🦝 PedroReports

Your Pawsome Reporter 🐾

What is PedroReports?

Watch the Video Tutorial by clicking on Thumbnail below !!

PedroReports is your clever companion for data analysis, transforming raw datasets into polished insights through the power of Google's Gemini AI. With a curious eye and quick thinking, it turns your CSV datasets into comprehensive, professional PDF reports with minimal effort. Whether you're analyzing financial trends, healthcare metrics, marketing data, or any other tabular dataset, PedroReports is ready to dig through your data and uncover hidden treasures.

Simply upload your data, ask your questions naturally, and let PedroReports:

• 🤖 Process your data using advanced AI analysis
• 📊 Create beautiful, informative visualizations
• 📝 Perform detailed statistical analysis
• 📑 Generate polished PDF reports automatically
• 💡 Deliver AI-powered insights and recommendations

Industry Applications

PedroReports excels across various domains:

• 📈 Finance: Analyze market trends, investment portfolios, and financial statements
• 🏥 Healthcare: Process patient data, treatment outcomes, and clinical trials
• 📊 Business Analytics: Track KPIs, sales metrics, and customer behavior
• 📱 Marketing: Evaluate campaign performance and customer engagement
• 🔬 Research: Analyze experimental data and survey responses
• 📦 Supply Chain: Monitor inventory levels and logistics metrics

Features

• 📊 Automated data analysis and visualization
• 📝 AI-powered insights generation using Google's Gemini-1.5-flash model
• 📈 Interactive data visualizations with Matplotlib & Seaborn
• 📱 Responsive and modern UI with Tailwind CSS
• 🎨 Light/Dark mode support
• 🔒 Secure file handling and validation
• 📄 Professional PDF report generation
• 🚀 Real-time processing status updates

Tech Stack

Frontend

• ⚛️ React with Vite for blazing-fast development
• 🎨 Tailwind CSS for styling
• 🌓 Dark/Light mode support
• 🔧 Modern React Hooks and best practices

Backend

• ⚡ FastAPI for high-performance API
• 🤖 Google Gemini AI for intelligent analysis
• 🔗 Langchain for AI orchestration and prompt management
• 📊 Pandas & NumPy for data processing
• 📈 Matplotlib & Seaborn for visualization
• 📑 ReportLab for PDF generation

Getting Started

Prerequisites

• Node.js 16+ 📦
• Python 3.11+ 🐍
• pip 🔧
• Virtual environment (recommended) 🌐

Installation & Setup

1. Clone the repository

git clone https://github.com/bobinsingh/PedroReports.git
cd pedroreports

2. Backend Setup

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# For Linux
conda create -n pedrotool python=3.11
conda activate pedrotool

# Install dependencies
pip install -r requirements.txt

# Rename .env.example to .env and add your API key

3. Frontend Setup

cd frontend
npm install

4. Start Development Servers

Backend:

# Navigate to backend directory
cd backend

# From root directory
uvicorn main:app --reload --port 8000

Frontend:

# Navigate to frontend directory
cd frontend

# From frontend directory
npm run dev

Visit http://localhost:5173 to access the application 🌐

Features in Detail

Data Analysis

• 📊 Automated statistical analysis
• 📈 Trend detection and pattern recognition
• 🔍 Outlier detection
• 📉 Correlation analysis
• 📊 Distribution analysis

Visualization

• 📊 Dynamic chart generation
• 📈 Interactive data exploration
• 🎨 Customizable visual themes
• 📱 Responsive design
• 📊 Multiple chart types support

Report Generation

• 📄 Professional PDF reports
• 📝 AI-generated insights
• 📊 Embedded visualizations
• 📑 Executive summaries
• 🔍 Detailed analysis sections

Project Structure

project/
├── backend/           # FastAPI backend
│   ├── api/          # API endpoints
│   ├── core/         # Core functionality
│   ├── services/     # Key Components
│   └── domain/       # Domain models
│
└── frontend/         # React frontend
    ├── src/
    │   ├── components/   # React components
    │   ├── styles/       # CSS styles
    │   └── App.jsx      # Main application
    └── public/          # Static assets

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the BSD 3-Clause License - see the LICENSE file for details.

Acknowledgments

• FastAPI for the amazing backend framework
• React team for the frontend library
• Google for the Gemini AI model
• Langchain for AI orchestration capabilities
• All contributors and supporters

---

Crafted with 🦝 by Bobin Singh