role": "assistant", - "content": f"Error: {str(e)}", - "metadata": {"title": "❌ Error"} - }) - - thread = threading.Thread(target=run_crew) - thread.start() - - while thread.is_alive() or not self.message_queue.message_queue.empty(): - messages = self.message_queue.get_messages() - if messages: - yield messages - await asyncio.sleep(0.1) - - except Exception as e: - print(f"Error in process_support: {str(e)}") - yield [{ - "role": "assistant", - "content": f"An error occurred: {str(e)}", - "metadata": {"title": "❌ Error"} - }] - -def registry(name: str, token: str | None = None, crew_type: str = "support", **kwargs): - default_api_key = os.environ.get("OPENAI_API_KEY") - crew_manager = None - - with gr.Blocks(theme=gr.themes.Soft()) as demo: - title = "📝 AI Article Writing Crew" if crew_type == "article" else "🤖 CrewAI Assistant" - gr.Markdown(f"# {title}") - - api_key = gr.Textbox( - label="OpenAI API Key", - type="password", - placeholder="Type your OpenAI API key and press Enter...", - interactive=True, - visible=not (token or default_api_key) - ) - - chatbot = gr.Chatbot( - label="Writing Process" if crew_type == "article" else "Process", - height=700 if crew_type == "article" else 600, - show_label=True, - visible=token or default_api_key, - avatar_images=(None, "https://avatars.githubusercontent.com/u/170677839?v=4"), - render_markdown=True, - type="messages" - ) - - with gr.Row(equal_height=True): - topic = gr.Textbox( - label="Article Topic" if crew_type == "article" else "Topic/Question", - placeholder="Enter topic..." if crew_type == "article" else "Enter your question...", - scale=4, - visible=token or default_api_key - ) - website_url = gr.Textbox( - label="Documentation URL", - placeholder="Enter documentation URL to search...", - scale=4, - visible=(token or default_api_key) and crew_type == "support" - ) - btn = gr.Button( - "Write Article" if crew_type == "article" else "Start", - variant="primary", - scale=1, - visible=token or default_api_key - ) - - async def process_input(topic, website_url, history, api_key): - nonlocal crew_manager - effective_api_key = token or api_key or default_api_key - - if not effective_api_key: - yield [ - {"role": "user", "content": f"Question: {topic}\nDocumentation: {website_url}"}, - {"role": "assistant", "content": "Please provide an OpenAI API key."} - ] - return # Early return without value - - if crew_manager is None: - crew_manager = CrewManager(api_key=effective_api_key) - - messages = [{"role": "user", "content": f"Question: {topic}\nDocumentation: {website_url}"}] - yield messages - - try: - async for new_messages in crew_manager.process_support(topic, website_url, crew_type): - for msg in new_messages: - if "metadata" in msg: - messages.append({ - "role": msg["role"], - "content": msg["content"], - "metadata": {"title": msg["metadata"]["title"]} - }) - else: - messages.append({ - "role": msg["role"], - "content": msg["content"] - }) - yield messages - except Exception as e: - messages.append({ - "role": "assistant", - "content": f"An error occurred: {str(e)}", - "metadata": {"title": "❌ Error"} - }) - yield messages - - def show_interface(): - return { - api_key: gr.Textbox(visible=False), - chatbot: gr.Chatbot(visible=True), - topic: gr.Textbox(visible=True), - website_url: gr.Textbox(visible=True), - btn: gr.Button(visible=True) - } - - if not (token or default_api_key): - api_key.submit(show_interface, None, [api_key, chatbot, topic, website_url, btn]) - - btn.click(process_input, [topic, website_url, chatbot, api_key], [chatbot]) - topic.submit(process_input, [topic, website_url, chatbot, api_key], [chatbot]) - - return demo \ No newline at end of file diff --git a/agents/promptize/src/promptize/providers/deepseek_gradio.py b/agents/promptize/src/promptize/providers/deepseek_gradio.py deleted file mode 100644 index 3c6655c..0000000 --- a/agents/promptize/src/promptize/providers/deepseek_gradio.py +++ /dev/null @@ -1,120 +0,0 @@ -import os -import base64 -from openai import OpenAI -import gradio as gr -from typing import Callable - -__version__ = "0.0.1" - -def get_image_base64(url: str, ext: str): - with open(url, "rb") as image_file: - encoded_string = base64.b64encode(image_file.read()).decode('utf-8') - return "data:image/" + ext + ";base64," + encoded_string - -def get_fn(model_name: str, preprocess: Callable, postprocess: Callable, api_key: str): - def fn(message, history): - inputs = preprocess(message, history) - client = OpenAI( - base_url="https://api.deepseek.com", - api_key=api_key, - ) - try: - completion = client.chat.completions.create( - model="deepseek-chat", - messages=inputs["messages"], - stream=True, - ) - response_text = "" - for chunk in completion: - delta = chunk.choices[0].delta.content or "" - response_text += delta - yield postprocess(response_text) - except Exception as e: - error_message = f"Error: {str(e)}" - return error_message - - return fn - -def handle_user_msg(message: str): - if type(message) is str: - return message - elif type(message) is dict: - if message["files"] is not None and len(message["files"]) > 0: - ext = os.path.splitext(message["files"][-1])[1].strip(".") - if ext.lower() in ["png", "jpg", "jpeg", "gif", "pdf"]: - encoded_str = get_image_base64(message["files"][-1], ext) - else: - raise NotImplementedError(f"Not supported file type {ext}") - content = [ - {"type": "text", "text": message["text"]}, - { - "type": "image_url", - "image_url": { - "url": encoded_str, - } - }, - ] - else: - content = message["text"] - return content - else: - raise NotImplementedError - -def get_interface_args(pipeline): - if pipeline == "chat": - inputs = None - outputs = None - - def preprocess(message, history): - messages = [] - files = None - for user_msg, assistant_msg in history: - if assistant_msg is not None: - messages.append({"role": "user", "content": handle_user_msg(user_msg)}) - messages.append({"role": "assistant", "content": assistant_msg}) - else: - files = user_msg - if type(message) is str and files is not None: - message = {"text":message, "files":files} - elif type(message) is dict and files is not None: - if message["files"] is None or len(message["files"]) == 0: - message["files"] = files - messages.append({"role": "user", "content": handle_user_msg(message)}) - return {"messages": messages} - - postprocess = lambda x: x # No post-processing needed - else: - # Add other pipeline types when they will be needed - raise ValueError(f"Unsupported pipeline type: {pipeline}") - return inputs, outputs, preprocess, postprocess - - -def get_pipeline(model_name): - # Determine the pipeline type based on the model name - # For simplicity, assuming all models are chat models at the moment - return "chat" - - -def registry(name: str, token: str | None = None, **kwargs): - """ - Create a Gradio Interface for a model on DeepSeek. - - Parameters: - - name (str): The name of the model on DeepSeek. - - token (str, optional): The API key for DeepSeek. - """ - api_key = token or os.environ.get("DEEPSEEK_API_KEY") - if not api_key: - raise ValueError("DEEPSEEK_API_KEY environment variable is not set.") - - pipeline = get_pipeline(name) - inputs, outputs, preprocess, postprocess = get_interface_args(pipeline) - fn = get_fn(name, preprocess, postprocess, api_key) - - if pipeline == "chat": - interface = gr.ChatInterface(fn=fn, **kwargs) - else: - # For other pipelines, create a standard Interface (not implemented yet) - interface = gr.Interface(fn=fn, inputs=inputs, outputs=outputs, **kwargs) - - return interface \ No newline at end of file diff --git a/agents/promptize/src/promptize/providers/fireworks_gradio.py b/agents/promptize/src/promptize/providers/fireworks_gradio.py deleted file mode 100644 index d17de0c..0000000 --- a/agents/promptize/src/promptize/providers/fireworks_gradio.py +++ /dev/null @@ -1,257 +0,0 @@ -import os -from openai import OpenAI -import gradio as gr -from typing import Callable -from fireworks.client.audio import AudioInference - -__version__ = "0.0.3" - -LANGUAGES = { - "en": "english", "zh": "chinese", "de": "german", "es": "spanish", - "ru": "russian", "ko": "korean", "fr": "french", "ja": "japanese", - "pt": "portuguese", "tr": "turkish", "pl": "polish", "ca": "catalan", - "nl": "dutch", "ar": "arabic", "sv": "swedish", "it": "italian", - "id": "indonesian", "hi": "hindi", "fi": "finnish", "vi": "vietnamese", - "he": "hebrew", "uk": "ukrainian", "el": "greek", "ms": "malay", - "cs": "czech", "ro": "romanian", "da": "danish", "hu": "hungarian", - "ta": "tamil", "no": "norwegian", "th": "thai", "ur": "urdu", - "hr": "croatian", "bg": "bulgarian", "lt": "lithuanian", "la": "latin", - "mi": "maori", "ml": "malayalam", "cy": "welsh", "sk": "slovak", - "te": "telugu", "fa": "persian", "lv": "latvian", "bn": "bengali", - "sr": "serbian", "az": "azerbaijani", "sl": "slovenian", "kn": "kannada", - "et": "estonian", "mk": "macedonian", "br": "breton", "eu": "basque", - "is": "icelandic", "hy": "armenian", "ne": "nepali", "mn": "mongolian", - "bs": "bosnian", "kk": "kazakh", "sq": "albanian", "sw": "swahili", - "gl": "galician", "mr": "marathi", "pa": "punjabi", "si": "sinhala", - "km": "khmer", "sn": "shona", "yo": "yoruba", "so": "somali", - "af": "afrikaans", "oc": "occitan", "ka": "georgian", "be": "belarusian", - "tg": "tajik", "sd": "sindhi", "gu": "gujarati", "am": "amharic", - "yi": "yiddish", "lo": "lao", "uz": "uzbek", "fo": "faroese", - "ht": "haitian creole", "ps": "pashto", "tk": "turkmen", "nn": "nynorsk", - "mt": "maltese", "sa": "sanskrit", "lb": "luxembourgish", "my": "myanmar", - "bo": "tibetan", "tl": "tagalog", "mg": "malagasy", "as": "assamese", - "tt": "tatar", "haw": "hawaiian", "ln": "lingala", "ha": "hausa", - "ba": "bashkir", "jw": "javanese", "su": "sundanese", "yue": "cantonese" -} - -# Language code lookup by name, with additional aliases -TO_LANGUAGE_CODE = { - **{language: code for code, language in LANGUAGES.items()}, - "burmese": "my", - "valencian": "ca", - "flemish": "nl", - "haitian": "ht", - "letzeburgesch": "lb", - "pushto": "ps", - "panjabi": "pa", - "moldavian": "ro", - "moldovan": "ro", - "sinhalese": "si", - "castilian": "es", - "mandarin": "zh" -} - - -def get_fn(model_name: str, preprocess: Callable, postprocess: Callable, api_key: str): - if "whisper" in model_name: - def fn(message, history, audio_input=None): - # Handle audio input if provided - if audio_input: - if not audio_input.endswith('.wav'): - new_path = audio_input + '.wav' - os.rename(audio_input, new_path) - audio_input = new_path - - base_url = ( - "https://audio-turbo.us-virginia-1.direct.fireworks.ai" - if model_name == "whisper-v3-turbo" - else "https://audio-prod.us-virginia-1.direct.fireworks.ai" - ) - - client = AudioInference( - model=model_name, - base_url=base_url, - api_key=api_key - ) - - with open(audio_input, "rb") as f: - audio_data = f.read() - response = client.transcribe(audio=audio_data) - return {"role": "assistant", "content": response.text} - - # Handle text message - if isinstance(message, dict): # Multimodal input - audio_path = message.get("files", [None])[0] or message.get("audio") - text = message.get("text", "") - if audio_path: - # Process audio file - return fn(None, history, audio_path) - return {"role": "assistant", "content": "No audio input provided."} - else: # String input - return {"role": "assistant", "content": "Please upload an audio file or use the microphone to record audio."} - - else: - def fn(message, history, audio_input=None): - # Ignore audio_input for non-whisper models - inputs = preprocess(message, history) - client = OpenAI( - base_url="https://api.fireworks.ai/inference/v1", - api_key=api_key - ) - - model_path = ( - "accounts/fireworks/agents/f1-preview" if model_name == "f1-preview" - else "accounts/fireworks/agents/f1-mini-preview" if model_name == "f1-mini" - else f"accounts/fireworks/models/{model_name}" - ) - - completion = client.chat.completions.create( - model=model_path, - messages=[{"role": "user", "content": inputs["prompt"]}], - stream=True, - max_tokens=1024, - temperature=0.7, - top_p=1, - ) - - response_text = "" - for chunk in completion: - delta = chunk.choices[0].delta.content or "" - response_text += delta - yield {"role": "assistant", "content": response_text} - - return fn - - -def get_interface_args(pipeline): - if pipeline == "audio": - inputs = [ - gr.Audio(sources=["microphone"], type="filepath"), - gr.Radio(["transcribe"], label="Task", value="transcribe"), - ] - outputs = "text" - - def preprocess(audio_path, task, text, history): - if audio_path and not audio_path.endswith('.wav'): - new_path = audio_path + '.wav' - os.rename(audio_path, new_path) - audio_path = new_path - return {"role": "user", "content": {"audio_path": audio_path, "task": task}} - - def postprocess(text): - return {"role": "assistant", "content": text} - - elif pipeline == "chat": - inputs = gr.Textbox(label="Message") - outputs = "text" - - def preprocess(message, history): - return {"prompt": message} - - def postprocess(response): - return response - - else: - raise ValueError(f"Unsupported pipeline type: {pipeline}") - - return inputs, outputs, preprocess, postprocess - - -def get_pipeline(model_name): - if "whisper" in model_name: - return "audio" - return "chat" - - -def registry(name: str, token: str | None = None, **kwargs): - """ - Create a Gradio Interface for a model on Fireworks. - Can be used directly or with gr.load: - - Example: - # Direct usage - interface = fireworks_gradio.registry("whisper-v3", token="your-api-key") - interface.launch() - - # With gr.load - gr.load( - name='whisper-v3', - src=fireworks_gradio.registry, - ).launch() - - Parameters: - name (str): The name of the OpenAI model. - token (str, optional): The API key for OpenAI. - """ - # Make the function compatible with gr.load by accepting name as a positional argument - if not isinstance(name, str): - raise ValueError("Model name must be a string") - - api_key = token or os.environ.get("FIREWORKS_API_KEY") - if not api_key: - raise ValueError("FIREWORKS_API_KEY environment variable is not set.") - - pipeline = get_pipeline(name) - _, _, preprocess, postprocess = get_interface_args(pipeline) - fn = get_fn(name, preprocess, postprocess, api_key) - - description = kwargs.pop("description", None) - if "whisper" in name: - description = (description or "") + """ - \n\nSupported inputs: - - Upload audio files using the textbox - - Record audio using the microphone - """ - - with gr.Blocks() as interface: - chatbot = gr.Chatbot(type="messages") - with gr.Row(): - mic = gr.Audio(sources=["microphone"], type="filepath", label="Record Audio") - - def process_audio(audio_path): - if audio_path: - if not audio_path.endswith('.wav'): - new_path = audio_path + '.wav' - os.rename(audio_path, new_path) - audio_path = new_path - - # Create message format expected by fn - message = {"files": [audio_path], "text": ""} - response = fn(message, []) - - return [ - {"role": "user", "content": gr.Audio(value=audio_path)}, - {"role": "assistant", "content": response["content"]} - ] - return [] - - mic.change( - fn=process_audio, - inputs=[mic], - outputs=[chatbot] - ) - - else: - # For non-whisper models, use regular ChatInterface - interface = gr.ChatInterface( - fn=fn, - type="messages", - description=description, - **kwargs - ) - - return interface - - -# Add these to make the module more discoverable -MODELS = [ - "whisper-v3", - "whisper-v3-turbo", - "f1-preview", - "f1-mini", - # Add other supported models here -] - -def get_all_models(): - """Returns a list of all supported models.""" - return MODELS \ No newline at end of file diff --git a/agents/promptize/src/promptize/providers/gemini_gradio.py b/agents/promptize/src/promptize/providers/gemini_gradio.py deleted file mode 100644 index c57946d..0000000 --- a/agents/promptize/src/promptize/providers/gemini_gradio.py +++ /dev/null @@ -1,760 +0,0 @@ -import os -from typing import Callable -import gradio as gr -import google.generativeai as genai -import base64 -import json -import numpy as np -import websockets.sync.client -from gradio_webrtc import StreamHandler, WebRTC, get_twilio_turn_credentials -import cv2 -import PIL.Image -import io -import re -import modelscope_studio.components.base as ms -import modelscope_studio.components.legacy as legacy -import modelscope_studio.components.antd as antd - - -__version__ = "0.0.3" - -# Constants for coder interface -SystemPrompt = """You are an expert web developer specializing in creating clean, efficient, and modern web applications. -Your task is to write complete, self-contained HTML files that include all necessary CSS and JavaScript. -Focus on: -- Writing clear, maintainable code -- Following best practices -- Creating responsive designs -- Adding appropriate styling and interactivity -Return only the complete HTML code without any additional explanation.""" - -DEMO_LIST = [ - { - "card": {"index": 0}, - "title": "Simple Button", - "description": "Create a button that changes color when clicked" - }, - { - "card": {"index": 1}, - "title": "Todo List", - "description": "Create a simple todo list with add/remove functionality" - }, - { - "card": {"index": 2}, - "title": "Timer App", - "description": "Create a countdown timer with start/pause/reset controls" - } -] - - -class GeminiConfig: - def __init__(self): - self.api_key = os.getenv("GEMINI_API_KEY") - self.host = "generativelanguage.googleapis.com" - self.model = "models/gemini-2.0-flash-exp" - self.ws_url = f"wss://{self.host}/ws/google.ai.generativelanguage.v1alpha.GenerativeService.BidiGenerateContent?key={self.api_key}" - - -class AudioProcessor: - @staticmethod - def encode_audio(data, sample_rate): - encoded = base64.b64encode(data.tobytes()).decode("UTF-8") - return { - "realtimeInput": { - "mediaChunks": [ - { - "mimeType": f"audio/pcm;rate={sample_rate}", - "data": encoded, - } - ], - }, - } - - @staticmethod - def process_audio_response(data): - audio_data = base64.b64decode(data) - return np.frombuffer(audio_data, dtype=np.int16) - - -def detection(frame, conf_threshold=0.3): - """Process video frame.""" - try: - # Convert BGR to RGB - image_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) - - # Create PIL Image - pil_image = PIL.Image.fromarray(image_rgb) - pil_image.thumbnail([1024, 1024]) - - # Convert back to numpy array - processed_frame = np.array(pil_image) - - # Convert back to BGR for OpenCV - processed_frame = cv2.cvtColor(processed_frame, cv2.COLOR_RGB2BGR) - - return processed_frame - except Exception as e: - print(f"Error processing frame: {e}") - return frame - - -class GeminiHandler(StreamHandler): - def __init__(self, expected_layout="mono", output_sample_rate=24000, output_frame_size=480) -> None: - super().__init__(expected_layout, output_sample_rate, output_frame_size, input_sample_rate=24000) - self.config = GeminiConfig() - self.ws = None - self.all_output_data = None - self.audio_processor = AudioProcessor() - self.current_frame = None - - def copy(self): - handler = GeminiHandler( - expected_layout=self.expected_layout, - output_sample_rate=self.output_sample_rate, - output_frame_size=self.output_frame_size, - ) - return handler - - def _initialize_websocket(self): - try: - self.ws = websockets.sync.client.connect(self.config.ws_url, timeout=30) - initial_request = { - "setup": { - "model": self.config.model, - } - } - self.ws.send(json.dumps(initial_request)) - setup_response = json.loads(self.ws.recv()) - print(f"Setup response: {setup_response}") - except websockets.exceptions.WebSocketException as e: - print(f"WebSocket connection failed: {str(e)}") - self.ws = None - except Exception as e: - print(f"Setup failed: {str(e)}") - self.ws = None - - def process_video_frame(self, frame): - self.current_frame = frame - _, buffer = cv2.imencode('.jpg', frame) - image_data = base64.b64encode(buffer).decode('utf-8') - return image_data - - def receive(self, frame: tuple[int, np.ndarray]) -> None: - try: - if not self.ws: - self._initialize_websocket() - - _, array = frame - array = array.squeeze() - - audio_data = self.audio_processor.encode_audio(array, self.output_sample_rate) - - message = { - "realtimeInput": { - "mediaChunks": [ - { - "mimeType": f"audio/pcm;rate={self.output_sample_rate}", - "data": audio_data["realtimeInput"]["mediaChunks"][0]["data"], - } - ], - } - } - - if self.current_frame is not None: - image_data = self.process_video_frame(self.current_frame) - message["realtimeInput"]["mediaChunks"].append({ - "mimeType": "image/jpeg", - "data": image_data - }) - - self.ws.send(json.dumps(message)) - except Exception as e: - print(f"Error in receive: {str(e)}") - if self.ws: - self.ws.close() - self.ws = None - - def _process_server_content(self, content): - for part in content.get("parts", []): - data = part.get("inlineData", {}).get("data", "") - if data: - audio_array = self.audio_processor.process_audio_response(data) - if self.all_output_data is None: - self.all_output_data = audio_array - else: - self.all_output_data = np.concatenate((self.all_output_data, audio_array)) - - while self.all_output_data.shape[-1] >= self.output_frame_size: - yield (self.output_sample_rate, self.all_output_data[: self.output_frame_size].reshape(1, -1)) - self.all_output_data = self.all_output_data[self.output_frame_size :] - - def generator(self): - while True: - if not self.ws: - print("WebSocket not connected") - yield None - continue - - try: - message = self.ws.recv(timeout=5) - msg = json.loads(message) - - if "serverContent" in msg: - content = msg["serverContent"].get("modelTurn", {}) - yield from self._process_server_content(content) - except TimeoutError: - print("Timeout waiting for server response") - yield None - except Exception as e: - print(f"Error in generator: {str(e)}") - yield None - - def emit(self) -> tuple[int, np.ndarray] | None: - if not self.ws: - return None - if not hasattr(self, "_generator"): - self._generator = self.generator() - try: - return next(self._generator) - except StopIteration: - self.reset() - return None - - def reset(self) -> None: - if hasattr(self, "_generator"): - delattr(self, "_generator") - self.all_output_data = None - - def shutdown(self) -> None: - if self.ws: - self.ws.close() - - def check_connection(self): - try: - if not self.ws or self.ws.closed: - self._initialize_websocket() - return True - except Exception as e: - print(f"Connection check failed: {str(e)}") - return False - - -def get_fn(model_name: str, preprocess: Callable, postprocess: Callable, api_key: str): - def fn(message, history, enable_search): - inputs = preprocess(message, history, enable_search) - is_gemini = model_name.startswith("gemini-") - - if is_gemini: - genai.configure(api_key=api_key) - - generation_config = { - "temperature": 1, - "top_p": 0.95, - "top_k": 40, - "max_output_tokens": 8192, - "response_mime_type": "text/plain", - } - - model = genai.GenerativeModel( - model_name=model_name, - generation_config=generation_config - ) - - chat = model.start_chat(history=inputs.get("history", [])) - - if inputs.get("enable_search"): - response = chat.send_message( - inputs["message"], - stream=True, - tools='google_search_retrieval' - ) - else: - response = chat.send_message(inputs["message"], stream=True) - - response_text = "" - for chunk in response: - if chunk.text: - response_text += chunk.text - yield {"role": "assistant", "content": response_text} - - return fn - - -def get_interface_args(pipeline, model_name: str): - if pipeline == "chat": - inputs = [gr.Checkbox(label="Enable Search", value=False)] - outputs = None - - def preprocess(message, history, enable_search): - is_gemini = model_name.startswith("gemini-") - if is_gemini: - # Handle multimodal input - if isinstance(message, dict): - parts = [] - if message.get("text"): - parts.append({"text": message["text"]}) - if message.get("files"): - for file in message["files"]: - # Determine file type and handle accordingly - if isinstance(file, str): # If it's a file path - mime_type = None - if file.lower().endswith('.pdf'): - mime_type = "application/pdf" - elif file.lower().endswith('.txt'): - mime_type = "text/plain" - elif file.lower().endswith('.html'): - mime_type = "text/html" - elif file.lower().endswith('.md'): - mime_type = "text/md" - elif file.lower().endswith('.csv'): - mime_type = "text/csv" - elif file.lower().endswith(('.js', '.javascript')): - mime_type = "application/x-javascript" - elif file.lower().endswith('.py'): - mime_type = "application/x-python" - - if mime_type: - try: - uploaded_file = genai.upload_file(file) - parts.append(uploaded_file) - except Exception as e: - print(f"Error uploading file: {e}") - else: - with open(file, "rb") as f: - image_data = f.read() - import base64 - image_data = base64.b64encode(image_data).decode() - parts.append({ - "inline_data": { - "mime_type": "image/jpeg", - "data": image_data - } - }) - else: # If it's binary data, treat as image - import base64 - image_data = base64.b64encode(file).decode() - parts.append({ - "inline_data": { - "mime_type": "image/jpeg", - "data": image_data - } - }) - message_parts = parts - else: - message_parts = [{"text": message}] - - # Process history - gemini_history = [] - for entry in history: - # Handle different history formats - if isinstance(entry, (list, tuple)): - user_msg, assistant_msg = entry - else: - # If it's a dict with role/content format - if entry.get("role") == "user": - user_msg = entry.get("content") - continue # Skip to next iteration to get assistant message - elif entry.get("role") == "assistant": - assistant_msg = entry.get("content") - continue # Skip to next iteration - else: - continue # Skip unknown roles - - # Process user message - if isinstance(user_msg, dict): - parts = [] - if user_msg.get("text"): - parts.append({"text": user_msg["text"]}) - if user_msg.get("files"): - for file in user_msg["files"]: - if isinstance(file, str): - mime_type = None - if file.lower().endswith('.pdf'): - mime_type = "application/pdf" - # ... (same mime type checks as before) - - if mime_type: - try: - uploaded_file = genai.upload_file(file) - parts.append(uploaded_file) - except Exception as e: - print(f"Error uploading file in history: {e}") - else: - with open(file, "rb") as f: - image_data = f.read() - import base64 - image_data = base64.b64encode(image_data).decode() - parts.append({ - "inline_data": { - "mime_type": "image/jpeg", - "data": image_data - } - }) - else: - import base64 - image_data = base64.b64encode(file).decode() - parts.append({ - "inline_data": { - "mime_type": "image/jpeg", - "data": image_data - } - }) - gemini_history.append({ - "role": "user", - "parts": parts - }) - else: - gemini_history.append({ - "role": "user", - "parts": [{"text": str(user_msg)}] - }) - - # Process assistant message - gemini_history.append({ - "role": "model", - "parts": [{"text": str(assistant_msg)}] - }) - - return { - "history": gemini_history, - "message": message_parts, - "enable_search": enable_search - } - else: - messages = [] - for user_msg, assistant_msg in history: - messages.append({"role": "user", "content": user_msg}) - messages.append({"role": "assistant", "content": assistant_msg}) - messages.append({"role": "user", "content": message}) - return {"messages": messages} - - postprocess = lambda x: x - else: - raise ValueError(f"Unsupported pipeline type: {pipeline}") - return inputs, outputs, preprocess, postprocess - - -def get_pipeline(model_name): - return "chat" - - -def registry( - name: str, - token: str | None = None, - examples: list | None = None, - enable_voice: bool = False, - enable_video: bool = False, - coder: bool = False, - **kwargs -): - env_key = "GEMINI_API_KEY" - api_key = token or os.environ.get(env_key) - if not api_key: - raise ValueError(f"{env_key} environment variable is not set.") - - if coder: - interface = gr.Blocks(css=""" - .left_header { - text-align: center; - margin-bottom: 20px; - } - - .right_panel { - background: white; - border-radius: 8px; - overflow: hidden; - box-shadow: 0 2px 8px rgba(0,0,0,0.15); - } - - .render_header { - background: #f5f5f5; - padding: 8px; - border-bottom: 1px solid #e8e8e8; - } - - .header_btn { - display: inline-block; - width: 12px; - height: 12px; - border-radius: 50%; - margin-right: 8px; - background: #ff5f56; - } - - .header_btn:nth-child(2) { - background: #ffbd2e; - } - - .header_btn:nth-child(3) { - background: #27c93f; - } - - .right_content { - padding: 24px; - height: 920px; - display: flex; - align-items: center; - justify-content: center; - } - - .html_content { - height: 920px; - width: 100%; - } - - .history_chatbot { - height: 100%; - } - """) - with interface: - history = gr.State([]) - setting = gr.State({"system": SystemPrompt}) - - with ms.Application() as app: - with antd.ConfigProvider(): - with antd.Row(gutter=[32, 12]) as layout: - # Left Column - with antd.Col(span=24, md=8): - with antd.Flex(vertical=True, gap="middle", wrap=True): - header = gr.HTML(""" -
-

Gemini Code Generator

-
- """) - - input = antd.InputTextarea( - size="large", - allow_clear=True, - placeholder="Describe the web application you want to create" - ) - btn = antd.Button("Generate", type="primary", size="large") - clear_btn = antd.Button("Clear History", type="default", size="large") - - antd.Divider("Examples") - with antd.Flex(gap="small", wrap=True): - with ms.Each(DEMO_LIST): - with antd.Card(hoverable=True, as_item="card") as demoCard: - antd.CardMeta() - - antd.Divider("Settings") - with antd.Flex(gap="small", wrap=True): - settingPromptBtn = antd.Button("⚙️ System Prompt", type="default") - codeBtn = antd.Button("🧑‍💻 View Code", type="default") - historyBtn = antd.Button("📜 History", type="default") - - # Modals and Drawers - with antd.Modal(open=False, title="System Prompt", width="800px") as system_prompt_modal: - systemPromptInput = antd.InputTextarea(SystemPrompt, auto_size=True) - - with antd.Drawer(open=False, title="Code", placement="left", width="750px") as code_drawer: - code_output = legacy.Markdown() - - with antd.Drawer(open=False, title="History", placement="left", width="900px") as history_drawer: - history_output = legacy.Chatbot( - show_label=False, - height=960, - elem_classes="history_chatbot" - ) - - # Right Column - with antd.Col(span=24, md=16): - with ms.Div(elem_classes="right_panel"): - gr.HTML(''' -
- - - -
- ''') - with antd.Tabs(active_key="empty", render_tab_bar="() => null") as state_tab: - with antd.Tabs.Item(key="empty"): - empty = antd.Empty( - description="Enter your request to generate code", - elem_classes="right_content" - ) - with antd.Tabs.Item(key="loading"): - loading = antd.Spin( - True, - tip="Generating code...", - size="large", - elem_classes="right_content" - ) - with antd.Tabs.Item(key="render"): - preview = gr.HTML(elem_classes="html_content") - - # Event Handlers - def demo_card_click(e: gr.EventData): - index = e._data['component']['index'] - return DEMO_LIST[index]['description'] - - def send_to_preview(code): - encoded_html = base64.b64encode(code.encode('utf-8')).decode('utf-8') - data_uri = f"data:text/html;charset=utf-8;base64,{encoded_html}" - return f'' - - def remove_code_block(text): - pattern = r'```html\n(.+?)\n```' - match = re.search(pattern, text, re.DOTALL) - if match: - return match.group(1).strip() - return text.strip() - - def generate_code(query, setting, history): - # Convert messages to Gemini's expected format - messages = [] - # Add system message - messages.append({ - "role": "user", - "parts": [{"text": setting["system"]}] - }) - messages.append({ - "role": "model", - "parts": [{"text": "I understand. I will help you write clean, efficient web code."}] - }) - - # Add history - for h in history: - messages.append({ - "role": "user", - "parts": [{"text": h[0]}] - }) - messages.append({ - "role": "model", - "parts": [{"text": h[1]}] - }) - - # Add current query - messages.append({ - "role": "user", - "parts": [{"text": query}] - }) - - genai.configure(api_key=api_key) - model = genai.GenerativeModel(model_name=name) - response = model.generate_content(messages, stream=True) - - response_text = "" - for chunk in response: - if chunk.text: - response_text += chunk.text - # Return all 5 required outputs - yield ( - response_text, # code_output (modelscopemarkdown) - history, # state - None, # preview (html) - gr.update(active_key="loading"), # state_tab (antdtabs) - gr.update(open=True) # code_drawer (antddrawer) - ) - - clean_code = remove_code_block(response_text) - new_history = history + [(query, response_text)] - - # Final yield with all outputs - yield ( - response_text, # code_output (modelscopemarkdown) - new_history, # state - send_to_preview(clean_code), # preview (html) - gr.update(active_key="render"), # state_tab (antdtabs) - gr.update(open=False) # code_drawer (antddrawer) - ) - - # Wire up event handlers - demoCard.click(demo_card_click, outputs=[input]) - settingPromptBtn.click(lambda: gr.update(open=True), outputs=[system_prompt_modal]) - system_prompt_modal.ok( - lambda input: ({"system": input}, gr.update(open=False)), - inputs=[systemPromptInput], - outputs=[setting, system_prompt_modal] - ) - system_prompt_modal.cancel(lambda: gr.update(open=False), outputs=[system_prompt_modal]) - - codeBtn.click(lambda: gr.update(open=True), outputs=[code_drawer]) - code_drawer.close(lambda: gr.update(open=False), outputs=[code_drawer]) - - historyBtn.click( - lambda h: (gr.update(open=True), h), - inputs=[history], - outputs=[history_drawer, history_output] - ) - history_drawer.close(lambda: gr.update(open=False), outputs=[history_drawer]) - - btn.click( - generate_code, - inputs=[input, setting, history], - outputs=[code_output, history, preview, state_tab, code_drawer] - ) - - clear_btn.click(lambda: [], outputs=[history]) - - return interface - - # Regular chat interface code continues here... - pipeline = get_pipeline(name) - inputs, outputs, preprocess, postprocess = get_interface_args(pipeline, name) - fn = get_fn(name, preprocess, postprocess, api_key) - - if examples: - formatted_examples = [[example, False] for example in examples] - kwargs["examples"] = formatted_examples - - if pipeline == "chat": - if enable_voice or enable_video: - interface = gr.Blocks() - with interface: - gr.HTML( - """ -
-

Gemini Chat

-
- """ - ) - - gemini_handler = GeminiHandler() - - with gr.Row(): - with gr.Column(scale=1): - if enable_video: - video = WebRTC( - label="Stream", - mode="send-receive", - modality="video", - rtc_configuration=get_twilio_turn_credentials() - ) - - if enable_voice: - audio = WebRTC( - label="Voice Chat", - modality="audio", - mode="send-receive", - rtc_configuration=get_twilio_turn_credentials(), - ) - - if enable_video: - video.stream( - fn=lambda frame: (frame, detection(frame)), - inputs=[video], - outputs=[video], - time_limit=90, - concurrency_limit=10 - ) - - if enable_voice: - audio.stream( - gemini_handler, - inputs=[audio], - outputs=[audio], - time_limit=90, - concurrency_limit=10 - ) - else: - interface = gr.ChatInterface( - fn=fn, - additional_inputs=inputs, - multimodal=True, - type="messages", - **kwargs - ) - else: - interface = gr.Interface(fn=fn, inputs=inputs, outputs=outputs, **kwargs) - - return interface \ No newline at end of file diff --git a/agents/promptize/src/promptize/providers/hyperbolic_gradio.py b/agents/promptize/src/promptize/providers/hyperbolic_gradio.py deleted file mode 100644 index 3974811..0000000 --- a/agents/promptize/src/promptize/providers/hyperbolic_gradio.py +++ /dev/null @@ -1,326 +0,0 @@ -import os -from openai import OpenAI -import gradio as gr -from typing import Callable -import base64 -import re -import modelscope_studio.components.base as ms -import modelscope_studio.components.legacy as legacy -import modelscope_studio.components.antd as antd - -# Constants for coder interface -SystemPrompt = """You are an expert web developer specializing in creating clean, efficient, and modern web applications. -Your task is to write complete, self-contained HTML files that include all necessary CSS and JavaScript. -Focus on: -- Writing clear, maintainable code -- Following best practices -- Creating responsive designs -- Adding appropriate styling and interactivity -Return only the complete HTML code without any additional explanation.""" - -DEMO_LIST = [ - { - "card": {"index": 0}, - "title": "Simple Button", - "description": "Create a button that changes color when clicked" - }, - { - "card": {"index": 1}, - "title": "Todo List", - "description": "Create a simple todo list with add/remove functionality" - }, - { - "card": {"index": 2}, - "title": "Timer App", - "description": "Create a countdown timer with start/pause/reset controls" - } -] - -def get_fn(model_name: str, preprocess: Callable, postprocess: Callable, api_key: str, base_url: str = None): - def fn(message, history): - inputs = preprocess(message, history) - # Set base URL and adjust endpoint handling - - client = OpenAI( - api_key=api_key, - base_url="https://api.hyperbolic.xyz/v1" - ) - - completion = client.chat.completions.create( - model=model_name, - messages=[ - *inputs["messages"] - ], - stream=True, - temperature=0.7, - max_tokens=512, - top_p=0.9, - ) - response_text = "" - for chunk in completion: - delta = chunk.choices[0].delta.content or "" - response_text += delta - yield postprocess(response_text) - - return fn - - -def get_interface_args(pipeline): - if pipeline == "chat": - inputs = None - outputs = None - - def preprocess(message, history): - messages = [] - for user_msg, assistant_msg in history: - messages.append({"role": "user", "content": user_msg}) - messages.append({"role": "assistant", "content": assistant_msg}) - messages.append({"role": "user", "content": message}) - return {"messages": messages} - - postprocess = lambda x: x # No post-processing needed - else: - # Add other pipeline types when they will be needed - raise ValueError(f"Unsupported pipeline type: {pipeline}") - return inputs, outputs, preprocess, postprocess - - -def get_pipeline(model_name): - # Determine the pipeline type based on the model name - # For simplicity, assuming all models are chat models at the moment - return "chat" - - -def registry(name: str, token: str | None = None, base_url: str | None = None, coder: bool = False, **kwargs): - api_key = token or os.environ.get("HYPERBOLIC_API_KEY") - if not api_key: - raise ValueError("API key is not set. Please provide a token or set HYPERBOLIC_API_KEY environment variable.") - - if coder: - interface = gr.Blocks(css=""" - .left_header { - text-align: center; - margin-bottom: 20px; - } - .right_panel { - background: white; - border-radius: 8px; - overflow: hidden; - box-shadow: 0 2px 8px rgba(0,0,0,0.15); - } - .render_header { - background: #f5f5f5; - padding: 8px; - border-bottom: 1px solid #e8e8e8; - } - .header_btn { - display: inline-block; - width: 12px; - height: 12px; - border-radius: 50%; - margin-right: 8px; - background: #ff5f56; - } - .header_btn:nth-child(2) { - background: #ffbd2e; - } - .header_btn:nth-child(3) { - background: #27c93f; - } - .right_content { - padding: 24px; - height: 920px; - display: flex; - align-items: center; - justify-content: center; - } - .html_content { - height: 920px; - width: 100%; - } - .history_chatbot { - height: 100%; - } - """) - - with interface: - history = gr.State([]) - setting = gr.State({"system": SystemPrompt}) - - with ms.Application() as app: - with antd.ConfigProvider(): - with antd.Row(gutter=[32, 12]) as layout: - # Left Column - with antd.Col(span=24, md=8): - with antd.Flex(vertical=True, gap="middle", wrap=True): - header = gr.HTML(""" -
-

Hyperbolic Code Generator

-
- """) - - input = antd.InputTextarea( - size="large", - allow_clear=True, - placeholder="Describe the web application you want to create" - ) - btn = antd.Button("Generate", type="primary", size="large") - clear_btn = antd.Button("Clear History", type="default", size="large") - - antd.Divider("Examples") - with antd.Flex(gap="small", wrap=True): - with ms.Each(DEMO_LIST): - with antd.Card(hoverable=True, as_item="card") as demoCard: - antd.CardMeta() - - antd.Divider("Settings") - with antd.Flex(gap="small", wrap=True): - settingPromptBtn = antd.Button("⚙️ System Prompt", type="default") - codeBtn = antd.Button("🧑‍💻 View Code", type="default") - historyBtn = antd.Button("📜 History", type="default") - - # Modals and Drawers - with antd.Modal(open=False, title="System Prompt", width="800px") as system_prompt_modal: - systemPromptInput = antd.InputTextarea(SystemPrompt, auto_size=True) - - with antd.Drawer(open=False, title="Code", placement="left", width="750px") as code_drawer: - code_output = legacy.Markdown() - - with antd.Drawer(open=False, title="History", placement="left", width="900px") as history_drawer: - history_output = legacy.Chatbot( - show_label=False, - height=960, - elem_classes="history_chatbot" - ) - - # Right Column - with antd.Col(span=24, md=16): - with ms.Div(elem_classes="right_panel"): - gr.HTML(''' -
- - - -
- ''') - with antd.Tabs(active_key="empty", render_tab_bar="() => null") as state_tab: - with antd.Tabs.Item(key="empty"): - empty = antd.Empty( - description="Enter your request to generate code", - elem_classes="right_content" - ) - with antd.Tabs.Item(key="loading"): - loading = antd.Spin( - True, - tip="Generating code...", - size="large", - elem_classes="right_content" - ) - with antd.Tabs.Item(key="render"): - preview = gr.HTML(elem_classes="html_content") - - # Helper functions - def demo_card_click(e: gr.EventData): - index = e._data['component']['index'] - return DEMO_LIST[index]['description'] - - def send_to_preview(code): - encoded_html = base64.b64encode(code.encode('utf-8')).decode('utf-8') - data_uri = f"data:text/html;charset=utf-8;base64,{encoded_html}" - return f'' - - def remove_code_block(text): - pattern = r'```html\n(.+?)\n```' - match = re.search(pattern, text, re.DOTALL) - if match: - return match.group(1).strip() - return text.strip() - - def generate_code(query, setting, history): - client = OpenAI( - api_key=api_key, - base_url="https://api.hyperbolic.xyz/v1" - ) - - messages = [ - {"role": "system", "content": setting["system"]}, - ] - - for h in history: - messages.append({"role": "user", "content": h[0]}) - messages.append({"role": "assistant", "content": h[1]}) - - messages.append({"role": "user", "content": query}) - - response = client.chat.completions.create( - model=name, - messages=messages, - stream=True, - temperature=0.7, - max_tokens=2048, - ) - - response_text = "" - for chunk in response: - if chunk.choices[0].delta.content: - response_text += chunk.choices[0].delta.content - yield ( - response_text, - history, - None, - gr.update(active_key="loading"), - gr.update(open=True) - ) - - clean_code = remove_code_block(response_text) - new_history = history + [(query, response_text)] - - yield ( - response_text, - new_history, - send_to_preview(clean_code), - gr.update(active_key="render"), - gr.update(open=False) - ) - - # Wire up event handlers - demoCard.click(demo_card_click, outputs=[input]) - settingPromptBtn.click(lambda: gr.update(open=True), outputs=[system_prompt_modal]) - system_prompt_modal.ok( - lambda input: ({"system": input}, gr.update(open=False)), - inputs=[systemPromptInput], - outputs=[setting, system_prompt_modal] - ) - system_prompt_modal.cancel(lambda: gr.update(open=False), outputs=[system_prompt_modal]) - - codeBtn.click(lambda: gr.update(open=True), outputs=[code_drawer]) - code_drawer.close(lambda: gr.update(open=False), outputs=[code_drawer]) - - historyBtn.click( - lambda h: (gr.update(open=True), h), - inputs=[history], - outputs=[history_drawer, history_output] - ) - history_drawer.close(lambda: gr.update(open=False), outputs=[history_drawer]) - - btn.click( - generate_code, - inputs=[input, setting, history], - outputs=[code_output, history, preview, state_tab, code_drawer] - ) - - clear_btn.click(lambda: [], outputs=[history]) - - return interface - - # Regular chat interface - pipeline = get_pipeline(name) - inputs, outputs, preprocess, postprocess = get_interface_args(pipeline) - fn = get_fn(name, preprocess, postprocess, api_key, base_url) - - if pipeline == "chat": - interface = gr.ChatInterface(fn=fn, **kwargs) - else: - interface = gr.Interface(fn=fn, inputs=inputs, outputs=outputs, **kwargs) - - return interface \ No newline at end of file diff --git a/agents/promptize/src/promptize/providers/lumaai_gradio.py b/agents/promptize/src/promptize/providers/lumaai_gradio.py deleted file mode 100644 index 045dfe3..0000000 --- a/agents/promptize/src/promptize/providers/lumaai_gradio.py +++ /dev/null @@ -1,164 +0,0 @@ -import os -from lumaai import LumaAI -import gradio as gr -from typing import Callable -import base64 -import time - -__version__ = "0.0.3" - - -def get_fn(preprocess: Callable, postprocess: Callable, api_key: str, pipeline: str): - def fn(message, history, generation_type): - try: - inputs = preprocess(message, history) - # Create a fresh client instance for each generation - client = LumaAI(auth_token=api_key) - - # Validate generation type - if generation_type not in ["video", "image"]: - raise ValueError(f"Invalid generation type: {generation_type}") - - try: - if generation_type == "video": - generation = client.generations.create( - prompt=inputs["prompt"], - **inputs.get("additional_params", {}) - ) - else: # image - generation = client.generations.image.create( - prompt=inputs["prompt"], - **inputs.get("additional_params", {}) - ) - except Exception as e: - raise RuntimeError(f"Failed to create generation: {str(e)}") - - # Poll for completion with timeout - start_time = time.time() - timeout = 300 # 5 minutes timeout - - while True: - if time.time() - start_time > timeout: - raise RuntimeError("Generation timed out after 5 minutes") - - try: - generation = client.generations.get(id=generation.id) - if generation.state == "completed": - asset_url = generation.assets.video if generation_type == "video" else generation.assets.image - break - elif generation.state == "failed": - raise RuntimeError(f"Generation failed: {generation.failure_reason}") - time.sleep(3) - yield f"Generating {generation_type}... (Status: {generation.state})" - except Exception as e: - raise RuntimeError(f"Error checking generation status: {str(e)}") - - # Return asset URL wrapped in appropriate format for display - yield postprocess(asset_url, generation_type) - - except Exception as e: - yield f"Error: {str(e)}" - raise - - return fn - - -def get_image_base64(url: str, ext: str): - with open(url, "rb") as image_file: - encoded_string = base64.b64encode(image_file.read()).decode('utf-8') - return "data:image/" + ext + ";base64," + encoded_string - - -def handle_user_msg(message: str): - if type(message) is str: - return message - elif type(message) is dict: - if message["files"] is not None and len(message["files"]) > 0: - ext = os.path.splitext(message["files"][-1])[1].strip(".") - if ext.lower() in ["png", "jpg", "jpeg", "gif", "pdf"]: - encoded_str = get_image_base64(message["files"][-1], ext) - else: - raise NotImplementedError(f"Not supported file type {ext}") - content = [ - {"type": "text", "text": message["text"]}, - { - "type": "image_url", - "image_url": { - "url": encoded_str, - } - }, - ] - else: - content = message["text"] - return content - else: - raise NotImplementedError - - -def get_interface_args(pipeline): - generation_type = gr.Dropdown( - choices=["video", "image"], - label="Generation Type", - value="video" if pipeline == "video" else "image" - ) - outputs = None - - def preprocess(message, history): - if isinstance(message, str): - return {"prompt": message} - elif isinstance(message, dict): - prompt = message.get("text", "") - additional_params = {} - - # Handle optional parameters - if message.get("aspect_ratio"): - additional_params["aspect_ratio"] = message["aspect_ratio"] - if message.get("model"): - additional_params["model"] = message["model"] - - return { - "prompt": prompt, - "additional_params": additional_params - } - - def postprocess(url, generation_type): - if generation_type == "video": - return f'' - else: - return f"![Generated Image]({url})" - - return generation_type, outputs, preprocess, postprocess - - -def get_pipeline(model_name): - # Support both video and image pipelines - return "video" if model_name == "dream-machine" else "image" - - -def registry(name: str = "dream-machine", token: str = None, **kwargs): - """ - Create a Gradio Interface for LumaAI generation. - - Parameters: - - name (str): Model name (defaults to 'dream-machine' for video, use 'photon-1' or 'photon-flash-1' for images) - - token (str, optional): The API key for LumaAI - - **kwargs: Additional keyword arguments passed to gr.Interface - """ - api_key = token or kwargs.pop('api_key', None) or os.environ.get("LUMAAI_API_KEY") - if not api_key: - raise ValueError("API key must be provided either through token parameter, kwargs, or LUMAAI_API_KEY environment variable.") - - pipeline = get_pipeline(name) - generation_type, outputs, preprocess, postprocess = get_interface_args(pipeline) - fn = get_fn(preprocess, postprocess, api_key, pipeline) - - interface = gr.ChatInterface( - fn=fn, - additional_inputs=[generation_type], - type="messages", - title="LumaAI Generation", - description="Generate videos or images from text prompts using LumaAI", - **kwargs - ) - - return interface \ No newline at end of file diff --git a/agents/promptize/src/promptize/providers/openai_gradio.py b/agents/promptize/src/promptize/providers/openai_gradio.py deleted file mode 100644 index c09d169..0000000 --- a/agents/promptize/src/promptize/providers/openai_gradio.py +++ /dev/null @@ -1,235 +0,0 @@ -import os -import asyncio -import base64 -import time -from threading import Event, Thread - -import gradio as gr -import numpy as np -from openai import OpenAI -from gradio_webrtc import ( - AdditionalOutputs, - StreamHandler, - WebRTC, - get_twilio_turn_credentials, -) -from pydub import AudioSegment - -SAMPLE_RATE = 24000 - -def encode_audio(sample_rate, data): - segment = AudioSegment( - data.tobytes(), - frame_rate=sample_rate, - sample_width=data.dtype.itemsize, - channels=1, - ) - pcm_audio = ( - segment.set_frame_rate(SAMPLE_RATE).set_channels(1).set_sample_width(2).raw_data - ) - return base64.b64encode(pcm_audio).decode("utf-8") - -class RealtimeHandler(StreamHandler): - def __init__( - self, - expected_layout="mono", - output_sample_rate=SAMPLE_RATE, - output_frame_size=480, - model=None - ) -> None: - super().__init__( - expected_layout, - output_sample_rate, - output_frame_size, - input_sample_rate=SAMPLE_RATE, - ) - self.model = model - # Initialize Event objects first - self.args_set = Event() - self.quit = Event() - self.connected = Event() - self.reset_state() - - def reset_state(self): - """Reset connection state for new recording session""" - self.connection = None - self.args_set.clear() - self.quit.clear() - self.connected.clear() - self.thread = None - self._generator = None - self.current_session = None - - def copy(self): - return RealtimeHandler( - expected_layout=self.expected_layout, - output_sample_rate=self.output_sample_rate, - output_frame_size=self.output_frame_size, - model=self.model - ) - - def _initialize_connection(self, api_key: str): - self.client = OpenAI(api_key=api_key) - with self.client.beta.realtime.connect( - model=self.model - ) as conn: - conn.session.update(session={"turn_detection": {"type": "server_vad"}}) - self.connection = conn - self.connected.set() - self.current_session = conn.session - while not self.quit.is_set(): - time.sleep(0.25) - - async def fetch_args(self): - if self.channel: - self.channel.send("tick") - - def set_args(self, args): - super().set_args(args) - self.args_set.set() - - def receive(self, frame: tuple[int, np.ndarray]) -> None: - if not self.channel: - return - try: - # Initialize connection if needed - if not self.connection: - asyncio.run_coroutine_threadsafe(self.fetch_args(), self.loop) - self.args_set.wait() - self.thread = Thread( - target=self._initialize_connection, args=(self.latest_args[-1],) - ) - self.thread.start() - self.connected.wait() - - # Send audio data - assert self.connection, "Connection not initialized" - sample_rate, array = frame - array = array.squeeze() - audio_message = encode_audio(sample_rate, array) - - # Send the audio data - self.connection.input_audio_buffer.append(audio=audio_message) - - except Exception as e: - print(f"Error in receive: {str(e)}") - import traceback - traceback.print_exc() - - def generator(self): - while True: - if not self.connection: - yield None - continue - for event in self.connection: - if event.type == "response.audio_transcript.done": - yield AdditionalOutputs(event) - if event.type == "response.audio.delta": - yield ( - self.output_sample_rate, - np.frombuffer( - base64.b64decode(event.delta), dtype=np.int16 - ).reshape(1, -1), - ) - - def emit(self) -> tuple[int, np.ndarray] | None: - if not self.connection: - return None - if not self._generator: - self._generator = self.generator() - try: - return next(self._generator) - except StopIteration: - self._generator = self.generator() - return None - - def shutdown(self) -> None: - if self.connection: - self.quit.set() - self.connection.close() - if self.thread: - self.thread.join(timeout=5) - self.reset_state() # Reset state after shutdown - -def update_chatbot(chatbot: list[dict], response): - chatbot.append({"role": "assistant", "content": response.transcript}) - return chatbot - -def get_fn(api_key: str, model: str): - # Return the function instead of executing it - def chat_fn(message, history): - client = OpenAI(api_key=api_key) - messages = [] - for user_msg, assistant_msg in history: - messages.append({"role": "user", "content": user_msg}) - messages.append({"role": "assistant", "content": assistant_msg}) - messages.append({"role": "user", "content": message}) - - response = client.chat.completions.create( - model=model, - messages=messages, - stream=True - ) - - partial_message = "" - for chunk in response: - if chunk.choices[0].delta.content: - partial_message += chunk.choices[0].delta.content - yield partial_message - - return chat_fn # Return the function object - -def registry(name: str, token: str | None = None, twilio_sid: str | None = None, twilio_token: str | None = None, enable_voice: bool = False, **kwargs): - api_key = token or os.environ.get("OPENAI_API_KEY") - if not api_key: - raise ValueError("OPENAI_API_KEY environment variable is not set.") - - model = name if name else "gpt-4" - - # Check if model is a realtime model or if enable_voice is True - is_realtime_model = "realtime" in model.lower() - use_voice_ui = enable_voice or is_realtime_model - - if use_voice_ui: - # Voice UI implementation - with gr.Blocks() as interface: - # Set initial visibility based on whether API key is provided - show_api_input = api_key is None - - with gr.Row(visible=show_api_input) as api_key_row: - api_key_input = gr.Textbox( - label="OpenAI API Key", - placeholder="Enter your OpenAI API Key", - value=api_key, - type="password", - ) - - with gr.Row(visible=not show_api_input) as row: - webrtc = WebRTC( - label="Conversation", - modality="audio", - mode="send-receive", - rtc_configuration=get_twilio_turn_credentials(twilio_sid, twilio_token), - ) - - webrtc.stream( - RealtimeHandler(model=model), - inputs=[webrtc, api_key_input], - outputs=[webrtc], - time_limit=90, - concurrency_limit=10, - ) - - api_key_input.submit( - lambda: (gr.update(visible=False), gr.update(visible=True)), - None, - [api_key_row, row], - ) - else: - # New chat interface implementation - interface = gr.ChatInterface( - fn=get_fn(api_key, model), - **kwargs - ) - - return interface \ No newline at end of file diff --git a/agents/promptize/src/promptize/providers/qwen_gradio.py b/agents/promptize/src/promptize/providers/qwen_gradio.py deleted file mode 100644 index dda18fd..0000000 --- a/agents/promptize/src/promptize/providers/qwen_gradio.py +++ /dev/null @@ -1,109 +0,0 @@ -import os -from openai import OpenAI -import gradio as gr -from typing import Callable - -__version__ = "0.0.3" - - -def get_fn(model_name: str, preprocess: Callable, postprocess: Callable, api_key: str, base_url: str | None = None): - def fn(message, history): - inputs = preprocess(message, history) - - - - client = OpenAI( - api_key=api_key, - base_url="https://dashscope.aliyuncs.com/compatible-mode/v1" - ) - completion = client.chat.completions.create( - model=model_name, - messages=inputs["messages"], - stream=True, - ) - response_text = "" - for chunk in completion: - delta = chunk.choices[0].delta.content or "" - response_text += delta - yield postprocess(response_text) - - return fn - - -def get_interface_args(pipeline, model_name: str): - if pipeline == "chat": - inputs = None - outputs = None - - def preprocess(message, history): - messages = [] - # Add system prompt for qwq-32b-preview - if model_name == "qwq-32b-preview": - messages.append({ - "role": "system", - "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step." - }) - - for user_msg, assistant_msg in history: - messages.append({"role": "user", "content": user_msg}) - messages.append({"role": "assistant", "content": assistant_msg}) - - # Handle multimodal input - if isinstance(message, dict): - content = [] - if message.get("files"): - # Convert local file path to data URL - import base64 - with open(message["files"][0], "rb") as image_file: - encoded_image = base64.b64encode(image_file.read()).decode('utf-8') - content.append({ - "type": "image_url", - "image_url": f"data:image/jpeg;base64,{encoded_image}" - }) - content.append({ - "type": "text", - "text": message["text"] - }) - messages.append({"role": "user", "content": content}) - else: - messages.append({"role": "user", "content": [{"type": "text", "text": message}]}) - - return {"messages": messages} - - postprocess = lambda x: x # No post-processing needed - else: - # Add other pipeline types when they will be needed - raise ValueError(f"Unsupported pipeline type: {pipeline}") - return inputs, outputs, preprocess, postprocess - - -def get_pipeline(model_name): - # Determine the pipeline type based on the model name - # For simplicity, assuming all models are chat models at the moment - return "chat" - - -def registry(name: str, token: str | None = None, base_url: str | None = None, **kwargs): - """ - Create a Gradio Interface for a model on OpenAI or DashScope. - - Parameters: - - name (str): The name of the model (e.g. "qwen-turbo", "gpt-3.5-turbo") - - token (str, optional): The API key - - base_url (str, optional): The base URL for the API. Defaults to DashScope URL. - """ - api_key = token or os.environ.get("DASHSCOPE_API_KEY") - if not api_key: - raise ValueError("API key not found in environment variables.") - - pipeline = get_pipeline(name) - inputs, outputs, preprocess, postprocess = get_interface_args(pipeline, name) - fn = get_fn(name, preprocess, postprocess, api_key, base_url) - - if pipeline == "chat": - interface = gr.ChatInterface(fn=fn, multimodal=True, **kwargs) - else: - # For other pipelines, create a standard Interface (not implemented yet) - interface = gr.Interface(fn=fn, inputs=inputs, outputs=outputs, **kwargs) - - return interface \ No newline at end of file diff --git a/agents/promptize/src/promptize/providers/sambanova_gradio.py b/agents/promptize/src/promptize/providers/sambanova_gradio.py deleted file mode 100644 index a8e2c56..0000000 --- a/agents/promptize/src/promptize/providers/sambanova_gradio.py +++ /dev/null @@ -1,120 +0,0 @@ -import os -import base64 -from openai import OpenAI -import gradio as gr -from typing import Callable - -__version__ = "0.0.1" - -def get_image_base64(url: str, ext: str): - with open(url, "rb") as image_file: - encoded_string = base64.b64encode(image_file.read()).decode('utf-8') - return "data:image/" + ext + ";base64," + encoded_string - -def get_fn(model_name: str, preprocess: Callable, postprocess: Callable, api_key: str): - def fn(message, history): - inputs = preprocess(message, history) - client = OpenAI( - base_url="https://api.sambanova.ai/v1/", - api_key=api_key, - ) - try: - completion = client.chat.completions.create( - model=model_name, - messages=inputs["messages"], - stream=True, - ) - response_text = "" - for chunk in completion: - delta = chunk.choices[0].delta.content or "" - response_text += delta - yield postprocess(response_text) - except Exception as e: - error_message = f"Error: {str(e)}" - return error_message - - return fn - -def handle_user_msg(message: str): - if type(message) is str: - return message - elif type(message) is dict: - if message["files"] is not None and len(message["files"]) > 0: - ext = os.path.splitext(message["files"][-1])[1].strip(".") - if ext.lower() in ["png", "jpg", "jpeg", "gif", "pdf"]: - encoded_str = get_image_base64(message["files"][-1], ext) - else: - raise NotImplementedError(f"Not supported file type {ext}") - content = [ - {"type": "text", "text": message["text"]}, - { - "type": "image_url", - "image_url": { - "url": encoded_str, - } - }, - ] - else: - content = message["text"] - return content - else: - raise NotImplementedError - -def get_interface_args(pipeline): - if pipeline == "chat": - inputs = None - outputs = None - - def preprocess(message, history): - messages = [] - files = None - for user_msg, assistant_msg in history: - if assistant_msg is not None: - messages.append({"role": "user", "content": handle_user_msg(user_msg)}) - messages.append({"role": "assistant", "content": assistant_msg}) - else: - files = user_msg - if type(message) is str and files is not None: - message = {"text":message, "files":files} - elif type(message) is dict and files is not None: - if message["files"] is None or len(message["files"]) == 0: - message["files"] = files - messages.append({"role": "user", "content": handle_user_msg(message)}) - return {"messages": messages} - - postprocess = lambda x: x # No post-processing needed - else: - # Add other pipeline types when they will be needed - raise ValueError(f"Unsupported pipeline type: {pipeline}") - return inputs, outputs, preprocess, postprocess - - -def get_pipeline(model_name): - # Determine the pipeline type based on the model name - # For simplicity, assuming all models are chat models at the moment - return "chat" - - -def registry(name: str, token: str | None = None, **kwargs): - """ - Create a Gradio Interface for a model on Sambanova. - - Parameters: - - name (str): The name of the model on Sambanova. - - token (str, optional): The API key for Sambanova. - """ - api_key = token or os.environ.get("SAMBANOVA_API_KEY") - if not api_key: - raise ValueError("SAMBANOVA_API_KEY environment variable is not set.") - - pipeline = get_pipeline(name) - inputs, outputs, preprocess, postprocess = get_interface_args(pipeline) - fn = get_fn(name, preprocess, postprocess, api_key) - - if pipeline == "chat": - interface = gr.ChatInterface(fn=fn, **kwargs) - else: - # For other pipelines, create a standard Interface (not implemented yet) - interface = gr.Interface(fn=fn, inputs=inputs, outputs=outputs, **kwargs) - - return interface \ No newline at end of file diff --git a/agents/promptize/src/promptize/providers/together_gradio.py b/agents/promptize/src/promptize/providers/together_gradio.py deleted file mode 100644 index 631dea2..0000000 --- a/agents/promptize/src/promptize/providers/together_gradio.py +++ /dev/null @@ -1,120 +0,0 @@ -import os -import base64 -from openai import OpenAI -import gradio as gr -from typing import Callable - -__version__ = "0.0.1" - -def get_image_base64(url: str, ext: str): - with open(url, "rb") as image_file: - encoded_string = base64.b64encode(image_file.read()).decode('utf-8') - return "data:image/" + ext + ";base64," + encoded_string - -def get_fn(model_name: str, preprocess: Callable, postprocess: Callable, api_key: str): - def fn(message, history): - inputs = preprocess(message, history) - client = OpenAI( - base_url="https://api.together.xyz/v1", - api_key=api_key, - ) - try: - completion = client.chat.completions.create( - model=model_name, - messages=inputs["messages"], - stream=True, - ) - response_text = "" - for chunk in completion: - delta = chunk.choices[0].delta.content or "" - response_text += delta - yield postprocess(response_text) - except Exception as e: - error_message = f"Error: {str(e)}" - return error_message - - return fn - -def handle_user_msg(message: str): - if type(message) is str: - return message - elif type(message) is dict: - if message["files"] is not None and len(message["files"]) > 0: - ext = os.path.splitext(message["files"][-1])[1].strip(".") - if ext.lower() in ["png", "jpg", "jpeg", "gif", "pdf"]: - encoded_str = get_image_base64(message["files"][-1], ext) - else: - raise NotImplementedError(f"Not supported file type {ext}") - content = [ - {"type": "text", "text": message["text"]}, - { - "type": "image_url", - "image_url": { - "url": encoded_str, - } - }, - ] - else: - content = message["text"] - return content - else: - raise NotImplementedError - -def get_interface_args(pipeline): - if pipeline == "chat": - inputs = None - outputs = None - - def preprocess(message, history): - messages = [] - files = None - for user_msg, assistant_msg in history: - if assistant_msg is not None: - messages.append({"role": "user", "content": handle_user_msg(user_msg)}) - messages.append({"role": "assistant", "content": assistant_msg}) - else: - files = user_msg - if type(message) is str and files is not None: - message = {"text":message, "files":files} - elif type(message) is dict and files is not None: - if message["files"] is None or len(message["files"]) == 0: - message["files"] = files - messages.append({"role": "user", "content": handle_user_msg(message)}) - return {"messages": messages} - - postprocess = lambda x: x # No post-processing needed - else: - # Add other pipeline types when they will be needed - raise ValueError(f"Unsupported pipeline type: {pipeline}") - return inputs, outputs, preprocess, postprocess - - -def get_pipeline(model_name): - # Determine the pipeline type based on the model name - # For simplicity, assuming all models are chat models at the moment - return "chat" - - -def registry(name: str, token: str | None = None, **kwargs): - """ - Create a Gradio Interface for a model on Together. - - Parameters: - - name (str): The name of the model on Together. - - token (str, optional): The API key for Together. - """ - api_key = token or os.environ.get("TOGETHER_API_KEY") - if not api_key: - raise ValueError("TOGETHER_API_KEY environment variable is not set.") - - pipeline = get_pipeline(name) - inputs, outputs, preprocess, postprocess = get_interface_args(pipeline) - fn = get_fn(name, preprocess, postprocess, api_key) - - if pipeline == "chat": - interface = gr.ChatInterface(fn=fn, **kwargs) - else: - # For other pipelines, create a standard Interface (not implemented yet) - interface = gr.Interface(fn=fn, inputs=inputs, outputs=outputs, **kwargs) - - return interface \ No newline at end of file diff --git a/agents/promptize/src/promptize/providers/xai_gradio.py b/agents/promptize/src/promptize/providers/xai_gradio.py deleted file mode 100644 index 667975e..0000000 --- a/agents/promptize/src/promptize/providers/xai_gradio.py +++ /dev/null @@ -1,105 +0,0 @@ -import os -import base64 -from openai import OpenAI -import gradio as gr -from typing import Callable - -__version__ = "0.0.1" - -def get_image_base64(url: str, ext: str): - with open(url, "rb") as image_file: - encoded_string = base64.b64encode(image_file.read()).decode('utf-8') - return "data:image/" + ext + ";base64," + encoded_string - -def get_fn(model_name: str, preprocess: Callable, postprocess: Callable, api_key: str): - def fn(message, history): - inputs = preprocess(message, history) - client = OpenAI( - api_key=api_key, - base_url="https://api.x.ai/v1", - ) - completion = client.chat.completions.create( - model=model_name, - messages=inputs["messages"], - stream=True, - ) - response_text = "" - for chunk in completion: - delta = chunk.choices[0].delta.content or "" - response_text += delta - yield postprocess(response_text) - - return fn - -def handle_user_msg(message: str): - if type(message) is str: - return message - elif type(message) is dict: - if message["files"] is not None and len(message["files"]) > 0: - ext = os.path.splitext(message["files"][-1])[1].strip(".") - if ext.lower() in ["png", "jpg", "jpeg", "gif"]: - encoded_str = get_image_base64(message["files"][-1], ext) - else: - raise NotImplementedError(f"Not supported file type {ext}") - content = [ - {"type": "text", "text": message["text"]}, - { - "type": "image_url", - "image_url": { - "url": encoded_str, - } - }, - ] - else: - content = message["text"] - return content - else: - raise NotImplementedError - -def get_interface_args(model_name: str): - inputs = None - outputs = None - - def preprocess(message, history): - messages = [{"role": "system", "content": "You are Grok, a chatbot inspired by the Hitchhikers Guide to the Galaxy."}] - files = None - for user_msg, assistant_msg in history: - if assistant_msg is not None: - messages.append({"role": "user", "content": handle_user_msg(user_msg)}) - messages.append({"role": "assistant", "content": assistant_msg}) - else: - files = user_msg - - if type(message) is str and files is not None: - message = {"text": message, "files": files} - elif type(message) is dict and files is not None: - if message["files"] is None or len(message["files"]) == 0: - message["files"] = files - - messages.append({"role": "user", "content": handle_user_msg(message)}) - return {"messages": messages} - - postprocess = lambda x: x # No post-processing needed - return inputs, outputs, preprocess, postprocess - -def registry(name: str = "grok-beta", token: str | None = None, **kwargs): - """ - Create a Gradio Interface for X.AI's Grok model. - - Parameters: - - name (str): The name of the model (defaults to "grok-beta" or "grok-vision-beta") - - token (str, optional): The X.AI API key - """ - api_key = token or os.environ.get("XAI_API_KEY") - if not api_key: - raise ValueError("XAI_API_KEY environment variable is not set.") - - inputs, outputs, preprocess, postprocess = get_interface_args(name) - fn = get_fn(name, preprocess, postprocess, api_key) - - # Always set multimodal=True - kwargs["multimodal"] = True - - interface = gr.ChatInterface(fn=fn, **kwargs) - - return interface \ No newline at end of file diff --git a/appkits/qpyapp/src/qpycases/auto_cut/__init__.py b/appkits/qpyapp/src/qpycases/auto_cut/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/datafarm/primary/README.md b/datafarm/primary/README.md index 52fbec4..1e61778 100644 --- a/datafarm/primary/README.md +++ b/datafarm/primary/README.md @@ -1,37 +1,5 @@ -## README -安装：使用python uv 安装所有依赖 -```shell -uv sync -``` -配置： -1. setting.toml 文件：REWORKSPACE 这个配置到本地目录 -2. 代码运行主要在： [test_standard_process.py](tests%2Floc_gov_bonds%2Ftest_standard_process.py) 这个文件 +# README -## 代码运行步骤: +## Tools to use -1. ```test_download_all_bonds``` 这个函数用来下载pdf文件 -修改年份范围和省份范围，可以指定某个省某个年份范围下载PDF,下载完成之后的文件目录结构：{REWORKSPACE}/<省份>/<年份> -![download-pdf.png](download-pdf.png) - -2```test_unzip_all_files``` 这个用来unzip下载的所有zip包里面的文件到指定目录all_files目录,修改省份和年份就可以 -![img_1.png](img_1.png) - -3```test_import_project_detail_data``` 用来将csv数据数据清洗，结构化，导入数据句，修改年份和省份， -从 **<年份>-<省份名>.csv** 文件中读取数据，清洗结果的文件是 ***cleanup-<年份>-<省份名>.csv*** -![img_1.png](img_1.png) -![img_2.png](img_2.png) - -4.```test_cleanup_import`` 直接从清洗过的文件中导入数据库,使用基本同第三步骤，主要发现有些原始文件保存数据库出错，然后修改 -一下清洗文件之后，直接导入 - -## 数据从哪里来 - -pdf文件下载完成之后, -1. 通过***WPS*** 的 ***PDF转换成EXCEL功能*** 批量转换PDF成EXCEL文件 -2. 从EXCEL文件中获取到相关的债券信息表格，复制到<年份>-<省份名>.csv 中间中 -3. 复制过程中尽可能的对齐数据，有偏差问题不大，但是取的值的列数需要对上，比如 -![img_3.png](img_3.png) -1，2，3，4是需要的数据，每行不对齐没有问题， -但是每行有数据的1，2，3，4顺序的值需要是正确对应的，中间有多少空行是可以在清洗时候处理的 -具体就是每行的：第1有数字的列代表了债券名称，第2个有值的列代表了项目名称，第4个有值的列代表了发行金额， -1和2，3和4中间有空列，每行都有不一样数量的空列都问题不大可以清洗 +- https://seleniumbase.io/ \ No newline at end of file diff --git a/datafarm/primary/pyproject.toml b/datafarm/primary/pyproject.toml index eb87a93..0a6848f 100644 --- a/datafarm/primary/pyproject.toml +++ b/datafarm/primary/pyproject.toml @@ -14,6 +14,7 @@ dependencies = [ "qpydao", "requests>=2.32.3", "rich>=13.9.4", + "seleniumbase>=4.33.11", "weasyprint>=63.1", ] diff --git a/datafarm/primary/src/primary/adspower/README.md b/datafarm/primary/src/primary/adspower/README.md index e69de29..178c201 100644 --- a/datafarm/primary/src/primary/adspower/README.md +++ b/datafarm/primary/src/primary/adspower/README.md @@ -0,0 +1,201 @@ +# adspower + +


 + +[![Python versions](https://img.shields.io/pypi/pyversions/adspower?color=%231D4DFF)](https://pypi.org/project/adspower/) +[![PyPi Version](https://img.shields.io/pypi/v/adspower?color=%231D4DFF)](https://pypi.org/project/adspower/) + + +The package for interacting with API of anti-detect browser [AdsPower](https://www.adspower.com). + +- **[Overview](#quick-start)** +- **[Installing](#installing-adspower)** +- **[Bug reports](https://github.com/CrocoFactory/adspower/issues)** + +The project is made by the **[Croco Factory](https://github.com/CrocoFactory)** team + +adspower's source code is made available under the [MIT License](LICENSE) + +## Features +- Synchronous and asynchronous interaction with the local API +- Interaction with the most popular libraries for browser automation in Python: Selenium and Playwright + +## Restrictions +1. During using the package, AdsPower must be opened. +2. The local API is available only in paid AdsPower subscriptions +3. AdsPower has frequency control for all APIs, max. access frequency: 1 request/second + + +## Quick start + +*Example of interacting with synchronous API.* + +```python +from adspower.sync_api import Group, ProfileAPI +group = Group.create(name='my_group', remark='The best group ever') + +profile_api = ProfileAPI.create(group=group) +print(f'Profile {profile_api.name} was created in group {group.name}') +``` + +**Use `ProfileAPI` only when** you don't need `Selenium` and `Playwright` interactions. + +Library provides ways to interact the most popular libraries for browser automation in Python: `Selenium` and `Playwright`. +To get a browser, you can use `with` statement: + +- *Selenium* + +```python +from adspower.sync_api.selenium import Profile, Group +my_group = Group.query(name='my_group')[0] +profile = Profile.create(group=my_group, name='my_profile') + +with profile as browser: + browser.get('https://github.com/blnkoff/adspower') +``` + +- *Playwright* + +```python +from adspower.async_api.playwright import Profile, Group + +async def main() -> None: + my_group = (await Group.query(name='my_group'))[0] + profile = await Profile.create(group=my_group, name='my_profile') + + async with profile as browser: + page = browser.pages[0] + await page.goto('https://github.com/blnkoff/adspower') +``` + +Both versions support sync and async API. + +Or manually call `get_browser` if you need specify part of behaviour. +```python +from adspower.sync_api.selenium import Profile, Group + +my_group = Group.query(name='my_group')[0] +profile = Profile.create(group=my_group, name='my_profile') +browser = profile.get_browser(ip_tab=False, headless=True, disable_password_filling=True) +browser.get('https://github.com/blnkoff/adspower') +profile.quit() +``` + +Notice that you must not call quitting methods of `Playwright` library or `Selenium` after `profile.quit()`, since +it calls these methods automatically. An attempt to do so will lead to the error. + +*Example of setting proxy and fingerprint* + +```python +from adspower.sync_api.playwright import Profile, Group +from adspower import ProxyConfig, FingerprintConfig + +proxy = ProxyConfig( + soft='other', + type='http', + host='xx.xx.x.xx', + port=1000, + user='username', + password='password' +) + +fingerprint = FingerprintConfig( + ua='Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.6261.112 Safari/537.36' +) + +group = Group.query(name='my_group')[0] +profile = Profile.create(group=group, proxy_config=proxy, name='my_profile', fingerprint_config=fingerprint) +``` + +There are extension categories, implemented as `Category` class. At the moment, it can`t be created, but can be retrieved. +You can manually create extension category and used it for profile creation using API. + +*Example of querying category* + +```python +from adspower.sync_api.playwright import Profile, Category, Group + +category = Category.query(name='my_category')[0] +group = Group.query(name='my_group')[0] + +profile = Profile.create(group=group, category=category) +``` + +You can create anonymous profile that is deleted after last statement in context manager. + +*Example of anonymous profile* +```python +from adspower.async_api.playwright import Profile, Group + +async def main() -> None: + my_group = (await Group.query(name='my_group'))[0] + profile = await Profile.anonymous(group=my_group) + + async with profile as browser: + page = browser.pages[0] + await page.goto('https://www.google.com') +``` + +Each API entity, such as Profile, Group and Category, pretty formatted, can be compared and converted to dict + +*Example 1* + +```python +from adspower.sync_api.playwright import Category + +category = Category.query(name='my_category')[0] +print(category) +``` + +```markdown +Category(id=10515; name=my_category) +``` + +*Example 2* + +```python +from adspower.sync_api.playwright import Profile, Group + +group = Group.query(name='my_group')[0] +profile_created = Profile.create(group=group) + +profile_queried = Profile.query(id_=profile_created.id) +print(profile_queried == profile_created) +``` + +```python +True +``` + +*Example 3* +```python +from adspower.sync_api.playwright import Category + +category = Category.query(name='my_category')[0] +print(category.to_dict()) +``` + +```json +{ + "id": 10515, + "name": "my_category", + "remark": "category remark" +} +``` + +# Installing adspower +To install the package from PyPi you can use that: + +```sh +pip install adspower +``` + +You will probably want to use the pacakge with `Selenium` or `Playwright`. You can install it as extra-package: + +```sh +pip install adspower[playwright] +``` + +```sh +pip install adspower[selenium] +``` diff --git a/datafarm/primary/src/primary/adspower/_api_entity.py b/datafarm/primary/src/primary/adspower/_api_entity.py index e8dc142..b13a6bd 100644 --- a/datafarm/primary/src/primary/adspower/_api_entity.py +++ b/datafarm/primary/src/primary/adspower/_api_entity.py @@ -1,3 +1,5 @@ +from __future__ import annotations + from abc import ABC, abstractmethod from typing import Mapping, MutableMapping diff --git a/datafarm/primary/src/primary/adspower/_base_category.py b/datafarm/primary/src/primary/adspower/_base_category.py index 94c1108..c51d151 100644 --- a/datafarm/primary/src/primary/adspower/_base_category.py +++ b/datafarm/primary/src/primary/adspower/_base_category.py @@ -1,6 +1,11 @@ +from __future__ import annotations + from abc import ABC, abstractmethod from typing import Self, Optional, Any +from primary.adspower._api_entity import _APIEntity +from primary.adspower.types import CategoryInfo + class _BaseCategory(_APIEntity, ABC): diff --git a/datafarm/primary/src/primary/adspower/_base_group.py b/datafarm/primary/src/primary/adspower/_base_group.py index 5810d86..6482b05 100644 --- a/datafarm/primary/src/primary/adspower/_base_group.py +++ b/datafarm/primary/src/primary/adspower/_base_group.py @@ -1,10 +1,10 @@ from abc import ABC, abstractmethod from typing import Optional, Any from typing import Self -from adspower.types import GroupInfo -from adspower._api_entity import _APIEntity -from adspower.types import HandlingTuple -from adspower.utils import _convert_json +from primary.adspower.types import GroupInfo +from primary.adspower._api_entity import _APIEntity +from primary.adspower.types import HandlingTuple +from primary.adspower.utils import _convert_json class _BaseGroup(_APIEntity, ABC): diff --git a/datafarm/primary/src/primary/adspower/_base_http_client.py b/datafarm/primary/src/primary/adspower/_base_http_client.py index 49d1229..7f57fda 100644 --- a/datafarm/primary/src/primary/adspower/_base_http_client.py +++ b/datafarm/primary/src/primary/adspower/_base_http_client.py @@ -6,7 +6,7 @@ from httpx._types import (URLTypes, RequestContent, RequestData, RequestFiles, QueryParamTypes, HeaderTypes, CookieTypes, AuthTypes, TimeoutTypes, RequestExtensions) -from adspower.exceptions import InvalidPortError, InternalAPIError, ExceededQPSError, APIRefusedError, ZeroResponseError +from primary.adspower.exceptions import InvalidPortError, InternalAPIError, ExceededQPSError, APIRefusedError, ZeroResponseError class _BaseHTTPClient(ABC): diff --git a/datafarm/primary/src/primary/adspower/_base_profile_api.py b/datafarm/primary/src/primary/adspower/_base_profile_api.py index 5662b37..f980dfd 100644 --- a/datafarm/primary/src/primary/adspower/_base_profile_api.py +++ b/datafarm/primary/src/primary/adspower/_base_profile_api.py @@ -1,11 +1,11 @@ from datetime import datetime from typing import Optional, Self, Any from abc import ABC, abstractmethod -from adspower._api_entity import _APIEntity +from primary.adspower._api_entity import _APIEntity from ._base_group import _BaseGroup as Group from ._base_category import _BaseCategory as Category -from adspower.types import (ProxyConfig, Cookies, FingerprintConfig, RepeatConfigType, ProfileInfo, - UserSort, IpChecker, HandlingTuple) +from primary.adspower.types import (ProxyConfig, Cookies, FingerprintConfig, RepeatConfigType, ProfileInfo, + UserSort, IpChecker, HandlingTuple) from .exceptions import InvalidProxyConfig diff --git a/datafarm/primary/src/primary/adspower/async_api/_base_profile.py b/datafarm/primary/src/primary/adspower/async_api/_base_profile.py index 6a4c10c..0305367 100644 --- a/datafarm/primary/src/primary/adspower/async_api/_base_profile.py +++ b/datafarm/primary/src/primary/adspower/async_api/_base_profile.py @@ -1,6 +1,6 @@ from abc import ABC, abstractmethod from typing import Optional, AsyncContextManager -from adspower.async_api import ProfileAPI, Group +from primary.adspower.async_api import ProfileAPI, Group from contextlib import asynccontextmanager diff --git a/datafarm/primary/src/primary/adspower/async_api/category.py b/datafarm/primary/src/primary/adspower/async_api/category.py index 746110a..6e552dd 100644 --- a/datafarm/primary/src/primary/adspower/async_api/category.py +++ b/datafarm/primary/src/primary/adspower/async_api/category.py @@ -1,5 +1,5 @@ from typing import Self, Optional, ClassVar -from adspower._base_category import _BaseCategory +from primary.adspower._base_category import _BaseCategory from .http_client import HTTPClient diff --git a/datafarm/primary/src/primary/adspower/async_api/group.py b/datafarm/primary/src/primary/adspower/async_api/group.py index eb0267f..fc33646 100644 --- a/datafarm/primary/src/primary/adspower/async_api/group.py +++ b/datafarm/primary/src/primary/adspower/async_api/group.py @@ -1,6 +1,6 @@ from typing import ClassVar, Optional from typing import Self -from adspower._base_group import _BaseGroup +from primary.adspower._base_group import _BaseGroup from .http_client import HTTPClient diff --git a/datafarm/primary/src/primary/adspower/async_api/http_client.py b/datafarm/primary/src/primary/adspower/async_api/http_client.py index 26cf271..954dd90 100644 --- a/datafarm/primary/src/primary/adspower/async_api/http_client.py +++ b/datafarm/primary/src/primary/adspower/async_api/http_client.py @@ -8,8 +8,8 @@ CookieTypes, AuthTypes, TimeoutTypes, RequestExtensions) from urllib3.exceptions import MaxRetryError, NewConnectionError -from adspower.exceptions import UnavailableAPIError -from adspower._base_http_client import _BaseHTTPClient +from primary.adspower.exceptions import UnavailableAPIError +from primary.adspower._base_http_client import _BaseHTTPClient class HTTPClient(AsyncClient, _BaseHTTPClient): diff --git a/datafarm/primary/src/primary/adspower/async_api/playwright/profile.py b/datafarm/primary/src/primary/adspower/async_api/playwright/profile.py index fa9bddc..1fd13f8 100644 --- a/datafarm/primary/src/primary/adspower/async_api/playwright/profile.py +++ b/datafarm/primary/src/primary/adspower/async_api/playwright/profile.py @@ -1,10 +1,10 @@ from datetime import datetime from typing import Optional, AsyncContextManager -from adspower.async_api._base_profile import _BaseProfile -from adspower.async_api.group import Group -from adspower.async_api.category import Category +from primary.adspower.async_api._base_profile import _BaseProfile +from primary.adspower.async_api.group import Group +from primary.adspower.async_api.category import Category from playwright.async_api import async_playwright, BrowserContext, Playwright, Browser -from adspower.types import IpChecker +from primary.adspower.types import IpChecker class Profile(_BaseProfile): diff --git a/datafarm/primary/src/primary/adspower/async_api/profile_api.py b/datafarm/primary/src/primary/adspower/async_api/profile_api.py index a00d23f..7202fc3 100644 --- a/datafarm/primary/src/primary/adspower/async_api/profile_api.py +++ b/datafarm/primary/src/primary/adspower/async_api/profile_api.py @@ -4,10 +4,10 @@ from .category import Category from .http_client import HTTPClient from .group import Group -from adspower._base_profile_api import _BaseProfileAPI -from adspower.types import (ProxyConfig, Cookies, FingerprintConfig, RepeatConfigType, BrowserResponse, +from primary.adspower._base_profile_api import _BaseProfileAPI +from primary.adspower.types import (ProxyConfig, Cookies, FingerprintConfig, RepeatConfigType, BrowserResponse, UserSort, IpChecker) -from adspower.utils import _convert_json +from primary.adspower.utils import _convert_json class ProfileAPI(_BaseProfileAPI, ABC): diff --git a/datafarm/primary/src/primary/adspower/exceptions.py b/datafarm/primary/src/primary/adspower/exceptions.py index b350ac9..614e1b2 100644 --- a/datafarm/primary/src/primary/adspower/exceptions.py +++ b/datafarm/primary/src/primary/adspower/exceptions.py @@ -1,7 +1,7 @@ from typing import Any from httpx import ConnectError, RequestError, Request -from adspower import ProxyConfig +from primary.adspower import ProxyConfig class ZeroResponseError(RequestError): diff --git a/datafarm/primary/src/primary/adspower/sync_api/_base_profile.py b/datafarm/primary/src/primary/adspower/sync_api/_base_profile.py index b9f783d..4128749 100644 --- a/datafarm/primary/src/primary/adspower/sync_api/_base_profile.py +++ b/datafarm/primary/src/primary/adspower/sync_api/_base_profile.py @@ -1,7 +1,7 @@ from abc import ABC, abstractmethod from contextlib import contextmanager from typing import Optional, ContextManager -from adspower.sync_api import ProfileAPI, Group +from primary.adspower.sync_api import ProfileAPI, Group class _BaseProfile(ProfileAPI, ABC): diff --git a/datafarm/primary/src/primary/adspower/sync_api/category.py b/datafarm/primary/src/primary/adspower/sync_api/category.py index aaa23c2..7ca94fa 100644 --- a/datafarm/primary/src/primary/adspower/sync_api/category.py +++ b/datafarm/primary/src/primary/adspower/sync_api/category.py @@ -1,5 +1,5 @@ from typing import Self, Optional, ClassVar -from adspower._base_category import _BaseCategory +from primary.adspower._base_category import _BaseCategory from .http_client import HTTPClient diff --git a/datafarm/primary/src/primary/adspower/sync_api/group.py b/datafarm/primary/src/primary/adspower/sync_api/group.py index c535ea9..857e41b 100644 --- a/datafarm/primary/src/primary/adspower/sync_api/group.py +++ b/datafarm/primary/src/primary/adspower/sync_api/group.py @@ -1,6 +1,6 @@ from typing import ClassVar, Optional from typing import Self -from adspower._base_group import _BaseGroup +from primary.adspower._base_group import _BaseGroup from .http_client import HTTPClient diff --git a/datafarm/primary/src/primary/adspower/sync_api/http_client.py b/datafarm/primary/src/primary/adspower/sync_api/http_client.py index 3c6d292..e81b5ed 100644 --- a/datafarm/primary/src/primary/adspower/sync_api/http_client.py +++ b/datafarm/primary/src/primary/adspower/sync_api/http_client.py @@ -7,8 +7,8 @@ CookieTypes, AuthTypes, TimeoutTypes, RequestExtensions) from urllib3.exceptions import MaxRetryError, NewConnectionError -from adspower.exceptions import UnavailableAPIError -from adspower._base_http_client import _BaseHTTPClient +from primary.adspower.exceptions import UnavailableAPIError +from primary.adspower._base_http_client import _BaseHTTPClient class HTTPClient(Client, _BaseHTTPClient): diff --git a/datafarm/primary/src/primary/adspower/sync_api/playwright/profile.py b/datafarm/primary/src/primary/adspower/sync_api/playwright/profile.py index c59841c..3d20c6c 100644 --- a/datafarm/primary/src/primary/adspower/sync_api/playwright/profile.py +++ b/datafarm/primary/src/primary/adspower/sync_api/playwright/profile.py @@ -1,8 +1,8 @@ from datetime import datetime from typing import Optional, ContextManager -from adspower.sync_api._base_profile import _BaseProfile -from adspower.sync_api.group import Group -from adspower.sync_api.category import Category +from primary.adspower.sync_api._base_profile import _BaseProfile +from primary.adspower.sync_api.group import Group +from primary.adspower.sync_api.category import Category from playwright.sync_api import sync_playwright, BrowserContext, Playwright, Browser from adspower.types import IpChecker diff --git a/datafarm/primary/src/primary/adspower/sync_api/profile_api.py b/datafarm/primary/src/primary/adspower/sync_api/profile_api.py index 6cd7de7..2716dcf 100644 --- a/datafarm/primary/src/primary/adspower/sync_api/profile_api.py +++ b/datafarm/primary/src/primary/adspower/sync_api/profile_api.py @@ -3,8 +3,8 @@ from .category import Category from .http_client import HTTPClient from .group import Group -from adspower._base_profile_api import _BaseProfileAPI -from adspower.types import (ProxyConfig, Cookies, FingerprintConfig, RepeatConfigType, BrowserResponse, +from primary.adspower._base_profile_api import _BaseProfileAPI +from primary.adspower.types import (ProxyConfig, Cookies, FingerprintConfig, RepeatConfigType, BrowserResponse, UserSort, IpChecker) from ..utils import _convert_json diff --git a/datafarm/primary/src/primary/adspower/sync_api/selenium/profile.py b/datafarm/primary/src/primary/adspower/sync_api/selenium/profile.py index 56076d2..1be1373 100644 --- a/datafarm/primary/src/primary/adspower/sync_api/selenium/profile.py +++ b/datafarm/primary/src/primary/adspower/sync_api/selenium/profile.py @@ -3,10 +3,10 @@ from selenium.webdriver.chrome.options import Options from selenium.webdriver.chrome.service import Service from selenium.webdriver.chrome.webdriver import WebDriver -from adspower.sync_api._base_profile import _BaseProfile -from adspower.sync_api.group import Group -from adspower.sync_api.category import Category -from adspower.types import IpChecker +from primary.adspower.sync_api._base_profile import _BaseProfile +from primary.adspower.sync_api.group import Group +from primary.adspower.sync_api.category import Category +from primary.adspower.types import IpChecker class Profile(_BaseProfile): diff --git a/datafarm/primary/src/primary/adspower/types.py b/datafarm/primary/src/primary/adspower/types.py index 905b6dc..c321ca0 100644 --- a/datafarm/primary/src/primary/adspower/types.py +++ b/datafarm/primary/src/primary/adspower/types.py @@ -1,6 +1,6 @@ -from typing import TypedDict, Literal, NotRequired, Any, Callable +from typing import TypedDict, Literal, NotRequired, Any, Callable, Union -HandlingTuple = tuple[dict[str, dict | str], Callable[[dict[str, Any]], Any] | Callable[[], Any]] +HandlingTuple = tuple[dict[str, Union[dict, str]], Union[Callable[[dict[str, Any]], Any], Callable[[], Any]]] ProxySoft = Literal['luminati', 'lumauto', 'oxylabsatuto', '922S5', 'ipideaauto', 'ipfoxyauto', 'ssh', 'other', 'no_proxy'] @@ -14,7 +14,7 @@ MediaDeviceType = Literal[0, 1, 2] GPUType = Literal[0, 1, 2] WebGLVersion = Literal[0, 2, 3] -Cookies = list[dict[str, Any]] | dict[str, Any] +Cookies = Union[list[dict[str, Any]], dict[str, Any]] IntBool = Literal[0, 1] UserSortKey = Literal['serial_number', 'last_open_time', 'created_time'] diff --git a/pyproject.toml b/pyproject.toml index 8afce2b..6f96aa9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ name = "pyway" version = "0.1.0" description = "Personal Python Collections" readme = "README.md" -requires-python = ">=3.10" +requires-python = ">=3.10, <3.13" dependencies = [] [project.scripts] diff --git a/uv.lock b/uv.lock index 008f9a7..4151af4 100644 --- a/uv.lock +++ b/uv.lock @@ -1,21 +1,45 @@ version = 1 -requires-python = ">=3.10" +requires-python = ">=3.10, <3.13" resolution-markers = [ - "python_full_version < '3.11'", - 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