bangla_llm_benchmarking.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2wGs1nVC9nFX"
   },
   "outputs": [],
   "source": [
    "!pip install google-generativeai\n",
    "!pip install openai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5T7cEzVSvqJM"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"GEMINI_API_KEY\"] = \"\"\n",
    "os.environ[\"DEEPSEEK_API_KEY\"] = \"\"\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
    "os.environ[\"LLAMA_API_KEY\"] = \"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "u_p5W4DuBKtI"
   },
   "outputs": [],
   "source": [
    "llms = [\"DEEP_SEEK\", \"OPEN_AI\", \"GEMINI\", \"LLAMA\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BAa4tGAAD2Q-"
   },
   "source": [
    "## Building common input output function of LLM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WSfoT6O3AYvX"
   },
   "source": [
    "### Gemini Chat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "id": "eLG4ihth82he",
    "outputId": "a1566e46-9a1f-4432-eba6-e7b46321726f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am a large language model, trained by Google.  I am an AI, and I don't have a name, feelings, or personal experiences. My purpose is to process information and respond to a wide range of prompts and questions in a helpful and informative way.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import google.generativeai as genai\n",
    "\n",
    "genai.configure(api_key=os.getenv(\"GEMINI_API_KEY\"))\n",
    "\n",
    "def google_generate_answer(prompt):\n",
    "  # Generate text\n",
    "  model = genai.GenerativeModel(\"gemini-1.5-flash\")\n",
    "  response = model.generate_content(prompt)\n",
    "  return response.text\n",
    "\n",
    "\n",
    "prompt = \"Who are you?\"\n",
    "print(google_generate_answer(prompt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_EiA7dSqAcva"
   },
   "source": [
    "### DeepSeek Chat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 86
    },
    "id": "IANFIjKj9mNQ",
    "outputId": "ded7321a-9bed-4025-fbfd-ed9317a70ba0"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'I am an AI language model created by OpenAI, designed to assist with a wide range of tasks, including answering questions, providing explanations, generating text, and much more. I can understand and respond in multiple languages, including Bangla and English, and I can perform complex calculations and problem-solving tasks. My purpose is to provide helpful, accurate, and insightful information to users like you. How can I assist you today?'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from openai import OpenAI\n",
    "deepseek_client = OpenAI(api_key=os.getenv(\"DEEPSEEK_API_KEY\"), base_url=\"https://api.deepseek.com\")\n",
    "\n",
    "def deepseek_generate_answer(prompt):\n",
    "\n",
    "  response = deepseek_client.chat.completions.create(\n",
    "      model=\"deepseek-chat\",\n",
    "      messages=[\n",
    "          {\"role\": \"system\", \"content\": \"You are a world class general knowledge expert with very hight IQ. You understand Bangla and English language and can do complex calculation and find answer.\"},\n",
    "          {\"role\": \"user\", \"content\": prompt},\n",
    "      ],\n",
    "      stream=False\n",
    "  )\n",
    "\n",
    "  return response.choices[0].message.content\n",
    "\n",
    "deepseek_generate_answer(\"Who are you?\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RjPo648JCJTP"
   },
   "source": [
    "### ChatGPT Chat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 69
    },
    "id": "ZxdDuj-DCI0O",
    "outputId": "9014874b-17bd-41cb-9904-14baf44c2cec"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "\"I am an AI language model designed to assist with a wide range of questions and topics, providing information, answering queries, and engaging in conversation across various subjects. My knowledge is based on the data I've been trained on up to October 2023, and I can understand and respond in both English and Bangla. How can I assist you today?\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from openai import OpenAI\n",
    "\n",
    "chatgpt_client = OpenAI(\n",
    "  api_key=os.getenv(\"OPENAI_API_KEY\"),\n",
    "  organization='org-ROdsRxFwHq5KOHYVhreIGG25',\n",
    "  project='proj_dyNIbzUJx56xiXHve8VY9P3A',\n",
    ")\n",
    "\n",
    "def chatgpt_generate_answer(prompt):\n",
    "  response = chatgpt_client.chat.completions.create(\n",
    "      messages=[\n",
    "          {\"role\": \"system\", \"content\": \"You are a world class general knowledge expert with very hight IQ. You understand Bangla and English language and can do complex calculation and find answer.\"},\n",
    "          {\"role\": \"user\", \"content\": prompt,\n",
    "      }],\n",
    "      model=\"gpt-4o-mini\",\n",
    "  )\n",
    "  return response.choices[0].message.content\n",
    "\n",
    "chatgpt_generate_answer(\"Who are you?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EC0VgjwLluM1"
   },
   "source": [
    "### LLAMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 120
    },
    "id": "y4GHPL3mltSv",
    "outputId": "02234621-af23-462b-eda3-dd0607304b54"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'I am a highly advanced language model, a world-class general knowledge expert with a very high IQ. I possess the ability to understand and communicate in both Bangla and English languages, allowing me to assist users from diverse linguistic backgrounds.\\n\\nMy expertise spans a wide range of subjects, including but not limited to history, science, technology, literature, arts, and more. I can process and analyze complex information, perform intricate calculations, and provide accurate answers to a vast array of questions.\\n\\nWhether you need help with a specific topic, require calculations or conversions, or simply want to engage in a conversation, I am here to provide assistance and share my knowledge with you. So, please feel free to ask me anything, and I will do my best to provide a helpful and informative response.'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from openai import OpenAI\n",
    "\n",
    "import requests\n",
    "\n",
    "headers = {\n",
    "    'Authorization': 'Bearer eyJhbGciOiJIUzM4NCJ9.eyJuYW1lIjoiTW9zaGl1ciBSYWhtYW4gKE1vc2hpKSIsImRlZmF1bHRUZWFtIjp7Im5hbWUiOiJNeSBUZWFtIiwiZW1haWwiOiJtb3NoaS5paXRAZ21haWwuY29tIiwiYWN0aXZlIjp0cnVlLCJ0ZWFtSWQiOiJzdmdhdmt3eGptcml2eHAiLCJ0ZWFtUm9sZSI6eyJuYW1lIjoiVEVBTV9BRE1JTiIsImRlc2NyaXB0aW9uIjoiT3duZXIifSwic2V0dGluZ3MiOnt9LCJwcm9qZWN0TGltaXQiOjEwMDAsInVzZXJMaW1pdCI6MTAwMCwic3Vic2NyaXB0aW9uTGV2ZWwiOiJGUkVFIiwibGltaXRMbG1zIjpmYWxzZSwiZW5hYmxlUHJvbXB0QXVkaXRMb2dnaW5nIjpmYWxzZX0sInR5cGUiOiJBUEkiLCJzdWIiOiJtb3NoaS5paXRAZ21haWwuY29tIiwiYXVkIjpbInN2Z2F2a3d4am1yaXZ4cCJdLCJpYXQiOjE3Mzc4OTQ5ODgsImV4cCI6MTc0MDQ4Njk4OH0.L3uQSNfQx8p6QTn9MgNBofykJ6EKQPuhmy5mztTEiD7dYCn2oSdv3BOaeLpuuesA'\n",
    "}\n",
    "\n",
    "def llama_generate_answer(prompt):\n",
    "  payload = {\n",
    "    \"messages\": [\n",
    "      {\"role\": \"system\", \"content\": \"You are a world class general knowledge expert with very hight IQ. You understand Bangla and English language and can do complex calculation and find answer.\"},\n",
    "      {\"role\": \"user\", \"content\": prompt\n",
    "      }\n",
    "    ]\n",
    "  }\n",
    "  response = requests.post('https://platform.sectorflow.ai/api/v1/chat/5c79ba5d-23f8-404d-b776-42e045b35a1d/completions', json=payload, headers=headers)\n",
    "  return response.json()['choices'][0]['choices'][0]['message']['content']\n",
    "\n",
    "\n",
    "llama_generate_answer(\"Who are you?\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d99uh9WiEFyH"
   },
   "source": [
    "## Generate LLM Answer and Accuracy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "4KV_jqpDG0Y6"
   },
   "outputs": [],
   "source": [
    "def generate_answer(llm, question, option_1, option_2, option_3, option_4):\n",
    "    prompt = f\"\"\"\n",
    "    Question: {question}\n",
    "    A: f{option_1}\n",
    "    B: f{option_2}\n",
    "    C: f{option_3}\n",
    "    D: f{option_4}\n",
    "\n",
    "    Please provide answer inside <Answer>YOUR ANSWER HERE</Answer>, Answer will be ONLY A/B/C/D, no other explanation is required>\n",
    "    \"\"\"\n",
    "    # Convert question to string to avoid TypeError\n",
    "    prompt = prompt.replace(\"${content}\", str(question))\n",
    "\n",
    "    if llm == \"DEEP_SEEK\":\n",
    "        answer = deepseek_generate_answer(prompt)\n",
    "    elif llm == \"OPEN_AI\":\n",
    "        answer = chatgpt_generate_answer(prompt)\n",
    "    elif llm == \"GEMINI\":\n",
    "        answer = google_generate_answer(prompt)\n",
    "    elif llm == \"LLAMA\":\n",
    "        answer = llama_generate_answer(prompt)\n",
    "    else:\n",
    "        answer = None\n",
    "\n",
    "    try:\n",
    "        answer = answer.replace(\"<Answer>\", \"\").replace(\"</Answer>\", \"\").strip()\n",
    "    except:\n",
    "        answer = None\n",
    "\n",
    "    return answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "rimxrnlJ0_Ab",
    "outputId": "092ad0dc-241e-4bb8-d621-211ae3dad065"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'A'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "answer = \"<Answer> A </Answer>\"\n",
    "answer.replace(\"<Answer>\", \"\").replace(\"</Answer>\", \"\").strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "id": "fXX-VOezq05-",
    "outputId": "a7187e80-dc9c-44aa-96b0-cba8a59eeaad"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEEP_SEEK: B\n",
      "OPEN_AI: B\n",
      "GEMINI: B\n",
      "LLAMA: B\n"
     ]
    }
   ],
   "source": [
    "question = \"\"\"\n",
    "শুদ্ধ বানান কোনটি?\n",
    "\"\"\"\n",
    "option_1 = 'মূমুর্ষু'\n",
    "option_2 = 'মুমূর্ষু'\n",
    "option_3 = 'মূমূর্ষ'\n",
    "option_4 = 'মুমূর্ষ'\n",
    "for llm in llms:\n",
    "  answer = generate_answer(llm, question, option_1, option_2, option_3, option_4)\n",
    "  print(f\"{llm}: {answer}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aLY-xl2dvG_f"
   },
   "source": [
    "## Loading data from CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "id": "RlzBG4cqJM3w",
    "outputId": "55413b72-b79b-4db7-9ff3-acf57d87b0de"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3950,\n  \"fields\": [\n    {\n      \"column\": \"Question\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3794,\n        \"samples\": [\n          \"\\u09e7\\u09ef\\u09ee\\u09ef \\u09b8\\u09be\\u09b2\\u09c7\\u09b0 \\u09b8\\u09ae\\u09c0\\u0995\\u09cd\\u09b7\\u09be \\u0985\\u09a8\\u09c1\\u09b8\\u09be\\u09b0\\u09c7 \\u09b8\\u09ac\\u099a\\u09c7\\u09df\\u09c7 \\u09ac\\u09c7\\u09b6\\u09bf \\u099a\\u09be\\u09b2 \\u09b0\\u09aa\\u09cd\\u09a4\\u09be\\u09a8\\u09bf\\u0995\\u09be\\u09b0\\u0995 \\u09a6\\u09c7\\u09b6-\",\n          \"Of the following authors, who wrote an epic?\",\n          \"\\u09ac\\u09be\\u0982\\u09b2\\u09be\\u09a6\\u09c7\\u09b6\\u09c7\\u09b0 \\u098f\\u0987 \\u099c\\u09c7\\u09b2\\u09be\\u09df \\u09b8\\u09ae\\u09cd\\u09aa\\u09cd\\u09b0\\u09a4\\u09bf \\u098f\\u0987 \\u09aa\\u09cd\\u09b0\\u09a5\\u09ae \\u09b0\\u09c7\\u09b2 \\u09b8\\u0982\\u09af\\u09cb\\u0997 \\u09b9\\u09df-\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Subject\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"GEOGRAPHY-ENVIRONMENT-AND-DIGESTER-MANAGEMENT\",\n          \"MENTAL-ABILITY\",\n          \"BANGLA-LANGUAGE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Answer\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"B\",\n          \"A, b\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"A\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3322,\n        \"samples\": [\n          \"\\u09a8\\u09be\\u0987\\u099c\\u09c7\\u09b0\\u09bf\\u09df\\u09be\",\n          \"\\u09b8\\u09be\\u09b2\\u09ae\\u09be \\u09b8\\u09cb\\u09ac\\u09b9\\u09be\\u09a8\",\n          \"Vital\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"B\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3387,\n        \"samples\": [\n          \"\\u09a6\\u09c1\\u2019\\u09a6\\u09c7\\u09b6\\u09c7\\u09b0 \\u09a8\\u09a6\\u09c0\\u0997\\u09c1\\u09b2\\u09cb\\u09b0 \\u09aa\\u09b2\\u09bf\\u09ae\\u09be\\u099f\\u09bf \\u0985\\u09aa\\u09b8\\u09be\\u09b0\\u09a3\",\n          \"\\u09e8\\u09ee \\u09ac\\u099b\\u09b0\",\n          \"\\u09a7\\u09be\\u09b0\\u09be \\u09e8\\u09ed\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"C\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3416,\n        \"samples\": [\n          \"diplomatic\",\n          \"came\",\n          \"\\u200d\\\\(\\\\mathrm {a}\\\\)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"D\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3404,\n        \"samples\": [\n          \"\\\\({\\\\mathrm{ECO}}\\\\)\",\n          \"Static\",\n          \"\\\\(8\\\\mathrm {xy}\\\\)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "df"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-76becc4f-83d3-47d7-8e17-9f3d1742fabd\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Question</th>\n",
       "      <th>Subject</th>\n",
       "      <th>Answer</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>‘আনারস’ এবং ‘চাবি’ শব্দ দুটি বাংলা ভাষা গ্রহণ ...</td>\n",
       "      <td>BANGLA-LANGUAGE</td>\n",
       "      <td>A</td>\n",
       "      <td>পর্তুগিজ ভাষা হতে</td>\n",
       "      <td>আরবি ভাষা হতে</td>\n",
       "      <td>দেশী ভাষা হতে</td>\n",
       "      <td>ওলন্দাজ ভাষা হতে</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>শুদ্ধ বানান কোনটি?</td>\n",
       "      <td>BANGLA-LANGUAGE</td>\n",
       "      <td>B</td>\n",
       "      <td>মূমুর্ষু</td>\n",
       "      <td>মুমূর্ষু</td>\n",
       "      <td>মূমূর্ষ</td>\n",
       "      <td>মুমূর্ষ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>গুরুচণ্ডালী দোষমুক্ত কোনটি?</td>\n",
       "      <td>BANGLA-LANGUAGE</td>\n",
       "      <td>C</td>\n",
       "      <td>শবপোড়া</td>\n",
       "      <td>মড়াদাহ</td>\n",
       "      <td>শবদাহ</td>\n",
       "      <td>শবমড়া</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>‘কবর’ নাটকটির লেখক-</td>\n",
       "      <td>BANGLA-LITERATURE</td>\n",
       "      <td>C</td>\n",
       "      <td>জসীমউদ্দীন</td>\n",
       "      <td>নজরুল ইসলাম</td>\n",
       "      <td>মুনীর চৌধুরী</td>\n",
       "      <td>দ্বিজেন্দ্রলাল রায়</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>‘উভয়কূল রক্ষা’ অর্থে ব্যবহৃত প্রবচন কোনটি?</td>\n",
       "      <td>BANGLA-LANGUAGE</td>\n",
       "      <td>C</td>\n",
       "      <td>কারো পৌষ মাস, কারো সর্বনাস</td>\n",
       "      <td>চাল না চুলো, ঢেঁকী না কুলো</td>\n",
       "      <td>সাপও মরে, লাঠিও না ভাঙ্গে</td>\n",
       "      <td>বোঝার উপর, শাকের আঁটি</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "      --hover-bg-color: #E2EBFA;\n",
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       "  [theme=dark] .colab-df-quickchart {\n",
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       "  .colab-df-quickchart-complete:disabled,\n",
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       "\n",
       "  .colab-df-spinner {\n",
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       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
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       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
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       "</div>\n",
       "\n",
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       "  </div>\n"
      ],
      "text/plain": [
       "                                            Question            Subject  \\\n",
       "0  ‘আনারস’ এবং ‘চাবি’ শব্দ দুটি বাংলা ভাষা গ্রহণ ...    BANGLA-LANGUAGE   \n",
       "1                                 শুদ্ধ বানান কোনটি?    BANGLA-LANGUAGE   \n",
       "2                       গুরুচণ্ডালী দোষমুক্ত কোনটি?    BANGLA-LANGUAGE   \n",
       "3                                ‘কবর’ নাটকটির লেখক-  BANGLA-LITERATURE   \n",
       "4         ‘উভয়কূল রক্ষা’ অর্থে ব্যবহৃত প্রবচন কোনটি?    BANGLA-LANGUAGE   \n",
       "\n",
       "  Answer                           A                           B  \\\n",
       "0      A           পর্তুগিজ ভাষা হতে               আরবি ভাষা হতে   \n",
       "1      B                    মূমুর্ষু                    মুমূর্ষু   \n",
       "2      C                      শবপোড়া                      মড়াদাহ   \n",
       "3      C                  জসীমউদ্দীন                 নজরুল ইসলাম   \n",
       "4      C  কারো পৌষ মাস, কারো সর্বনাস  চাল না চুলো, ঢেঁকী না কুলো   \n",
       "\n",
       "                           C                      D  \n",
       "0              দেশী ভাষা হতে       ওলন্দাজ ভাষা হতে  \n",
       "1                    মূমূর্ষ                মুমূর্ষ  \n",
       "2                      শবদাহ                  শবমড়া  \n",
       "3               মুনীর চৌধুরী     দ্বিজেন্দ্রলাল রায়  \n",
       "4  সাপও মরে, লাঠিও না ভাঙ্গে  বোঝার উপর, শাকের আঁটি  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "csv_file_path = \"cleaned_questions_answers_10-42_BCS.csv\"\n",
    "df = pd.read_csv(csv_file_path, sep=',')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 430
    },
    "id": "6crWTWlIifeW",
    "outputId": "9b8e86df-b36e-4624-c3ba-f81334369ddc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "df.groupby('Subject').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))\n",
    "plt.gca().spines[['top', 'right',]].set_visible(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iVAqLkLVENK9"
   },
   "source": [
    "## Finding accuracy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "8h5B9Yc_Djmq",
    "outputId": "89d59b11-8b13-4876-8428-5f1ea80b8204"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping row 1 (already processed)\n",
      "Skipping row 2 (already processed)\n",
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      "Error generating answer for LLAMA: 'choices'\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tornado.access:429 POST /v1beta/models/gemini-1.5-flash:generateContent?%24alt=json%3Benum-encoding%3Dint (127.0.0.1) 609.50ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error generating answer for GEMINI: 429 POST https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent?%24alt=json%3Benum-encoding%3Dint: Resource has been exhausted (e.g. check quota).\n",
      "Error generating answer for LLAMA: 'choices'\n",
      "Processed row 192/200\n",
      "Processed row 192/200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tornado.access:429 POST /v1beta/models/gemini-1.5-flash:generateContent?%24alt=json%3Benum-encoding%3Dint (127.0.0.1) 635.28ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error generating answer for GEMINI: 429 POST https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent?%24alt=json%3Benum-encoding%3Dint: Resource has been exhausted (e.g. check quota).\n",
      "Error generating answer for LLAMA: 'choices'\n",
      "Processed row 193/200\n",
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      "Error generating answer for LLAMA: 'choices'\n",
      "Processed row 194/200\n",
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      "Error generating answer for LLAMA: 'choices'\n",
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      "Error generating answer for LLAMA: 'choices'\n",
      "Processed row 196/200\n",
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      "Error generating answer for LLAMA: 'choices'\n",
      "Processed row 197/200\n",
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      "Error generating answer for LLAMA: 'choices'\n",
      "Processed row 198/200\n",
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      "Error generating answer for LLAMA: 'choices'\n",
      "Processed row 199/200\n",
      "Processed row 199/200\n",
      "Error generating answer for LLAMA: 'choices'\n",
      "Processed row 200/200\n",
      "Processed row 200/200\n"
     ]
    }
   ],
   "source": [
    "# Initialize columns for each LLM's generated answer (if they don't already exist)\n",
    "df = df.head(200)\n",
    "\n",
    "for llm in llms:\n",
    "    if llm not in df.columns:\n",
    "        df[llm] = None  # Add a column for each LLM to store generated answers\n",
    "\n",
    "# Iterate over each row in the DataFrame\n",
    "for index, row in df.iterrows():\n",
    "    # Skip rows that have already been processed (if resuming)\n",
    "    if all(pd.notna(row[llm]) for llm in llms):\n",
    "        print(f\"Skipping row {index + 1} (already processed)\")\n",
    "        continue\n",
    "    question = row['Question']\n",
    "    option_1 = row['A']\n",
    "    option_2 = row['B']\n",
    "    option_3 = row['C']\n",
    "    option_4 = row['D']\n",
    "    correct_answer = row['Answer']  # Assuming you have a column 'Correct_Answer'\n",
    "\n",
    "    # Generate answers for each LLM\n",
    "    for llm in llms:\n",
    "        try:\n",
    "          generated_answer = generate_answer(llm, question, option_1, option_2, option_3, option_4)\n",
    "          df.at[index, llm] = generated_answer  # Store the generated answer in the DataFrame\n",
    "        except Exception as e:\n",
    "          print(f\"Error generating answer for {llm}: {e}\")\n",
    "\n",
    "    # Save the updated DataFrame back to the CSV file after processing each row\n",
    "    df.to_csv(csv_file_path, index=False)\n",
    "    print(f\"Processed row {index + 1}/{len(df)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XfrONfTmDLMH",
    "outputId": "a85bf922-33f4-4870-a22c-18a501591358"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for DEEP_SEEK: 79.50%\n",
      "Accuracy for OPEN_AI: 61.50%\n",
      "Accuracy for GEMINI: 59.50%\n",
      "Accuracy for LLAMA: 52.50%\n"
     ]
    }
   ],
   "source": [
    "# Calculate accuracy for each LLM\n",
    "correct_answers = {llm: 0 for llm in llms}\n",
    "for index, row in df.iterrows():\n",
    "    correct_answer = row['Answer']\n",
    "    for llm in llms:\n",
    "        if row[llm] == correct_answer:\n",
    "            correct_answers[llm] += 1\n",
    "\n",
    "# Calculate and print accuracy\n",
    "total_questions = len(df)\n",
    "accuracy = {llm: (correct_answers[llm] / total_questions) * 100 for llm in llms}\n",
    "for llm in llms:\n",
    "    print(f\"Accuracy for {llm}: {accuracy[llm]:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "zkrfGsnosoja",
    "outputId": "2d5372ff-a9eb-43cb-884c-9add6f457de2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vnq0RvPbt26t69ep65ZVXtG3bNqflSUlJGj58uCQpPDxcJUuW1MyZM5WSkmLv89lnn2nfvn2ZzqR4o9555x1dvnzZ/vytt97SX3/9ZX/fZfZapqam5ujnkZmrp4X38vJSlSpVZFmWLl++LHd3dz344INauXKlw2WD8fHx9i+a9vPzu6EaACA3MOIEADcgq0vlrhQZGammTZtq+PDhOnLkiGrUqKG1a9dq5cqVGjx4sP2eppo1a6pLly6aMWOGEhIS1KBBA23YsEEHDx502uaECRP0xRdfqF69eurbt6+qVKmic+fOadeuXVq/fr3OnTuX7WP49ttvdfDgwSyncw4JCVHt2rU1f/58DR06VFFRUXr//fcVExOj7du3q1GjRkpOTtb69ev11FNP6ZFHHlHTpk3Vo0cPTZ06VQcOHLBfNvfVV1+padOm9n099thjmjBhgh577DGFh4dr8+bN+uWXX7Jdu5+fnxo3bqxJkybp8uXLCgkJ0dq1azMdJRk3bpzWrl2rJk2aqF+/fqpcubJOnjypJUuWaMuWLQ6XWkZFRWnq1Kn64osvNHHixGzV4unpqeXLlysiIkKNGzdWx44dde+998rT01M//fSTFixYoKJFi+qVV16Rp6enJk6cqOjoaDVp0kRdunRRfHy83njjDYWFhemZZ57J9muQXampqbr//vvVsWNH7d+/XzNmzFDDhg318MMPS5IaNGigokWLqmfPnho4cKBsNps++OCDG7787cEHH1RQUJDuvfdeBQYGat++fZo+fbpat25tH4F9+eWXtW7dOjVs2FBPPfWUPDw89PbbbyslJUWTJk264WMHgFzhkrn8AOA2dOV05Ndy9XTklmVZSUlJ1jPPPGOVKlXK8vT0tCpUqGD95z//cZhS2bIs6+LFi9bAgQOt4sWLWwULFrQiIyOtY8eOOU0xbVl/Tx/ev39/KzQ01PL09LSCgoKs+++/33rnnXfsfbIzHfnTTz9tSXKYCvpqo0aNsiRZu3fvtizr72mrhw8fbpUpU8a+7/bt2zts46+//rL+85//WJUqVbK8vLysEiVKWC1btrR27txp73PhwgWrT58+lr+/v1W4cGGrY8eO1qlTp7Kcjvz06dNOtR0/ftxq27atVaRIEcvf39/q0KGD9fvvv2f6mv32229WVFSUVaJECcvb29sqW7as1b9/fyslJcVpu1WrVrXc3Nys48ePZ/m6ZOb8+fPWiBEjrOrVq1sFChSwfHx8rGrVqlnDhg2zTp486dB38eLFVq1atSxvb2+rWLFiVrdu3Zz217NnT6tgwYJO+2nSpEmm03xf/f7LeN9++eWXVr9+/ayiRYtahQoVsrp162adPXvWYd2vv/7auueeeyxfX1+rVKlS1nPPPWd9/vnnliTriy++MO47Y9mV05G//fbbVuPGja3ixYtb3t7eVrly5awhQ4ZYCQkJDuvt2rXLat68uVWoUCGrQIECVtOmTa2tW7c69MnqHPziiy+cagSA3GazLO6kBADgarVq1VKxYsW0YcMGV5dyQ+bNm6fo6Gh99913mU7FDgDIHu5xAgDgKjt27FBsbKyioqJcXQoAII/gHicAAP7fjz/+qJ07d+q1115TcHCwOnXq5OqSAAB5BCNOAAD8v6VLlyo6OlqXL1/WwoUL5ePj4+qSAAB5hEuD0+bNmxUZGalSpUrJZrNpxYoVxnU2bdqk2rVry9vbW+XLl9e8efNuep0AgPxh1KhRSk9P1759+9SkSRNXl5MrevXqJcuyuL8JAG6QS4NTcnKyatSooTfffDNb/X/99Ve1bt1aTZs2VWxsrAYPHqzHHntMn3/++U2uFAAAAEB+lmdm1bPZbProo4/Upk2bLPsMHTpUq1evdvjCx86dO+uPP/7QmjVrbkGVAAAAAPKj22pyiG3btikiIsKhrXnz5ho8eHCW66SkpDh8K3t6errOnTun4sWLy2az3axSAQAAAORxlmUpKSlJpUqVkpvbtS/Gu62CU1xcnAIDAx3aAgMDlZiYqIsXL8rX19dpnfHjx2v06NG3qkQAAAAAt5ljx47pjjvuuGaf2yo4XY9hw4YpJibG/jwhIUF33nmnjh07Jj8/PxdWBgAAAMCVEhMTFRoaqsKFCxv73lbBKSgoSPHx8Q5t8fHx8vPzy3S0SZK8vb3l7e3t1O7n50dwAgAAAJCtW3huq+9xql+/vjZs2ODQtm7dOtWvX99FFQEAAADID1wanP7880/FxsYqNjZW0t/TjcfGxuro0aOS/r7MLioqyt7/iSee0OHDh/Xcc8/p559/1owZM/Tf//5XzzzzjCvKBwAAAJBPuDQ47dixQ7Vq1VKtWrUkSTExMapVq5ZGjBghSTp58qQ9RElSmTJltHr1aq1bt041atTQa6+9pnfffVfNmzd3Sf0AAAAA8oc88z1Ot0piYqL8/f2VkJDAPU4AAABAPpaTbHBb3eMEAAAAAK5AcAIAAAAAA4ITAAAAABgQnAAAAADAgOAEAAAAAAYEJwAAAAAwIDgBAAAAgAHBCQAAAAAMCE4AAAAAYEBwAgAAAAADghMAAAAAGBCcAAAAAMCA4AQAAAAABgQnAAAAADAgOAEAAACAAcEJAAAAAAwITgAAAABgQHACAAAAAAOCEwAAAAAYEJwAAAAAwIDgBAAAAAAGBCcAAAAAMCA4AQAAAIABwQkAAAAADAhOAAAAAGBAcAIAAAAAA4ITAAAAABgQnAAAAADAgOAEAAAAAAYEJwAAAAAwIDgBAAAAgAHBCQAAAAAMCE4AAAAAYEBwAgAAAAADghMAAAAAGBCcAAAAAMCA4AQAAAAABgQnAAAAADAgOAEAAACAAcEJAAAAAAwITgAAAABgQHACAAAAAAOCEwAAAAAYEJwAAAAAwIDgBAAAAAAGBCcAAAAAMCA4AQAAAIABwQkAAAAADAhOAAAAAGBAcAIAAAAAA4ITAAAAABgQnAAAAADAgOAEAAAAAAYEJwAAAAAwIDgBAAAAgAHBCQAAAAAMCE4AAAAAYEBwAgAAAAADghMAAAAAGBCcAAAAAMCA4AQAAAAABgQnAAAAADAgOAEAAACAAcEJAAAAAAwITgAAAABgQHACAAAAAAOCEwAAAAAYEJwAAAAAwIDgBAAAAAAGBCcAAAAAMCA4AQAAAIABwQkAAAAADAhOAAAAAGBAcAIAAAAAA4ITAAAAABgQnAAAAADAgOAEAAAAAAYEJwAAAAAwIDgBAAAAgAHBCQAAAAAMCE4AAAAAYODy4PTmm28qLCxMPj4+qlevnrZv337N/lOmTFHFihXl6+ur0NBQPfPMM7p06dItqhYAAABAfuTS4LR48WLFxMRo5MiR2rVrl2rUqKHmzZvr1KlTmfZfsGCBnn/+eY0cOVL79u3T7NmztXjxYr3wwgu3uHIAAAAA+YlLg9PkyZPVt29fRUdHq0qVKpo5c6YKFCigOXPmZNp/69atuvfee9W1a1eFhYXpwQcfVJcuXYyjVAAAAABwI1wWnFJTU7Vz505FRET8rxg3N0VERGjbtm2ZrtOgQQPt3LnTHpQOHz6sTz/9VK1atcpyPykpKUpMTHR4AAAAAEBOeLhqx2fOnFFaWpoCAwMd2gMDA/Xzzz9nuk7Xrl115swZNWzYUJZl6a+//tITTzxxzUv1xo8fr9GjR+dq7QAAAADyF5dPDpETmzZt0rhx4zRjxgzt2rVLy5cv1+rVqzV27Ngs1xk2bJgSEhLsj2PHjt3CigEAAAD8E7hsxCkgIEDu7u6Kj493aI+Pj1dQUFCm67z00kvq0aOHHnvsMUlS9erVlZycrH79+mn48OFyc3POgd7e3vL29s79A7hC68iRN3X7QIbVHzN6CgAA4AouG3Hy8vJSnTp1tGHDBntbenq6NmzYoPr162e6zoULF5zCkbu7uyTJsqybVywAAACAfM1lI06SFBMTo549eyo8PFx169bVlClTlJycrOjoaElSVFSUQkJCNH78eElSZGSkJk+erFq1aqlevXo6ePCgXnrpJUVGRtoDFAAAAADkNpcGp06dOun06dMaMWKE4uLiVLNmTa1Zs8Y+YcTRo0cdRphefPFF2Ww2vfjiizpx4oRKlCihyMhIvfLKK646BAAAAAD5gM3KZ9e4JSYmyt/fXwkJCfLz88uVbXKPE24V7nECAADIPTnJBrfVrHoAAAAA4AoEJwAAAAAwIDgBAAAAgAHBCQAAAAAMCE4AAAAAYEBwAgAAAAADghMAAAAAGBCcAAAAAMCA4AQAAAAABgQnAAAAADAgOAEAAACAAcEJAAAAAAwITgAAAABgQHACAAAAAAOCEwAAAAAYEJwAAAAAwIDgBAAAAAAGBCcAAAAAMCA4AQAAAIABwQkAAAAADAhOAAAAAGBAcAIAAAAAA4ITAAAAABgQnAAAAADAgOAEAAAAAAYEJwAAAAAwIDgBAAAAgAHBCQAAAAAMCE4AAAAAYEBwAgAAAAADghMAAAAAGBCcAAAAAMCA4AQAAAAABgQnAAAAADAgOAEAAACAAcEJAAAAAAwITgAAAABgQHACAAAAAAOCEwAAAAAYEJwAAAAAwIDgBAAAAAAGBCcAAAAAMCA4AQAAAIABwQkAAAAADAhOAAAAAGBAcAIAAAAAA4ITAAAAABgQnAAAAADAgOAEAAAAAAYEJwAAAAAwIDgBAAAAgAHBCQAAAAAMCE4AAAAAYEBwAgAAAAADghMAAAAAGBCcAAAAAMCA4AQAAAAABgQnAAAAADAgOAEAAACAAcEJAAAAAAwITgAAAABgQHACAAAAAAOCEwAAAAAYEJwAAAAAwIDgBAAAAAAGBCcAAAAAMCA4AQAAAIABwQkAAAAADAhOAAAAAGBAcAIAAAAAA4ITAAAAABgQnAAAAADAgOAEAAAAAAYEJwAAAAAwIDgBAAAAgAHBCQAAAAAMCE4AAAAAYEBwAgAAAAADghMAAAAAGHi4ugAAt796MWNdXQLyiW8nv+TqEgAA+RQjTgAAAABgQHACAAAAAAOXB6c333xTYWFh8vHxUb169bR9+/Zr9v/jjz/Uv39/BQcHy9vbW3fddZc+/fTTW1QtAAAAgPzIpfc4LV68WDExMZo5c6bq1aunKVOmqHnz5tq/f79Klizp1D81NVUPPPCASpYsqaVLlyokJES//fabihQpcuuLBwAAAJBvuDQ4TZ48WX379lV0dLQkaebMmVq9erXmzJmj559/3qn/nDlzdO7cOW3dulWenp6SpLCwsFtZMgAAAIB8yGWX6qWmpmrnzp2KiIj4XzFuboqIiNC2bdsyXWfVqlWqX7+++vfvr8DAQFWrVk3jxo1TWlpalvtJSUlRYmKiwwMAAAAAcsJlwenMmTNKS0tTYGCgQ3tgYKDi4uIyXefw4cNaunSp0tLS9Omnn+qll17Sa6+9ppdffjnL/YwfP17+/v72R2hoaK4eBwAAAIB/PpdPDpET6enpKlmypN555x3VqVNHnTp10vDhwzVz5sws1xk2bJgSEhLsj2PHjt3CigEAAAD8E7jsHqeAgAC5u7srPj7eoT0+Pl5BQUGZrhMcHCxPT0+5u7vb2ypXrqy4uDilpqbKy8vLaR1vb295e3vnbvEAAFwlfCZfzotbY8cTfOk44AouG3Hy8vJSnTp1tGHDBntbenq6NmzYoPr162e6zr333quDBw8qPT3d3vbLL78oODg409AEAAAAALnBpZfqxcTEaNasWXrvvfe0b98+Pfnkk0pOTrbPshcVFaVhw4bZ+z/55JM6d+6cBg0apF9++UWrV6/WuHHj1L9/f1cdAgAAAIB8wKXTkXfq1EmnT5/WiBEjFBcXp5o1a2rNmjX2CSOOHj0qN7f/ZbvQ0FB9/vnneuaZZ3T33XcrJCREgwYN0tChQ111CAAAAADyAZcGJ0kaMGCABgwYkOmyTZs2ObXVr19f33zzzU2uCgAAAAD+57aaVQ8AAAAAXIHgBAAAAAAGBCcAAAAAMCA4AQAAAIABwQkAAAAADAhOAAAAAGBAcAIAAAAAA4ITAAAAABgQnAAAAADAgOAEAAAAAAY5Dk5hYWEaM2aMjh49ejPqAQAAAIA8J8fBafDgwVq+fLnKli2rBx54QIsWLVJKSsrNqA0AAAAA8oTrCk6xsbHavn27KleurKefflrBwcEaMGCAdu3adTNqBAAAAACXuu57nGrXrq2pU6fq999/18iRI/Xuu+/qX//6l2rWrKk5c+bIsqzcrBMAAAAAXMbjele8fPmyPvroI82dO1fr1q3TPffcoz59+uj48eN64YUXtH79ei1YsCA3awUAAAAAl8hxcNq1a5fmzp2rhQsXys3NTVFRUXr99ddVqVIle5+2bdvqX//6V64WCgAAAACukuPg9K9//UsPPPCA3nrrLbVp00aenp5OfcqUKaPOnTvnSoEAAAAA4Go5Dk6HDx9W6dKlr9mnYMGCmjt37nUXBQAAAAB5SY4nhzh16pS+/fZbp/Zvv/1WO3bsyJWiAAAAACAvyXFw6t+/v44dO+bUfuLECfXv3z9XigIAAACAvCTHwWnv3r2qXbu2U3utWrW0d+/eXCkKAAAAAPKSHAcnb29vxcfHO7WfPHlSHh7XPbs5AAAAAORZOQ5ODz74oIYNG6aEhAR72x9//KEXXnhBDzzwQK4WBwAAAAB5QY6HiF599VU1btxYpUuXVq1atSRJsbGxCgwM1AcffJDrBQIAAACAq+U4OIWEhGjPnj2aP3++du/eLV9fX0VHR6tLly6ZfqcTAAAAANzuruumpIIFC6pfv365XQsAAAAA5EnXPZvD3r17dfToUaWmpjq0P/zwwzdcFAAAAADkJTkOTocPH1bbtm31ww8/yGazybIsSZLNZpMkpaWl5W6FAAAAuC2M3tzb1SUgnxjZeM4t32eOZ9UbNGiQypQpo1OnTqlAgQL66aeftHnzZoWHh2vTpk03oUQAAAAAcK0cjzht27ZNGzduVEBAgNzc3OTm5qaGDRtq/PjxGjhwoL7//vubUScAAAAAuEyOR5zS0tJUuHBhSVJAQIB+//13SVLp0qW1f//+3K0OAAAAAPKAHI84VatWTbt371aZMmVUr149TZo0SV5eXnrnnXdUtmzZm1EjAAAAALhUjoPTiy++qOTkZEnSmDFj9NBDD6lRo0YqXry4Fi9enOsFAgAAAICr5Tg4NW/e3P7/5cuX188//6xz586paNGi9pn1AAAAAOCfJEf3OF2+fFkeHh768ccfHdqLFStGaAIAAADwj5Wj4OTp6ak777yT72oCAAAAkK/keFa94cOH64UXXtC5c+duRj0AAAAAkOfk+B6n6dOn6+DBgypVqpRKly6tggULOizftWtXrhUHAAAAAHlBjoNTmzZtbkIZAAAAAJB35Tg4jRw58mbUAQAAAAB5Vo7vcQIAAACA/CbHI05ubm7XnHqcGfcAAAAA/NPkODh99NFHDs8vX76s77//Xu+9955Gjx6da4UBAAAAQF6R4+D0yCOPOLW1b99eVatW1eLFi9WnT59cKQwAAAAA8opcu8fpnnvu0YYNG3JrcwAAAACQZ+RKcLp48aKmTp2qkJCQ3NgcAAAAAOQpOb5Ur2jRog6TQ1iWpaSkJBUoUEAffvhhrhYHAAAAAHlBjoPT66+/7hCc3NzcVKJECdWrV09FixbN1eIAAAAAIC/IcXDq1avXTSgDAAAAAPKuHN/jNHfuXC1ZssSpfcmSJXrvvfdypSgAAAAAyEtyHJzGjx+vgIAAp/aSJUtq3LhxuVIUAAAAAOQlOQ5OR48eVZkyZZzaS5curaNHj+ZKUQAAAACQl+Q4OJUsWVJ79uxxat+9e7eKFy+eK0UBAAAAQF6S4+DUpUsXDRw4UF988YXS0tKUlpamjRs3atCgQercufPNqBEAAAAAXCrHs+qNHTtWR44c0f333y8Pj79XT09PV1RUFPc4AQAAAPhHynFw8vLy0uLFi/Xyyy8rNjZWvr6+ql69ukqXLn0z6gMAAAAAl8txcMpQoUIFVahQITdrAQAAAIA8Kcf3OLVr104TJ050ap80aZI6dOiQK0UBAAAAQF6S4+C0efNmtWrVyqm9ZcuW2rx5c64UBQAAAAB5SY6D059//ikvLy+ndk9PTyUmJuZKUQAAAACQl+Q4OFWvXl2LFy92al+0aJGqVKmSK0UBAAAAQF6S48khXnrpJT366KM6dOiQmjVrJknasGGDFixYoKVLl+Z6gQAAAADgajkOTpGRkVqxYoXGjRunpUuXytfXVzVq1NDGjRtVrFixm1EjAAAAALjUdU1H3rp1a7Vu3VqSlJiYqIULF+rZZ5/Vzp07lZaWlqsFAgAAAICr5fgepwybN29Wz549VapUKb322mtq1qyZvvnmm9ysDQAAAADyhByNOMXFxWnevHmaPXu2EhMT1bFjR6WkpGjFihVMDAEAAADgHyvbI06RkZGqWLGi9uzZoylTpuj333/XtGnTbmZtAAAAAJAnZHvE6bPPPtPAgQP15JNPqkKFCjezJgAAAADIU7I94rRlyxYlJSWpTp06qlevnqZPn64zZ87czNoAAAAAIE/IdnC65557NGvWLJ08eVKPP/64Fi1apFKlSik9PV3r1q1TUlLSzawTAAAAAFwmx7PqFSxYUL1799aWLVv0ww8/6N///rcmTJigkiVL6uGHH74ZNQIAAACAS133dOSSVLFiRU2aNEnHjx/XwoULc6smAAAAAMhTbig4ZXB3d1ebNm20atWq3NgcAAAAAOQpuRKcAAAAAOCfjOAEAAAAAAYEJwAAAAAwIDgBAAAAgAHBCQAAAAAMCE4AAAAAYEBwAgAAAAADghMAAAAAGOSJ4PTmm28qLCxMPj4+qlevnrZv356t9RYtWiSbzaY2bdrc3AIBAAAA5GsuD06LFy9WTEyMRo4cqV27dqlGjRpq3ry5Tp06dc31jhw5omeffVaNGjW6RZUCAAAAyK9cHpwmT56svn37Kjo6WlWqVNHMmTNVoEABzZkzJ8t10tLS1K1bN40ePVply5a9hdUCAAAAyI9cGpxSU1O1c+dORURE2Nvc3NwUERGhbdu2ZbnemDFjVLJkSfXp08e4j5SUFCUmJjo8AAAAACAnXBqczpw5o7S0NAUGBjq0BwYGKi4uLtN1tmzZotmzZ2vWrFnZ2sf48ePl7+9vf4SGht5w3QAAAADyF5dfqpcTSUlJ6tGjh2bNmqWAgIBsrTNs2DAlJCTYH8eOHbvJVQIAAAD4p/Fw5c4DAgLk7u6u+Ph4h/b4+HgFBQU59T906JCOHDmiyMhIe1t6erokycPDQ/v371e5cuUc1vH29pa3t/dNqB4AAABAfuHSEScvLy/VqVNHGzZssLelp6drw4YNql+/vlP/SpUq6YcfflBsbKz98fDDD6tp06aKjY3lMjwAAAAAN4VLR5wkKSYmRj179lR4eLjq1q2rKVOmKDk5WdHR0ZKkqKgohYSEaPz48fLx8VG1atUc1i9SpIgkObUDAAAAQG5xeXDq1KmTTp8+rREjRiguLk41a9bUmjVr7BNGHD16VG5ut9WtWAAAAAD+YVwenCRpwIABGjBgQKbLNm3adM11582bl/sFAQAAAMAVGMoBAAAAAAOCEwAAAAAYEJwAAAAAwIDgBAAAAAAGBCcAAAAAMCA4AQAAAIABwQkAAAAADAhOAAAAAGBAcAIAAAAAA4ITAAAAABgQnAAAAADAgOAEAAAAAAYEJwAAAAAwIDgBAAAAgAHBCQAAAAAMCE4AAAAAYEBwAgAAAAADghMAAAAAGBCcAAAAAMCA4AQAAAAABgQnAAAAADAgOAEAAACAAcEJAAAAAAwITgAAAABgQHACAAAAAAOCEwAAAAAYEJwAAAAAwIDgBAAAAAAGBCcAAAAAMCA4AQAAAIABwQkAAAAADAhOAAAAAGBAcAIAAAAAA4ITAAAAABgQnAAAAADAgOAEAAAAAAYEJwAAAAAwIDgBAAAAgAHBCQAAAAAMCE4AAAAAYEBwAgAAAAADghMAAAAAGBCcAAAAAMCA4AQAAAAABgQnAAAAADAgOAEAAACAAcEJAAAAAAwITgAAAABgQHACAAAAAAOCEwAAAAAYEJwAAAAAwIDgBAAAAAAGBCcAAAAAMCA4AQAAAIABwQkAAAAADAhOAAAAAGBAcAIAAAAAA4ITAAAAABgQnAAAAADAgOAEAAAAAAYEJwAAAAAwIDgBAAAAgAHBCQAAAAAMCE4AAAAAYEBwAgAAAAADghMAAAAAGBCcAAAAAMCA4AQAAAAABgQnAAAAADAgOAEAAACAAcEJAAAAAAwITgAAAABgQHACAAAAAAOCEwAAAAAYEJwAAAAAwIDgBAAAAAAGBCcAAAAAMCA4AQAAAIABwQkAAAAADAhOAAAAAGBAcAIAAAAAgzwRnN58802FhYXJx8dH9erV0/bt27PsO2vWLDVq1EhFixZV0aJFFRERcc3+AAAAAHCjXB6cFi9erJiYGI0cOVK7du1SjRo11Lx5c506dSrT/ps2bVKXLl30xRdfaNu2bQoNDdWDDz6oEydO3OLKAQAAAOQXLg9OkydPVt++fRUdHa0qVapo5syZKlCggObMmZNp//nz5+upp55SzZo1ValSJb377rtKT0/Xhg0bbnHlAAAAAPILlwan1NRU7dy5UxEREfY2Nzc3RUREaNu2bdnaxoULF3T58mUVK1Ys0+UpKSlKTEx0eAAAAABATrg0OJ05c0ZpaWkKDAx0aA8MDFRcXFy2tjF06FCVKlXKIXxdafz48fL397c/QkNDb7huAAAAAPmLyy/VuxETJkzQokWL9NFHH8nHxyfTPsOGDVNCQoL9cezYsVtcJQAAAIDbnYcrdx4QECB3d3fFx8c7tMfHxysoKOia67766quaMGGC1q9fr7vvvjvLft7e3vL29s6VegEAAADkTy4dcfLy8lKdOnUcJnbImOihfv36Wa43adIkjR07VmvWrFF4ePitKBUAAABAPubSESdJiomJUc+ePRUeHq66detqypQpSk5OVnR0tCQpKipKISEhGj9+vCRp4sSJGjFihBYsWKCwsDD7vVCFChVSoUKFXHYcAAAAAP65XB6cOnXqpNOnT2vEiBGKi4tTzZo1tWbNGvuEEUePHpWb2/8Gxt566y2lpqaqffv2DtsZOXKkRo0adStLBwAAAJBPuDw4SdKAAQM0YMCATJdt2rTJ4fmRI0dufkEAAAAAcIXbelY9AAAAALgVCE4AAAAAYEBwAgAAAAADghMAAAAAGBCcAAAAAMCA4AQAAAAABgQnAAAAADAgOAEAAACAAcEJAAAAAAwITgAAAABgQHACAAAAAAOCEwAAAAAYEJwAAAAAwIDgBAAAAAAGBCcAAAAAMCA4AQAAAIABwQkAAAAADAhOAAAAAGBAcAIAAAAAA4ITAAAAABgQnAAAAADAgOAEAAAAAAYEJwAAAAAwIDgBAAAAgAHBCQAAAAAMCE4AAAAAYEBwAgAAAAADghMAAAAAGBCcAAAAAMCA4AQAAAAABgQnAAAAADAgOAEAAACAAcEJAAAAAAwITgAAAABgQHACAAAAAAOCEwAAAAAYEJwAAAAAwIDgBAAAAAAGBCcAAAAAMCA4AQAAAIABwQkAAAAADAhOAAAAAGBAcAIAAAAAA4ITAAAAABgQnAAAAADAgOAEAAAAAAYEJwAAAAAwIDgBAAAAgAHBCQAAAAAMCE4AAAAAYEBwAgAAAAADghMAAAAAGBCcAAAAAMCA4AQAAAAABgQnAAAAADAgOAEAAACAAcEJAAAAAAwITgAAAABgQHACAAAAAAOCEwAAAAAYEJwAAAAAwIDgBAAAAAAGBCcAAAAAMCA4AQAAAIABwQkAAAAADAhOAAAAAGBAcAIAAAAAA4ITAAAAABgQnAAAAADAgOAEAAAAAAYEJwAAAAAwIDgBAAAAgAHBCQAAAAAMCE4AAAAAYEBwAgAAAAADghMAAAAAGBCcAAAAAMCA4AQAAAAABgQnAAAAADAgOAEAAACAAcEJAAAAAAwITgAAAABgkCeC05tvvqmwsDD5+PioXr162r59+zX7L1myRJUqVZKPj4+qV6+uTz/99BZVCgAAACA/cnlwWrx4sWJiYjRy5Ejt2rVLNWrUUPPmzXXq1KlM+2/dulVdunRRnz599P3336tNmzZq06aNfvzxx1tcOQAAAID8wuXBafLkyerbt6+io6NVpUoVzZw5UwUKFNCcOXMy7f/GG2+oRYsWGjJkiCpXrqyxY8eqdu3amj59+i2uHAAAAEB+4eHKnaempmrnzp0aNmyYvc3NzU0RERHatm1bputs27ZNMTExDm3NmzfXihUrMu2fkpKilJQU+/OEhARJUmJi4g1W/z+XL6eYOwG5IDfft7kpLeWSq0tAPpFXzwFJSrvIZwFujbx8HlxKTnV1Ccgncus8yNiOZVnGvi4NTmfOnFFaWpoCAwMd2gMDA/Xzzz9nuk5cXFym/ePi4jLtP378eI0ePdqpPTQ09DqrBlzH33+iq0sAXMp/xjhXlwC4nH/Mf1xdAuByEzQ/V7eXlJQkf3//a/ZxaXC6FYYNG+YwQpWenq5z586pePHistlsLqws/0pMTFRoaKiOHTsmPz8/V5cDuATnAcB5AHAOuJ5lWUpKSlKpUqWMfV0anAICAuTu7q74+HiH9vj4eAUFBWW6TlBQUI76e3t7y9vb26GtSJEi1180co2fnx//SCDf4zwAOA8AzgHXMo00ZXDp5BBeXl6qU6eONmzYYG9LT0/Xhg0bVL9+/UzXqV+/vkN/SVq3bl2W/QEAAADgRrn8Ur2YmBj17NlT4eHhqlu3rqZMmaLk5GRFR0dLkqKiohQSEqLx48dLkgYNGqQmTZrotddeU+vWrbVo0SLt2LFD77zzjisPAwAAAMA/mMuDU6dOnXT69GmNGDFCcXFxqlmzptasWWOfAOLo0aNyc/vfwFiDBg20YMECvfjii3rhhRdUoUIFrVixQtWqVXPVISCHvL29NXLkSKdLKIH8hPMA4DwAOAduLzYrO3PvAQAAAEA+5vIvwAUAAACAvI7gBAAAAAAGBCcAAAAAMCA4AQAAAIABwekfrlevXrLZbLLZbPL09FRgYKAeeOABzZkzR+np6fZ+YWFh9n5XPiZMmCBJOnLkSKbLbTabvvnmG0nSvHnz7G1ubm664447FB0drVOnTmWr1i+//FLNmjVTsWLFVKBAAVWoUEE9e/ZUamqqJGnTpk1Z1hAXFydJGjVqVKbLK1WqZN/Pfffdp8GDBzvs+4033pC3t7cWLVp03a81/hmOHTum3r17q1SpUvLy8lLp0qU1aNAgnT171t7nvvvus7+3fHx8VKVKFc2YMcO+/Mpz4cqHj4+PvU/GuZlxjmVYsWKFbDZbjuuuVKmSvL297efClTJ7zwM5ERcXp0GDBql8+fLy8fFRYGCg7r33Xr311lu6cOGCpOx/jri7u+vEiRMO2z958qQ8PDxks9l05MgRh/6xsbEOz0uWLKmkpCSH9WvWrKlRo0bZn/Oex83Qq1cvtWnTJtNlYWFhmjJlinEbCxculLu7u/r37++0LOP3nKJFi+rSpUsOy7777jv7OZWZa30GIPcQnPKBFi1a6OTJkzpy5Ig+++wzNW3aVIMGDdJDDz2kv/76y95vzJgxOnnypMPj6aefdtjW+vXrnfrUqVPHvtzPz08nT57U8ePHNWvWLH322Wfq0aOHsca9e/eqRYsWCg8P1+bNm/XDDz9o2rRp8vLyUlpamkPf/fv3O9VQsmRJ+/KqVas6Ld+yZUuW+x45cqReeOEFrVy5Up07dzbWin+uw4cPKzw8XAcOHNDChQt18OBBzZw50/6l3OfOnbP37du3r06ePKm9e/eqY8eO6t+/vxYuXGhfnnEuXPn47bffHPbn4+OjiRMn6vz58zdU95YtW3Tx4kW1b99e77333g1tC7ja4cOHVatWLa1du1bjxo3T999/r23btum5557TJ598ovXr19v7ZudzJCQkRO+//75D23vvvaeQkJBs1ZOUlKRXX331xg8McIHZs2frueee08KFC53CUYbChQvro48+clrvzjvvzLQ/nwG3DsEpH/D29lZQUJBCQkJUu3Zte0j47LPPNG/ePHu/woULKygoyOFRsGBBh20VL17cqY+np6d9uc1mU1BQkEqVKqWWLVtq4MCBWr9+vS5evHjNGteuXaugoCBNmjRJ1apVU7ly5dSiRQvNmjVLvr6+Dn1LlizpVMOV3/Xl4eHhtDwgIMBpn5Zl6emnn9bUqVO1bt06tWjRIicvK/6B+vfvLy8vL61du1ZNmjTRnXfeqZYtW2r9+vU6ceKEhg8fbu9boEABBQUFqWzZsho1apQqVKigVatW2ZdnnAtXPjK+ny5DRESEgoKC7F/wfb1mz56trl27qkePHpozZ84NbQu42lNPPSUPDw/t2LFDHTt2VOXKlVW2bFk98sgjWr16tSIjI+19s/M50rNnT82dO9ehbe7cuerZs2e26nn66ac1efLkbF/NAOQVv/76q7Zu3arnn39ed911l5YvX55pv549ezr8W37x4kUtWrQoy3OEz4Bbh+CUTzVr1kw1atTI8qTNLb6+vkpPT3cY2cpMUFCQTp48qc2bN9/UejL89ddf6t69u5YuXaovv/xSDRo0uCX7Rd517tw5ff7553rqqaecwnpQUJC6deumxYsXK6uvvvP19bVfVppd7u7uGjdunKZNm6bjx49fV91JSUlasmSJunfvrgceeEAJCQn66quvrmtbwNXOnj2rtWvXqn///k4BKENOLy19+OGHdf78efuVAFu2bNH58+cdAti1dOnSReXLl9eYMWNytF/A1ebOnavWrVvL399f3bt31+zZszPt16NHD3311Vc6evSoJGnZsmUKCwtT7dq1nfryGXBrEZzysUqVKtmvJZekoUOHqlChQg6Pq0++Bg0aOPXJyoEDBzRz5kyFh4ercOHC16ylQ4cO6tKli5o0aaLg4GC1bdtW06dPV2JiolPfO+64w2H/VatWdVj+ww8/ONX4xBNPOPSZNWuWli5dqi+++EJ33333NWtD/nDgwAFZlqXKlStnurxy5co6f/68Tp8+7dCelpamDz/8UHv27FGzZs3s7QkJCU7vw5YtWzptt23btqpZs6ZGjhx5XXUvWrRIFSpUUNWqVeXu7q7OnTtn+WEM5NTBgwdlWZYqVqzo0B4QEGB/Xw8dOtTenp3PEU9PT3Xv3t3+l/E5c+aoe/fuDlcvXEvGfVPvvPOODh06dINHCNwa6enpmjdvnrp37y5J6ty5s7Zs2aJff/3VqW/JkiXVsmVL+1VBc+bMUe/evTPdLp8Bt5aHqwuA61iW5fCXwiFDhqhXr14Ofa6+5nzx4sVZ/mIp/e+XxfT0dF26dEkNGzbUu+++a6zF3d1dc+fO1csvv6yNGzfq22+/1bhx4zRx4kRt375dwcHB9r5fffWVQxC7+sO2YsWKDpdMSX/fb3Klhg0bKjY2Vi+99JIWLlwoDw9OBfwtqxGlq82YMUPvvvuuUlNT5e7urmeeeUZPPvmkfXnhwoW1a9cuh3WuHsnKMHHiRDVr1kzPPvtsjuvN+KUzQ/fu3dWkSRNNmzbN+AcL4Hpt375d6enp6tatm1JSUuzt2fkckaTevXurQYMGGjdunJYsWaJt27YZr0y4UvPmzdWwYUO99NJLWrBgwXUfB3CrrFu3TsnJyWrVqpWkv//4kDFZ19ixY5369+7dW4MGDVL37t21bds2LVmyJNORJD4Dbi1+W8zH9u3bpzJlytifBwQEqHz58tdcJzQ09Jp9Mn5ZdHNzU3BwcJa/KGYlJCREPXr0UI8ePTR27FjdddddmjlzpkaPHm3vU6ZMGRUpUiTLbXh5eRmPo3r16nrttdcUERGhTp06afHixYSnfK58+fKy2Wzat2+f2rZt67R83759Klq0qEqUKCFJ6tatm4YPHy5fX18FBwc73GcnSW5ubsb3YYbGjRurefPmGjZsmNMvndeyd+9effPNN9q+fbvDX/3T0tK0aNEi9e3bN9vbAjKTcV7s37/fob1s2bKSnP8YkJ3PEenvf4MrVaqkLl26qHLlyqpWrZp99rzsmjBhgurXr68hQ4bkaD3AFWbPnq1z5845nDPp6enas2ePRo8e7fQZ0rJlS/Xr1099+vRRZGSkihcv7rRNPgNuPS7Vy6c2btyoH374Qe3atcvV7Wb8sli2bNkch6arFS1aVMHBwUpOTs6l6hzVrFlTGzZs0ObNm9WxY0ddvnz5puwHt4fixYvrgQce0IwZM5wmM4mLi9P8+fPVqVMn+yitv7+/ypcvr5CQEKcPvOsxYcIEffzxx9q2bVu215k9e7YaN26s3bt3KzY21v6IiYnhUg3kiozzYvr06bn+b3Hv3r21adOmLC9BMqlbt64effRRPf/887laF5Dbzp49q5UrV2rRokUO/1Z///33On/+vNauXeu0joeHh6Kioq55jvAZcOvxJ/Z8ICUlRXFxcUpLS1N8fLzWrFmj8ePH66GHHlJUVJS9X1JSktP8/wUKFHC4zO3s2bNOfYoUKeLw/TTX4+2331ZsbKzatm2rcuXK6dKlS3r//ff1008/adq0aQ59T5065TSFZ/Hixe2X7P31119ONdpsNqcZzSSpRo0a2rhxo+6//3517NhR//3vf7N9nT3+eaZPn64GDRqoefPmevnll1WmTBn99NNPGjJkiEJCQvTKK69ke1uWZWX6fRolS5bMNGhVr15d3bp109SpU7O1/cuXL+uDDz7QmDFjVK1aNYdljz32mCZPnqyffvrJ6R5AIKdmzJihe++9V+Hh4Ro1apTuvvtuubm56bvvvtPPP//s8JUU2fkcydC3b1916NDhmlcQmLzyyiuqWrUqVwzglklISHAaHc0YDTpx4oTTstKlS+uDDz5Q8eLF1bFjR6fJVFq1aqXZs2dnOrPv2LFjNWTIkExHm/gMcA1GnPKBNWvWKDg4WGFhYWrRooW++OILTZ06VStXrpS7u7u934gRIxQcHOzweO655xy2FRER4dRnxYoVN1xj3bp19eeff+qJJ55Q1apV1aRJE33zzTdasWKFmjRp4tC3YsWKTjXs3LnTvvynn35yWl66dOks9129enVt3LhRW7duVYcOHXI8Mxr+OSpUqKAdO3aobNmy6tixo8qVK6d+/fqpadOm2rZtm4oVK5btbSUmJjq9D4ODg685hfKYMWMcvpj6WlatWqWzZ89mellh5cqVVblyZf7iiFxRrlw5ff/994qIiNCwYcNUo0YNhYeHa9q0aXr22Wcd7s/IzudIBg8PDwUEBNxQ6LnrrrvUu3fvLL8PB8htmzZtUq1atRweGbcTvPrqq07LVq9erTlz5qht27aZzkDZrl07rVq1SmfOnHFa5uXlpYCAgEzX4zPANWxWdu+EBgAAAIB8ihEnAAAAADAgOOGWGDdunNN3e1zru20A/D2rUlbnzbhx41xdHgAA+QqX6uGWOHfunM6dO5fpMl9f30y/5wPI706cOOE0w1+GYsWK5eieKwAAcGMITgAAAABgwKV6AAAAAGBAcAIAAAAAA4ITAAAAABgQnAAAAADAgOAEAMD/27Rpk2w2m/74449srxMWFqYpU6bctJoAAHkDwQkAcNvo1auXbDabnnjiCadl/fv3l81mU69evW59YQCAfzyCEwDgthIaGqpFixY5fMfVpUuXtGDBAt15550urAwA8E9GcAIA3FZq166t0NBQLV++3N62fPly3XnnnapVq5a9LSUlRQMHDlTJkiXl4+Ojhg0b6rvvvnPY1qeffqq77rpLvr6+atq0qY4cOeK0vy1btqhRo0by9fVVaGioBg4cqOTk5Jt2fACAvIngBAC47fTu3Vtz5861P58zZ46io6Md+jz33HNatmyZ3nvvPe3atUvly5dX8+bNde7cOUnSsWPH9OijjyoyMlKxsbF67LHH9Pzzzzts49ChQ2rRooXatWunPXv2aPHixdqyZYsGDBhw8w8SAJCnEJwAALed7t27a8uWLfrtt9/022+/6euvv1b37t3ty5OTk/XWW2/pP//5j1q2bKkqVapo1qxZ8vX11ezZsyVJb731lsqVK6fXXntNFStWVLdu3Zzujxo/fry6deumwYMHq0KFCmrQoIGmTp2q999/X5cuXbqVhwwAcDEPVxcAAEBOlShRQq1bt9a8efNkWZZat26tgIAA+/JDhw7p8uXLuvfee+1tnp6eqlu3rvbt2ydJ2rdvn+rVq+ew3fr16zs83717t/bs2aP58+fb2yzLUnp6un799VdVrlz5ZhweACAPIjgBAG5LvXv3tl8y9+abb96Uffz55596/PHHNXDgQKdlTEQBAPkLwQkAcFtq0aKFUlNTZbPZ1Lx5c4dl5cqVk5eXl77++muVLl1aknT58mV99913Gjx4sCSpcuXKWrVqlcN633zzjcPz2rVra+/evSpfvvzNOxAAwG2Be5wAALcld3d37du3T3v37pW7u7vDsoIFC+rJJ5/UkCFDtGbNGu3du1d9+/bVhQsX1KdPH0nSE088oQMHDmjIkCHav3+/FixYoHnz5jlsZ+jQodq6dasGDBig2NhYHThwQCtXrmRyCADIhwhOAIDblp+fn/z8/DJdNmHCBLVr1049evRQ7dq1dfDgQX3++ecqWrSopL8vtVu2bJlWrFihGjVqaObMmRo3bpzDNu6++259+eWX+uWXX9SoUSPVqlVLI0aMUKlSpW76sQEA8habZVmWq4sAAAAAgLyMEScAAAAAMCA4AQAAAIABwQkAAAAADAhOAAAAAGBAcAIAAAAAA4ITAAAAABgQnAAAAADAgOAEAAAAAAYEJwAAAAAwIDgBAAAAgAHBCQAAAAAM/g9rk82/tfqjxgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-31-71f70e3b67a3>:31: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  subject_accuracy[model] = df.groupby('Subject').apply(lambda x: (x[model] == x['Answer']).mean())\n",
      "<ipython-input-31-71f70e3b67a3>:31: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  subject_accuracy[model] = df.groupby('Subject').apply(lambda x: (x[model] == x['Answer']).mean())\n",
      "<ipython-input-31-71f70e3b67a3>:31: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  subject_accuracy[model] = df.groupby('Subject').apply(lambda x: (x[model] == x['Answer']).mean())\n",
      "<ipython-input-31-71f70e3b67a3>:31: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  subject_accuracy[model] = df.groupby('Subject').apply(lambda x: (x[model] == x['Answer']).mean())\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Function to calculate accuracy\n",
    "def calculate_accuracy(model_column):\n",
    "    return (df[model_column] == df['Answer']).mean()\n",
    "\n",
    "# List of model columns\n",
    "models = ['DEEP_SEEK', 'OPEN_AI', 'GEMINI', 'LLAMA']\n",
    "\n",
    "# Calculate accuracy for each model\n",
    "accuracies = {model: calculate_accuracy(model) for model in models}\n",
    "\n",
    "# Convert to DataFrame for easy visualization\n",
    "accuracy_df = pd.DataFrame(list(accuracies.items()), columns=['Model', 'Accuracy'])\n",
    "\n",
    "# Plot accuracy comparison for each model\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='Model', y='Accuracy', data=accuracy_df, palette='viridis', hue='Model', legend=False)\n",
    "plt.title('Model Accuracy Comparison')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Model')\n",
    "plt.ylim(0, 1)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "subject_accuracy = {}\n",
    "\n",
    "for model in models:\n",
    "    subject_accuracy[model] = df.groupby('Subject').apply(lambda x: (x[model] == x['Answer']).mean())\n",
    "\n",
    "subject_accuracy_df = pd.DataFrame(subject_accuracy).reset_index()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "subject_accuracy_df.set_index('Subject').plot(kind='bar', stacked=False, figsize=(10, 6))\n",
    "plt.title('Accuracy by Subject for Different Models')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Subject')\n",
    "plt.ylim(0, 1)\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "BAa4tGAAD2Q-",
    "WSfoT6O3AYvX",
    "_EiA7dSqAcva",
    "RjPo648JCJTP",
    "EC0VgjwLluM1"
   ],
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}