final

.gitignore

@@ -0,0 +1,2 @@
+.venv/
+env/

Dockerfile

@@ -14,7 +14,7 @@ RUN pip install --no-cache-dir -r requirements.txt
 COPY . /app
 
 # Expose the port Flask will run on
-EXPOSE 7860
+EXPOSE 5000
 
 # Run the Flask app
 CMD ["python", "app.py"]

app.py

@@ -1,23 +1,88 @@
-from flask import Flask, request, jsonify, render_template
+from flask import Flask, request, render_template
+import numpy as np
+import pickle
 
 app = Flask(__name__)
 
-# Route for the home page
-@app.route('/')
-def index():
-    return render_template('index.html')
-
-# Prediction endpoint (mock prediction for now)
-@app.route('/predict', methods=['POST'])
-def predict():
-    data = request.json
-    activities = data.get('activities')
-    
-    # Placeholder logic for prediction (replace with your ML model)
-    if activities:
-        predicted_score = sum(activities) / len(activities)  # Example logic
-        return jsonify({'predicted_score': predicted_score})
-    return jsonify({'error': 'No activities provided'}), 400
+# Cloud Computing Weights and Max Marks
+cc_weights = {
+    'A1': 1, 'Q1': 1.5, 'A2': 1, 'Q2': 1.5, 'A3': 1,
+    'A4': 4, 'Q3': 1.5, 'Mid': 35, 'AWS Labs': 3,
+    'Q4': 1.25, 'A5': 4, 'Q5': 1.25, 'A6': 4, 'Final': 40
+}
+cc_max_marks = {
+    'A1': 10, 'Q1': 21, 'A2': 10, 'Q2': 30, 'A3': 100,
+    'A4': 10, 'Q3': 41, 'Mid': 35, 'AWS Labs': 10,
+    'Q4': 40, 'A5': 100, 'Q5': 20, 'A6': 100, 'Final': 40
+}
+
+# ICT Weights and Max Marks
+ict_weights = {
+    'Q1': 2.625, 'Q2': 2.625, 'A1': 2, 'Q3': 2.625, 'Q4': 2.625,
+    'Midterm': 35, 'Q5': 2.625, 'A2': 2, 'Q6': 2.625, 'Q7': 2.625,
+    'Q8': 2.625, 'Final': 40
+}
+ict_max_marks = {
+    'Q1': 30, 'Q2': 49, 'A1': 100, 'Q3': 30, 'Q4': 15,
+    'Midterm': 35, 'Q5': 45, 'A2': 100, 'Q6': 32, 'Q7': 24,
+    'Q8': 40, 'Final': 100
+}
+
+def normalize_input(input_activities, weights, max_marks):
+    """
+    Normalize the input activities based on weights and max marks.
+    """
+    normalized_activities = []
+    for activity, score in input_activities.items():
+        if score > 0:  # Ignore zeros
+            normalized_score = (score / max_marks[activity]) * weights[activity]
+            normalized_activities.append(normalized_score)
+    return normalized_activities
+
+def predict_final_score(input_activities, weights, max_marks, model_prefix):
+    """
+    Predict the final score based on input activities after normalization.
+    """
+    normalized_activities = normalize_input(input_activities, weights, max_marks)
+    n = len(normalized_activities)
+
+    if n == 0:
+        return "No valid activities entered. Please provide scores greater than 0."
+
+    try:
+        with open(f"{model_prefix}_model_{n}_activities.pkl", "rb") as file:
+            model = pickle.load(file)
+    except FileNotFoundError:
+        return f"No model available for {n} activities. Train the model first."
+
+    input_array = np.array(normalized_activities).reshape(1, -1)
+    predicted_score = model.predict(input_array)[0]
+    return round(predicted_score, 2)
+
+@app.route('/', methods=['GET', 'POST'])
+def home():
+    cc_predicted_score = None
+    ict_predicted_score = None
+
+    if request.method == 'POST':
+        # Identify the form (Cloud Computing or ICT)
+        form_type = request.form.get('form_type')
+
+        if form_type == 'cloud_computing':
+            input_activities = {activity: float(request.form.get(activity, 0)) for activity in cc_weights.keys()}
+            cc_predicted_score = predict_final_score(input_activities, cc_weights, cc_max_marks, "cc")
+
+        elif form_type == 'ict':
+            input_activities = {activity: float(request.form.get(activity, 0)) for activity in ict_weights.keys()}
+            ict_predicted_score = predict_final_score(input_activities, ict_weights, ict_max_marks, "ict")
+
+    return render_template(
+        'index.html',
+        cc_max_marks=cc_max_marks,
+        ict_max_marks=ict_max_marks,
+        cc_predicted_score=cc_predicted_score,
+        ict_predicted_score=ict_predicted_score
+    )
 
 if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=7860)
+    app.run(debug=False, port=5000)

requirements.txt

@@ -1,17 +1,56 @@
+asttokens==3.0.0
+blinker==1.9.0
 certifi==2024.12.14
 charset-normalizer==3.4.1
+click==8.1.8
+comm==0.2.2
+debugpy==1.8.11
+decorator==5.1.1
+et_xmlfile==2.0.0
+executing==2.1.0
 filelock==3.16.1
+Flask==3.1.0
 fsspec==2024.12.0
 huggingface-hub==0.27.0
 idna==3.10
+ipykernel==6.29.5
+ipython==8.31.0
+itsdangerous==2.2.0
+jedi==0.19.2
+Jinja2==3.1.5
 joblib==1.4.2
+jupyter_client==8.6.3
+jupyter_core==5.7.2
+MarkupSafe==3.0.2
+matplotlib-inline==0.1.7
+nest-asyncio==1.6.0
 numpy==2.2.1
+openpyxl==3.1.5
 packaging==24.2
+pandas==2.2.3
+parso==0.8.4
+pexpect==4.9.0
+platformdirs==4.3.6
+prompt_toolkit==3.0.48
+psutil==6.1.1
+ptyprocess==0.7.0
+pure_eval==0.2.3
+Pygments==2.19.0
+python-dateutil==2.9.0.post0
+pytz==2024.2
 PyYAML==6.0.2
+pyzmq==26.2.0
 requests==2.32.3
 scikit-learn==1.6.0
 scipy==1.15.0
+six==1.17.0
+stack-data==0.6.3
 threadpoolctl==3.5.0
+tornado==6.4.2
 tqdm==4.67.1
+traitlets==5.14.3
 typing_extensions==4.12.2
+tzdata==2024.2
 urllib3==2.3.0
+wcwidth==0.2.13
+Werkzeug==3.1.3

templates/index.html

@@ -6,7 +6,36 @@
     <title>Student Score Predictor</title>
 </head>
 <body>
-    <h1>Welcome to the Student Score Predictor</h1>
-    <p>Submit your activities scores via POST request to /predict to get the final score prediction.</p>
+    <h1>Student Score Predictor</h1>
+    
+    <h2>Cloud Computing</h2>
+    <form method="POST" action="/">
+        <input type="hidden" name="form_type" value="cloud_computing">
+        <p>Enter your scores for the activities:</p>
+        {% for activity, max_mark in cc_max_marks.items() %}
+            <label for="cc_{{ activity }}">{{ activity }} (Max Marks: {{ max_mark }}):</label>
+            <input type="number" step="0.1" name="{{ activity }}" id="cc_{{ activity }}"><br>
+        {% endfor %}
+        <button type="submit">Predict Cloud Computing Final Score</button>
+    </form>
+    
+    {% if cc_predicted_score is not none %}
+        <h3 style="color: blue;">Predicted Cloud Computing Final Score: {{ cc_predicted_score }}</h3>
+    {% endif %}
+
+    <h2>ICT</h2>
+    <form method="POST" action="/">
+        <input type="hidden" name="form_type" value="ict">
+        <p>Enter your scores for the activities:</p>
+        {% for activity, max_mark in ict_max_marks.items() %}
+            <label for="ict_{{ activity }}">{{ activity }} (Max Marks: {{ max_mark }}):</label>
+            <input type="number" step="0.1" name="{{ activity }}" id="ict_{{ activity }}"><br>
+        {% endfor %}
+        <button type="submit">Predict ICT Final Score</button>
+    </form>
+
+    {% if ict_predicted_score is not none %}
+        <h3 style="color: green;">Predicted ICT Final Score: {{ ict_predicted_score }}</h3>
+    {% endif %}
 </body>
 </html>

web.ipynb

@@ -0,0 +1,157 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "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"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}