model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "id": "cc4b2445",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: openai in c:\\users\\dev2005\\anaconda3\\lib\\site-packages (0.28.0)\n",
      "Requirement already satisfied: pandas in c:\\users\\dev2005\\anaconda3\\lib\\site-packages (1.5.3)\n",
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      "Requirement already satisfied: colorama in c:\\users\\dev2005\\anaconda3\\lib\\site-packages (from tqdm->openai) (0.4.6)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\dev2005\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\dev2005\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\dev2005\\anaconda3\\lib\\site-packages)\n",
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      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\dev2005\\anaconda3\\lib\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "pip install openai pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f4af1338",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to the Rajasthan Engineering College Chatbot!\n",
      "You can ask about college eligibility based on these exams: BITSAT, JEE Main, MET, REAP, SGVU CET, MUST CET, Banasthali Aptitude Test\n",
      "You can also ask about college fees, placements, and general information.\n",
      "Type 'quit' to exit.\n",
      "\n",
      "You: my jee main rank is 5000, which colleges can i get\n",
      "\n",
      "Chatbot: Based on your JEE Main score/rank of 5000, you may be eligible for the following colleges:\n",
      "\n",
      "                                                name location  rating\n",
      "    Malaviya National Institute of Technology (MNIT)   Jaipur     4.4\n",
      "                            JK Lakshmipat University   Jaipur     4.1\n",
      "                                    JECRC University   Jaipur     4.0\n",
      "Jaipur Engineering College & Research Centre (JECRC)   Jaipur     4.2\n",
      "                      Global Institute of Technology   Jaipur     3.9\n",
      "                 Vivekananda Global University (VGU)   Jaipur     4.0\n",
      "    LNM Institute of Information Technology (LNMIIT)   Jaipur     4.3\n",
      "                          Amity University Rajasthan   Jaipur     4.3\n",
      "                          Jaipur National University   Jaipur     4.0\n",
      "\n",
      "You: which college is best amongst above colleges\n",
      "\n",
      "Chatbot: The best college among your options is Malaviya National Institute of Technology (MNIT) in Jaipur with an average package of ₹1800000 and a rating of 4.4.\n",
      "\n",
      "You: what is the average package of bits pilani\n",
      "\n",
      "Chatbot: The average package for BITS Pilani is ₹2500000 per annum.\n",
      "\n",
      "You: what is the median salary of bits pilani\n",
      "\n",
      "Chatbot: The average package for BITS Pilani is ₹2500000 per annum.\n",
      "\n",
      "You: give me some information about bits pilani\n",
      "\n",
      "Chatbot: \n",
      "College: BITS Pilani\n",
      "Location: Pilani\n",
      "Type: Private\n",
      "Rating: 4.5\n",
      "Admission Exam: BITSAT\n",
      "Average Package: ₹2500000\n",
      "Highest Package: ₹6000000\n",
      "Top Recruiters: Microsoft, Google, Amazon, Oracle, Goldman Sachs\n",
      "Facilities: Wi-Fi campus, Smart classrooms, Advanced labs, Sports complex, Incubation center\n",
      "            \n",
      "\n",
      "You: what is the cutoff of bits pilani\n",
      "\n",
      "Chatbot: The cutoff for BITS Pilani in 2023 is 320.\n",
      "\n",
      "You: what is the cutoff of bits pilani for 2021\n",
      "\n",
      "Chatbot: The cutoff for BITS Pilani in 2021 is 310.\n",
      "\n",
      "You: What are the fees for BITS Pilani?\n",
      "\n",
      "Chatbot: Annual fees for BITS Pilani:\n",
      "B.Tech Computer Science: ₹245000 per year\n",
      "B.Tech Mechanical Engineering: ₹245000 per year\n",
      "B.Tech Electrical and Electronics Engineering: ₹245000 per year\n",
      "B.Tech Chemical Engineering: ₹245000 per year\n",
      "B.Tech Civil Engineering: ₹245000 per year\n",
      "\n",
      "You: quit\n",
      "Thank you for using the chatbot. Goodbye!\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import openai\n",
    "import re\n",
    "from fuzzywuzzy import fuzz\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "# Load environment variables\n",
    "load_dotenv()\n",
    "\n",
    "# Load and normalize data\n",
    "def load_data(file_path='dataset3.json'):\n",
    "    try:\n",
    "        with open(file_path, 'r') as f:\n",
    "            data = json.load(f)\n",
    "        return pd.json_normalize(data)\n",
    "    except FileNotFoundError:\n",
    "        print(f\"Error: File {file_path} not found.\")\n",
    "        return None\n",
    "    except json.JSONDecodeError:\n",
    "        print(f\"Error: Unable to parse JSON from {file_path}.\")\n",
    "        return None\n",
    "\n",
    "df = load_data()\n",
    "if df is None:\n",
    "    print(\"Exiting due to data loading error.\")\n",
    "    exit(1)\n",
    "\n",
    "# Set your OpenAI API key\n",
    "openai.api_key = os.getenv('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')\n",
    "\n",
    "def parse_user_input(user_input):\n",
    "    score = ''.join(filter(str.isdigit, user_input))\n",
    "    score = int(score) if score else None\n",
    "    \n",
    "    # Improved regex to handle more variations in phrasing\n",
    "    college_match = re.search(r\"(?:cutoff|information|fees|package|salary).*?\\b([A-Za-z\\s]+)\\b\", user_input, re.IGNORECASE)\n",
    "    college_name = college_match.group(1).strip() if college_match else None\n",
    "    \n",
    "    # Extract year or default to '2023'\n",
    "    year_match = re.search(r'\\b(2023|2022|2021)\\b', user_input)\n",
    "    year = year_match.group(0) if year_match else '2023'\n",
    "    \n",
    "    return score, college_name, year\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def get_unique_exams():\n",
    "    return list(df['admission.exam'].unique())\n",
    "\n",
    "unique_exams = get_unique_exams()\n",
    "\n",
    "def fuzzy_match_college(college_name, threshold=70):\n",
    "    def match_score(x):\n",
    "        full_name_score = fuzz.ratio(x.lower(), college_name.lower())\n",
    "        acronym_score = fuzz.ratio(''.join(word[0] for word in x.split() if word).lower(), college_name.lower())\n",
    "        return max(full_name_score, acronym_score)\n",
    "\n",
    "    best_match = max(df['name'], key=match_score)\n",
    "    if match_score(best_match) > threshold:\n",
    "        return best_match\n",
    "    return None\n",
    "\n",
    "def get_college_cutoff(college_name, year='2023'):\n",
    "    matched_college = fuzzy_match_college(college_name)\n",
    "    if matched_college:\n",
    "        college = df[df['name'] == matched_college]\n",
    "        if not college.empty:\n",
    "            # Construct the column name based on the year\n",
    "            cutoff_column = f'admission.cutoff.{year}'\n",
    "            if cutoff_column in college.columns:\n",
    "                cutoff_info = college.iloc[0][cutoff_column]\n",
    "                if pd.notna(cutoff_info):\n",
    "                    return f\"The cutoff for {matched_college} in {year} is {cutoff_info}.\"\n",
    "                else:\n",
    "                    return f\"Cutoff information for the year {year} is not available.\"\n",
    "            else:\n",
    "                return f\"Cutoff information for the year {year} is not available.\"\n",
    "    return f\"College '{college_name}' not found.\"\n",
    "\n",
    "def normalize_column(column):\n",
    "    return (column - column.min()) / (column.max() - column.min())\n",
    "\n",
    "def find_best_college(eligible_colleges):\n",
    "    if eligible_colleges is None or eligible_colleges.empty:\n",
    "        return \"There are no eligible colleges available. Please ask for eligible colleges first.\"\n",
    "\n",
    "    # Normalize the columns\n",
    "    eligible_colleges['normalized_avg_package'] = normalize_column(eligible_colleges['placements.average_package'].astype(float))\n",
    "    eligible_colleges['normalized_rating'] = normalize_column(eligible_colleges['rating'].astype(float))\n",
    "    eligible_colleges['normalized_highest_package'] = normalize_column(eligible_colleges['placements.highest_package'].astype(float))\n",
    "    eligible_colleges['normalized_cutoff'] = 1 - normalize_column(eligible_colleges['admission.cutoff.2023'].astype(float))\n",
    "\n",
    "    # Assign weights to each parameter\n",
    "    weights = {\n",
    "        'avg_package': 0.4,\n",
    "        'rating': 0.1,\n",
    "        'highest_package': 0.2,\n",
    "        'cutoff': 0.3\n",
    "    }\n",
    "\n",
    "    # Calculate the composite score\n",
    "    eligible_colleges['composite_score'] = sum(weights[param] * eligible_colleges[f'normalized_{param}'] for param in weights)\n",
    "\n",
    "    # Find the best college based on the highest composite score\n",
    "    best_college = eligible_colleges.loc[eligible_colleges['composite_score'].idxmax()]\n",
    "\n",
    "    return f\"The best college among your options is {best_college['name']} in {best_college['location']} with an average package of ₹{best_college['placements.average_package']} and a rating of {best_college['rating']}.\"\n",
    "\n",
    "def get_colleges_by_score(score, exam):\n",
    "    eligible_colleges = df[df['admission.exam'] == exam]\n",
    "    \n",
    "    if exam in [\"JEE Main\", \"REAP\", \"MET\"]:\n",
    "        eligible_colleges = eligible_colleges[eligible_colleges['admission.cutoff.2023'].astype(int) >= score]\n",
    "    elif exam == \"BITSAT\":\n",
    "        eligible_colleges = eligible_colleges[eligible_colleges['admission.cutoff.2023'].astype(int) <= score]\n",
    "    else:\n",
    "        return f\"I'm sorry, but I don't have specific information about how to interpret scores for the {exam} exam.\"\n",
    "    \n",
    "    if eligible_colleges.empty:\n",
    "        return f\"I'm sorry, but with the given {exam} score/rank of {score}, you may not be eligible for any of the colleges in our database. Consider exploring other options or improving your score.\"\n",
    "    else:\n",
    "        result = eligible_colleges[['name', 'location', 'rating']].head(10).to_string(index=False)\n",
    "        return f\"Based on your {exam} score/rank of {score}, you may be eligible for the following colleges:\\n\\n{result}\", eligible_colleges\n",
    "\n",
    "def get_college_fees(college_name):\n",
    "    matched_college = fuzzy_match_college(college_name)\n",
    "    if matched_college:\n",
    "        college = df[df['name'] == matched_college]\n",
    "        if not college.empty:\n",
    "            courses = college.iloc[0]['courses']\n",
    "            fees_info = \"\\n\".join([f\"{course['name']}: ₹{course['annual_fee']} per year\" for course in courses])\n",
    "            return f\"Annual fees for {matched_college}:\\n{fees_info}\"\n",
    "    else:\n",
    "        return f\"College '{college_name}' not found.\"\n",
    "\n",
    "def get_median_salary(college_name):\n",
    "    matched_college = fuzzy_match_college(college_name)\n",
    "    if matched_college:\n",
    "        college = df[df['name'] == matched_college]\n",
    "        if not college.empty:\n",
    "            avg_package = college['placements.average_package'].iloc[0]\n",
    "            return f\"The average package for {matched_college} is ₹{avg_package} per annum.\"\n",
    "    return f\"College '{college_name}' not found.\"\n",
    "\n",
    "def get_college_info(college_name):\n",
    "    matched_college = fuzzy_match_college(college_name)\n",
    "    if matched_college:\n",
    "        college = df[df['name'] == matched_college]\n",
    "        if not college.empty:\n",
    "            info = college.iloc[0]\n",
    "            return f\"\"\"\n",
    "College: {info['name']}\n",
    "Location: {info['location']}\n",
    "Type: {info['type']}\n",
    "Rating: {info['rating']}\n",
    "Admission Exam: {info['admission.exam']}\n",
    "Average Package: ₹{info['placements.average_package']}\n",
    "Highest Package: ₹{info['placements.highest_package']}\n",
    "Top Recruiters: {', '.join(info['placements.top_recruiters'])}\n",
    "Facilities: {', '.join(info['facilities'])}\n",
    "            \"\"\"\n",
    "    return f\"College '{college_name}' not found.\"\n",
    "\n",
    "def chatbot(user_input):\n",
    "    try:\n",
    "        response = openai.ChatCompletion.create(\n",
    "            model=\"gpt-3.5-turbo\",\n",
    "            messages=[\n",
    "                {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n",
    "                {\"role\": \"user\", \"content\": user_input},\n",
    "            ],\n",
    "            max_tokens=150,\n",
    "            n=1,\n",
    "            stop=None,\n",
    "            temperature=0.5,\n",
    "        )\n",
    "        return response['choices'][0]['message']['content'].strip()\n",
    "    except Exception as e:\n",
    "        return f\"An error occurred while processing your request: {str(e)}\"\n",
    "\n",
    "eligible_colleges = None\n",
    "    \n",
    "def process_query(user_input):\n",
    "    global eligible_colleges\n",
    "    lower_input = user_input.lower()\n",
    "    score, college_name, year = parse_user_input(user_input)\n",
    "    \n",
    "\n",
    "    if \"which colleges can i get\" in lower_input:\n",
    "        if score is not None:\n",
    "            exam = next((exam for exam in unique_exams if exam.lower() in lower_input), None)\n",
    "            if exam:\n",
    "                result, eligible_colleges = get_colleges_by_score(score, exam)\n",
    "                return result\n",
    "            else:\n",
    "                return \"Please specify a valid exam (e.g., JEE Main, BITSAT, REAP, MET).\"\n",
    "        else:\n",
    "            return \"Please provide a valid rank or score.\"\n",
    "\n",
    "    elif \"which college is best\" in lower_input:\n",
    "        if eligible_colleges is None:\n",
    "            return \"Please specify a score and exam first to find eligible colleges.\"\n",
    "        return find_best_college(eligible_colleges)\n",
    "    \n",
    "    elif \"cutoff\" in lower_input:\n",
    "        if college_name:\n",
    "            return get_college_cutoff(college_name, year)\n",
    "        else:\n",
    "            return \"I'm sorry, I couldn't find the college name in your request.\"\n",
    "    \n",
    "    elif not college_name:\n",
    "        return \"I'm sorry, I couldn't find the college name in your request.\"\n",
    "    \n",
    "    elif \"fees\" in lower_input:\n",
    "        return get_college_fees(college_name)\n",
    "    \n",
    "    elif \"median salary\" in lower_input or \"average package\" in lower_input:\n",
    "        return get_median_salary(college_name)\n",
    "    \n",
    "    else:\n",
    "        return get_college_info(college_name)\n",
    "\n",
    "\n",
    "def run_chatbot():\n",
    "    print(\"Welcome to the Rajasthan Engineering College Chatbot!\")\n",
    "    print(f\"You can ask about college eligibility based on these exams: {', '.join(unique_exams)}\")\n",
    "    print(\"You can also ask about college fees, placements, and general information.\")\n",
    "    print(\"Type 'quit' to exit.\")\n",
    "    \n",
    "    while True:\n",
    "        user_input = input(\"\\nYou: \")\n",
    "        if user_input.lower() == 'quit':\n",
    "            print(\"Thank you for using the chatbot. Goodbye!\")\n",
    "            break\n",
    "        \n",
    "        response = process_query(user_input)\n",
    "        print(f\"\\nChatbot: {response}\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    run_chatbot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "14f9dcbf",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (1747788076.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[1], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    cohere code\u001b[0m\n\u001b[1;37m           ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "cohere code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16eb10c4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ffe8f30",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb884603",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to the Rajasthan Engineering College Chatbot!\n",
      "You can ask about college eligibility based on these exams: BITSAT, JEE Main, MET, REAP, SGVU CET, MUST CET, Banasthali Aptitude Test\n",
      "You can also ask about college fees, placements, and general information.\n",
      "Type 'quit' to exit.\n",
      "\n",
      "You: which colleges can i get, my jee main rank is 1200\n",
      "\n",
      "Chatbot: Based on your JEE Main score/rank of 1200, you may be eligible for the following colleges:\n",
      "\n",
      "                                                name location  rating\n",
      "    Malaviya National Institute of Technology (MNIT)   Jaipur     4.4\n",
      "                            JK Lakshmipat University   Jaipur     4.1\n",
      "                                    JECRC University   Jaipur     4.0\n",
      "Jaipur Engineering College & Research Centre (JECRC)   Jaipur     4.2\n",
      "                      Global Institute of Technology   Jaipur     3.9\n",
      "                 Vivekananda Global University (VGU)   Jaipur     4.0\n",
      "    LNM Institute of Information Technology (LNMIIT)   Jaipur     4.3\n",
      "                          Amity University Rajasthan   Jaipur     4.3\n",
      "                          Jaipur National University   Jaipur     4.0\n",
      "\n",
      "You: which college is best amongst all of them\n",
      "\n",
      "Chatbot: The best college among your options is Malaviya National Institute of Technology (MNIT) in Jaipur with an average package of ₹1800000 and a rating of 4.4.\n",
      "\n",
      "You: what is the average package of bits pilani\n",
      "\n",
      "Chatbot: The average package for BITS Pilani is ₹2500000 per annum.\n",
      "\n",
      "You: what is the cutoff of bits pilani\n",
      "\n",
      "Chatbot: The cutoff for BITS Pilani in 2023 is 320.\n",
      "\n",
      "You: what is the cutoff of bits pilani for 2021\n",
      "\n",
      "Chatbot: The cutoff for BITS Pilani in 2021 is 310.\n",
      "\n",
      "You: what are the fees for bits pilani\n",
      "\n",
      "Chatbot: Annual fees for BITS Pilani:\n",
      "B.Tech Computer Science: ₹245000 per year\n",
      "B.Tech Mechanical Engineering: ₹245000 per year\n",
      "B.Tech Electrical and Electronics Engineering: ₹245000 per year\n",
      "B.Tech Chemical Engineering: ₹245000 per year\n",
      "B.Tech Civil Engineering: ₹245000 per year\n",
      "\n",
      "You: give me some information about bits pilani\n",
      "\n",
      "Chatbot: \n",
      "College: BITS Pilani\n",
      "Location: Pilani\n",
      "Type: Private\n",
      "Rating: 4.5\n",
      "Admission Exam: BITSAT\n",
      "Average Package: ₹2500000\n",
      "Highest Package: ₹6000000\n",
      "Top Recruiters: Microsoft, Google, Amazon, Oracle, Goldman Sachs\n",
      "Facilities: Wi-Fi campus, Smart classrooms, Advanced labs, Sports complex, Incubation center\n",
      "            \n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import cohere\n",
    "import re\n",
    "from fuzzywuzzy import fuzz\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "# Load environment variables from api.env file\n",
    "load_dotenv('api.env')\n",
    "\n",
    "# Load and normalize data\n",
    "def load_data(file_path='dataset3.json'):\n",
    "    try:\n",
    "        with open(file_path, 'r') as f:\n",
    "            data = json.load(f)\n",
    "        return pd.json_normalize(data)\n",
    "    except FileNotFoundError:\n",
    "        print(f\"Error: File {file_path} not found.\")\n",
    "        return None\n",
    "    except json.JSONDecodeError:\n",
    "        print(f\"Error: Unable to parse JSON from {file_path}.\")\n",
    "        return None\n",
    "\n",
    "df = load_data()\n",
    "if df is None:\n",
    "    print(\"Exiting due to data loading error.\")\n",
    "    exit(1)\n",
    "\n",
    "# Set up Cohere client\n",
    "cohere_api_key = os.getenv('COHERE_API_KEY')\n",
    "if not cohere_api_key:\n",
    "    print(\"Error: COHERE_API_KEY not found in api.env file.\")\n",
    "    exit(1)\n",
    "\n",
    "try:\n",
    "    co = cohere.Client(cohere_api_key)\n",
    "except Exception as e:\n",
    "    print(f\"Error initializing Cohere client: {str(e)}\")\n",
    "    exit(1)\n",
    "\n",
    "def parse_user_input(user_input):\n",
    "    score = ''.join(filter(str.isdigit, user_input))\n",
    "    score = int(score) if score else None\n",
    "    \n",
    "    college_match = re.search(r\"(?:cutoff|information|fees|package|salary).*?\\b([A-Za-z\\s]+)\\b\", user_input, re.IGNORECASE)\n",
    "    college_name = college_match.group(1).strip() if college_match else None\n",
    "    \n",
    "    year_match = re.search(r'\\b(2023|2022|2021)\\b', user_input)\n",
    "    year = year_match.group(0) if year_match else '2023'\n",
    "    \n",
    "    return score, college_name, year\n",
    "\n",
    "def get_unique_exams():\n",
    "    return list(df['admission.exam'].unique())\n",
    "\n",
    "unique_exams = get_unique_exams()\n",
    "\n",
    "def fuzzy_match_college(college_name, threshold=70):\n",
    "    def match_score(x):\n",
    "        full_name_score = fuzz.ratio(x.lower(), college_name.lower())\n",
    "        acronym_score = fuzz.ratio(''.join(word[0] for word in x.split() if word).lower(), college_name.lower())\n",
    "        return max(full_name_score, acronym_score)\n",
    "\n",
    "    best_match = max(df['name'], key=match_score)\n",
    "    if match_score(best_match) > threshold:\n",
    "        return best_match\n",
    "    return None\n",
    "\n",
    "def get_college_cutoff(college_name, year='2023'):\n",
    "    matched_college = fuzzy_match_college(college_name)\n",
    "    if matched_college:\n",
    "        college = df[df['name'] == matched_college]\n",
    "        if not college.empty:\n",
    "            cutoff_column = f'admission.cutoff.{year}'\n",
    "            if cutoff_column in college.columns:\n",
    "                cutoff_info = college.iloc[0][cutoff_column]\n",
    "                if pd.notna(cutoff_info):\n",
    "                    return f\"The cutoff for {matched_college} in {year} is {cutoff_info}.\"\n",
    "                else:\n",
    "                    return f\"Cutoff information for the year {year} is not available.\"\n",
    "            else:\n",
    "                return f\"Cutoff information for the year {year} is not available.\"\n",
    "    return f\"College '{college_name}' not found.\"\n",
    "\n",
    "def normalize_column(column):\n",
    "    return (column - column.min()) / (column.max() - column.min())\n",
    "\n",
    "def find_best_college(eligible_colleges):\n",
    "    if eligible_colleges is None or eligible_colleges.empty:\n",
    "        return \"There are no eligible colleges available. Please ask for eligible colleges first.\"\n",
    "\n",
    "    eligible_colleges['normalized_avg_package'] = normalize_column(eligible_colleges['placements.average_package'].astype(float))\n",
    "    eligible_colleges['normalized_rating'] = normalize_column(eligible_colleges['rating'].astype(float))\n",
    "    eligible_colleges['normalized_highest_package'] = normalize_column(eligible_colleges['placements.highest_package'].astype(float))\n",
    "    eligible_colleges['normalized_cutoff'] = 1 - normalize_column(eligible_colleges['admission.cutoff.2023'].astype(float))\n",
    "\n",
    "    weights = {\n",
    "        'avg_package': 0.4,\n",
    "        'rating': 0.1,\n",
    "        'highest_package': 0.2,\n",
    "        'cutoff': 0.3\n",
    "    }\n",
    "\n",
    "    eligible_colleges['composite_score'] = sum(weights[param] * eligible_colleges[f'normalized_{param}'] for param in weights)\n",
    "\n",
    "    best_college = eligible_colleges.loc[eligible_colleges['composite_score'].idxmax()]\n",
    "\n",
    "    return f\"The best college among your options is {best_college['name']} in {best_college['location']} with an average package of ₹{best_college['placements.average_package']} and a rating of {best_college['rating']}.\"\n",
    "\n",
    "def get_colleges_by_score(score, exam):\n",
    "    eligible_colleges = df[df['admission.exam'] == exam]\n",
    "    \n",
    "    if exam in [\"JEE Main\", \"REAP\", \"MET\"]:\n",
    "        eligible_colleges = eligible_colleges[eligible_colleges['admission.cutoff.2023'].astype(int) >= score]\n",
    "    elif exam == \"BITSAT\":\n",
    "        eligible_colleges = eligible_colleges[eligible_colleges['admission.cutoff.2023'].astype(int) <= score]\n",
    "    else:\n",
    "        return f\"I'm sorry, but I don't have specific information about how to interpret scores for the {exam} exam.\"\n",
    "    \n",
    "    if eligible_colleges.empty:\n",
    "        return f\"I'm sorry, but with the given {exam} score/rank of {score}, you may not be eligible for any of the colleges in our database. Consider exploring other options or improving your score.\"\n",
    "    else:\n",
    "        result = eligible_colleges[['name', 'location', 'rating']].head(10).to_string(index=False)\n",
    "        return f\"Based on your {exam} score/rank of {score}, you may be eligible for the following colleges:\\n\\n{result}\", eligible_colleges\n",
    "\n",
    "def get_college_fees(college_name):\n",
    "    matched_college = fuzzy_match_college(college_name)\n",
    "    if matched_college:\n",
    "        college = df[df['name'] == matched_college]\n",
    "        if not college.empty:\n",
    "            courses = college.iloc[0]['courses']\n",
    "            fees_info = \"\\n\".join([f\"{course['name']}: ₹{course['annual_fee']} per year\" for course in courses])\n",
    "            return f\"Annual fees for {matched_college}:\\n{fees_info}\"\n",
    "    else:\n",
    "        return f\"College '{college_name}' not found.\"\n",
    "\n",
    "def get_median_salary(college_name):\n",
    "    matched_college = fuzzy_match_college(college_name)\n",
    "    if matched_college:\n",
    "        college = df[df['name'] == matched_college]\n",
    "        if not college.empty:\n",
    "            avg_package = college['placements.average_package'].iloc[0]\n",
    "            return f\"The average package for {matched_college} is ₹{avg_package} per annum.\"\n",
    "    return f\"College '{college_name}' not found.\"\n",
    "\n",
    "def get_college_info(college_name):\n",
    "    matched_college = fuzzy_match_college(college_name)\n",
    "    if matched_college:\n",
    "        college = df[df['name'] == matched_college]\n",
    "        if not college.empty:\n",
    "            info = college.iloc[0]\n",
    "            return f\"\"\"\n",
    "College: {info['name']}\n",
    "Location: {info['location']}\n",
    "Type: {info['type']}\n",
    "Rating: {info['rating']}\n",
    "Admission Exam: {info['admission.exam']}\n",
    "Average Package: ₹{info['placements.average_package']}\n",
    "Highest Package: ₹{info['placements.highest_package']}\n",
    "Top Recruiters: {', '.join(info['placements.top_recruiters'])}\n",
    "Facilities: {', '.join(info['facilities'])}\n",
    "            \"\"\"\n",
    "    return f\"College '{college_name}' not found.\"\n",
    "\n",
    "def chatbot(user_input):\n",
    "    try:\n",
    "        response = co.generate(\n",
    "            model='command',\n",
    "            prompt=user_input,\n",
    "            max_tokens=150,\n",
    "            temperature=0.5,\n",
    "            k=0,\n",
    "            stop_sequences=[],\n",
    "            return_likelihoods='NONE'\n",
    "        )\n",
    "        return response.generations[0].text.strip()\n",
    "    except cohere.CohereError as e:\n",
    "        return f\"Cohere API error: {str(e)}\"\n",
    "    except Exception as e:\n",
    "        return f\"An unexpected error occurred: {str(e)}\"\n",
    "\n",
    "eligible_colleges = None\n",
    "    \n",
    "def process_query(user_input):\n",
    "    global eligible_colleges\n",
    "    lower_input = user_input.lower()\n",
    "    score, college_name, year = parse_user_input(user_input)\n",
    "    \n",
    "    if \"which colleges can i get\" in lower_input:\n",
    "        if score is not None:\n",
    "            exam = next((exam for exam in unique_exams if exam.lower() in lower_input), None)\n",
    "            if exam:\n",
    "                result, eligible_colleges = get_colleges_by_score(score, exam)\n",
    "                return result\n",
    "            else:\n",
    "                return \"Please specify a valid exam (e.g., JEE Main, BITSAT, REAP, MET).\"\n",
    "        else:\n",
    "            return \"Please provide a valid rank or score.\"\n",
    "\n",
    "    elif \"which college is best\" in lower_input:\n",
    "        if eligible_colleges is None:\n",
    "            return \"Please specify a score and exam first to find eligible colleges.\"\n",
    "        return find_best_college(eligible_colleges)\n",
    "    \n",
    "    elif \"cutoff\" in lower_input:\n",
    "        if college_name:\n",
    "            return get_college_cutoff(college_name, year)\n",
    "        else:\n",
    "            return \"I'm sorry, I couldn't find the college name in your request.\"\n",
    "    \n",
    "    elif not college_name:\n",
    "        return \"I'm sorry, I couldn't find the college name in your request.\"\n",
    "    \n",
    "    elif \"fees\" in lower_input:\n",
    "        return get_college_fees(college_name)\n",
    "    \n",
    "    elif \"median salary\" in lower_input or \"average package\" in lower_input:\n",
    "        return get_median_salary(college_name)\n",
    "    \n",
    "    else:\n",
    "        return get_college_info(college_name)\n",
    "\n",
    "def run_chatbot():\n",
    "    print(\"Welcome to the Rajasthan Engineering College Chatbot!\")\n",
    "    print(f\"You can ask about college eligibility based on these exams: {', '.join(unique_exams)}\")\n",
    "    print(\"You can also ask about college fees, placements, and general information.\")\n",
    "    print(\"Type 'quit' to exit.\")\n",
    "    \n",
    "    while True:\n",
    "        user_input = input(\"\\nYou: \")\n",
    "        if user_input.lower() == 'quit':\n",
    "            print(\"Thank you for using the chatbot. Goodbye!\")\n",
    "            break\n",
    "        \n",
    "        response = process_query(user_input)\n",
    "        print(f\"\\nChatbot: {response}\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    run_chatbot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b82130d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: cohere in c:\\users\\dev2005\\anaconda3\\lib\\site-packages (5.9.0)\n",
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\dev2005\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\dev2005\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\dev2005\\anaconda3\\lib\\site-packages)\n",
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      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\dev2005\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\dev2005\\anaconda3\\lib\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "pip install cohere"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21212eae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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