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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * Serving Flask app '__main__'\n",
      " * Debug mode: off\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[31m\u001b[1mWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\u001b[0m\n",
      " * Running on http://127.0.0.1:5000\n",
      "\u001b[33mPress CTRL+C to quit\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "127.0.0.1 - - [06/Jan/2025 13:19:51] \"GET / HTTP/1.1\" 200 -\n",
      "/home/faddy/Documents/7-sem/mlops/assignment4/.venv/lib/python3.11/site-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but GradientBoostingRegressor was fitted with feature names\n",
      "  warnings.warn(\n",
      "127.0.0.1 - - [06/Jan/2025 13:20:05] \"POST / HTTP/1.1\" 200 -\n",
      "127.0.0.1 - - [06/Jan/2025 13:20:16] \"POST / HTTP/1.1\" 200 -\n",
      "127.0.0.1 - - [06/Jan/2025 13:20:28] \"POST / HTTP/1.1\" 200 -\n"
     ]
    }
   ],
   "source": [
    "from flask import Flask, request, render_template\n",
    "import numpy as np\n",
    "import pickle\n",
    "\n",
    "app = Flask(__name__)\n",
    "\n",
    "# Cloud Computing Weights and Max Marks\n",
    "cc_weights = {\n",
    "    'A1': 1, 'Q1': 1.5, 'A2': 1, 'Q2': 1.5, 'A3': 1,\n",
    "    'A4': 4, 'Q3': 1.5, 'Mid': 35, 'AWS Labs': 3,\n",
    "    'Q4': 1.25, 'A5': 4, 'Q5': 1.25, 'A6': 4, 'Final': 40\n",
    "}\n",
    "cc_max_marks = {\n",
    "    'A1': 10, 'Q1': 21, 'A2': 10, 'Q2': 30, 'A3': 100,\n",
    "    'A4': 10, 'Q3': 41, 'Mid': 35, 'AWS Labs': 10,\n",
    "    'Q4': 40, 'A5': 100, 'Q5': 20, 'A6': 100, 'Final': 40\n",
    "}\n",
    "\n",
    "# ICT Weights and Max Marks\n",
    "ict_weights = {\n",
    "    'Q1': 2.625, 'Q2': 2.625, 'A1': 2, 'Q3': 2.625, 'Q4': 2.625,\n",
    "    'Midterm': 35, 'Q5': 2.625, 'A2': 2, 'Q6': 2.625, 'Q7': 2.625,\n",
    "    'Q8': 2.625, 'Final': 40\n",
    "}\n",
    "ict_max_marks = {\n",
    "    'Q1': 30, 'Q2': 49, 'A1': 100, 'Q3': 30, 'Q4': 15,\n",
    "    'Midterm': 35, 'Q5': 45, 'A2': 100, 'Q6': 32, 'Q7': 24,\n",
    "    'Q8': 40, 'Final': 100\n",
    "}\n",
    "\n",
    "def normalize_input(input_activities, weights, max_marks):\n",
    "    \"\"\"\n",
    "    Normalize the input activities based on weights and max marks.\n",
    "    \"\"\"\n",
    "    normalized_activities = []\n",
    "    for activity, score in input_activities.items():\n",
    "        if score > 0:  # Ignore zeros\n",
    "            normalized_score = (score / max_marks[activity]) * weights[activity]\n",
    "            normalized_activities.append(normalized_score)\n",
    "    return normalized_activities\n",
    "\n",
    "def predict_final_score(input_activities, weights, max_marks, model_prefix):\n",
    "    \"\"\"\n",
    "    Predict the final score based on input activities after normalization.\n",
    "    \"\"\"\n",
    "    normalized_activities = normalize_input(input_activities, weights, max_marks)\n",
    "    n = len(normalized_activities)\n",
    "\n",
    "    if n == 0:\n",
    "        return \"No valid activities entered. Please provide scores greater than 0.\"\n",
    "\n",
    "    try:\n",
    "        with open(f\"{model_prefix}_model_{n}_activities.pkl\", \"rb\") as file:\n",
    "            model = pickle.load(file)\n",
    "    except FileNotFoundError:\n",
    "        return f\"No model available for {n} activities. Train the model first.\"\n",
    "\n",
    "    input_array = np.array(normalized_activities).reshape(1, -1)\n",
    "    predicted_score = model.predict(input_array)[0]\n",
    "    return round(predicted_score, 2)\n",
    "\n",
    "@app.route('/', methods=['GET', 'POST'])\n",
    "def home():\n",
    "    cc_predicted_score = None\n",
    "    ict_predicted_score = None\n",
    "\n",
    "    if request.method == 'POST':\n",
    "        # Identify the form (Cloud Computing or ICT)\n",
    "        form_type = request.form.get('form_type')\n",
    "\n",
    "        if form_type == 'cloud_computing':\n",
    "            input_activities = {\n",
    "                activity: float(request.form.get(activity, 0) or 0)  # Default to 0 for empty inputs\n",
    "                for activity in cc_weights.keys()\n",
    "            }\n",
    "            cc_predicted_score = predict_final_score(input_activities, cc_weights, cc_max_marks, \"cloud_computing\")\n",
    "\n",
    "        elif form_type == 'ict':\n",
    "            input_activities = {\n",
    "                activity: float(request.form.get(activity, 0) or 0)  # Default to 0 for empty inputs\n",
    "                for activity in ict_weights.keys()\n",
    "            }\n",
    "            ict_predicted_score = predict_final_score(input_activities, ict_weights, ict_max_marks, \"ict\")\n",
    "\n",
    "    return render_template(\n",
    "        'index.html',\n",
    "        cc_max_marks=cc_max_marks,\n",
    "        ict_max_marks=ict_max_marks,\n",
    "        cc_predicted_score=cc_predicted_score,\n",
    "        ict_predicted_score=ict_predicted_score\n",
    "    )\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run(debug=False, port=5000)\n"
   ]
  }
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