diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..cde6826
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,23 @@
+# Git ignore for Freakquency Streamlit app
+
+# Python caches and bytecode
+__pycache__/
+*.py[cod]
+
+# Virtual environments
+.venv/
+venv/
+env/
+
+# Jupyter checkpoints
+.ipynb_checkpoints/
+
+# VS Code settings
+.vscode/
+
+# dotenv secret file
+.env
+
+# OS files
+.DS_Store
+Thumbs.db
diff --git a/Freakquency (2).pptx b/Freakquency (2).pptx
new file mode 100644
index 0000000..70ea3e9
Binary files /dev/null and b/Freakquency (2).pptx differ
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..4fb3fb7
--- /dev/null
+++ b/README.md
@@ -0,0 +1,81 @@
+# Freakquency — Disease Risk Prediction & AI Recommendations
+
+This repository contains a Streamlit web app that predicts a user's risk category for chronic diseases based on a short questionnaire and then provides AI-generated lifestyle recommendations. The app also includes a small rule-based chatbot for quick health tips.
+
+Working Figma Prototype: https://www.figma.com/proto/6WYEfSgLrciKueygaTbeCf/freakquency?type=design&node-id=1-8950&t=Hj6XFSN527rUBWYw-0&scaling=scale-down&page-id=0%3A1&starting-point-node-id=1%3A8950
+
+## What it does
+- Collects user inputs (age, gender, BMI, blood pressure, cholesterol, glucose, physical activity, smoking, alcohol intake, family history).
+- Uses a pre-trained machine learning model (`model.pkl`) and a pre-fitted scaler (`scaler.pkl`) to predict one of several disease categories (e.g., Healthy, Diabetes, Cardiovascular Disorder, Cancer, Multi-condition Cases).
+- Sends a short prompt to OpenAI to generate 3 practical lifestyle recommendations based on the predicted disease category.
+- Provides a minimal keyword-based chatbot for quick answers about exercise, diet, blood pressure, cholesterol, and glucose.
+
+## Files of interest
+- `app.py` — Streamlit application entrypoint.
+- `model.pkl` — Trained scikit-learn model used for prediction.
+- `scaler.pkl` — Feature scaler used to transform input features before prediction.
+- `chronic_disease_dataset.csv` — Dataset used during development (optional for running the app).
+- `llm_recommender.pkl` — (Optional) additional recommender artifact.
+- `requirements.txt` — Python dependencies.
+
+## Requirements
+- Python 3.8+ recommended
+- The dependencies in `requirements.txt`. Main packages include: `streamlit`, `pandas`, `numpy`, `scikit-learn`, `joblib`, `openai`.
+
+## Setup (Windows PowerShell)
+
+1. Create and activate a virtual environment (recommended):
+
+```powershell
+python -m venv .venv
+.\.venv\Scripts\Activate.ps1
+```
+
+2. Install dependencies:
+
+```powershell
+python -m pip install --upgrade pip
+pip install -r requirements.txt
+```
+
+3. Add your OpenAI API key to an environment variable. The app expects `OPENAI_API_KEY` to be available (the project uses `python-dotenv` if you prefer a `.env` file):
+
+```powershell
+$env:OPENAI_API_KEY = 'sk-...'
+# or create a .env file in the same folder as app.py with:
+# OPENAI_API_KEY=sk-...
+```
+
+Note: If you don't intend to use OpenAI (no API key), the app still works for predictions and the built-in fallback chatbot will answer some keyword queries. However, recommendations from OpenAI will not be available.
+
+## Run the Streamlit app
+
+From the `submissions/Freakquency/code/` folder run:
+
+```powershell
+streamlit run app.py
+```
+
+This will open the app in your default browser (or show a local URL in the terminal such as http://localhost:8501).
+
+## Usage
+1. Fill the questionnaire and click `Predict`.
+2. The app will display the predicted disease category and a short description.
+3. If OpenAI is configured, it will show 3 AI-generated lifestyle recommendations. Otherwise fallback recommendations are shown for certain keywords.
+4. Use the text input at the bottom to ask the small rule-based chatbot questions (e.g., "exercise", "diet").
+
+## Troubleshooting
+- If `model.pkl` or `scaler.pkl` are missing, the app will fail at startup. Make sure both files are in the same folder as `app.py`.
+- If you see OpenAI authentication errors, confirm that `OPENAI_API_KEY` is set correctly in your environment or `.env`.
+- If Streamlit fails to start, ensure your Python path and virtual environment are correct and that `streamlit` is installed in the active environment.
+
+## Security & Privacy
+- Do not commit your OpenAI API key to version control. Use environment variables or a local `.env` file excluded from source control.
+- This app is for demo/educational purposes only and does not replace professional medical advice.
+
+## License
+This project was created for the Oct-4 Hackathon 2025. Check the project or team repository for license details.
+
+---
+If you want, I can also add a brief CONTRIBUTING or LICENSE file, or update the README with screenshots and example inputs. What would you like next?
+
diff --git a/app.py b/app.py
new file mode 100644
index 0000000..e9a6401
--- /dev/null
+++ b/app.py
@@ -0,0 +1,135 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+import os
+from dotenv import load_dotenv
+from openai import OpenAI
+
+# --- Load environment variables from .env ---
+load_dotenv()
+
+# --- Initialize OpenAI client ---
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+# --- Load trained model and scaler ---
+model = joblib.load("model.pkl")
+scaler = joblib.load("scaler.pkl")
+
+# --- Disease names and descriptions ---
+disease_info = {
+    0: {"name": "Healthy", "description": "No major chronic conditions detected. Maintain a balanced lifestyle to stay healthy."},
+    1: {"name": "Diabetes", "description": "Elevated glucose levels detected. Monitor diet, exercise regularly, and consult your doctor."},
+    2: {"name": "Cardiovascular Disorder", "description": "Potential risk of heart or vascular conditions. Keep track of blood pressure, cholesterol, and physical activity."},
+    3: {"name": "Cancer", "description": "Risk factors indicate possible cancer conditions. Regular screenings and medical consultation are advised."},
+    4: {"name": "Multi-condition Cases", "description": "Multiple risk factors detected. Follow a comprehensive health plan and consult a healthcare professional."}
+}
+
+# --- Minimal chatbot responses (fallback) ---
+chat_responses = {
+    "exercise": "Regular exercise helps reduce risk for multiple diseases.",
+    "diet": "A balanced diet can reduce cholesterol and glucose levels.",
+    "blood pressure": "Monitoring your blood pressure regularly is important.",
+    "cholesterol": "Check cholesterol levels regularly and maintain a healthy diet.",
+    "glucose": "Keep glucose levels in check and maintain balanced meals."
+}
+
+def generate_recommendations(disease_name, description):
+    prompt = f"""
+    The user has been predicted to have: {disease_name}.
+    Description: {description}.
+    Suggest 3 practical lifestyle recommendations for this user.
+    Keep them short, actionable, and easy to understand.
+    Format each recommendation as a bullet point.
+    """
+    response = client.chat.completions.create(
+        model="gpt-4o-mini",   # you can switch to gpt-4 or gpt-3.5 if needed
+        messages=[
+            {"role": "system", "content": "You are a helpful health assistant."},
+            {"role": "user", "content": prompt}
+        ],
+        max_tokens=200,
+        temperature=0.7
+    )
+
+    text = response.choices[0].message.content.strip()
+
+    # Clean and split into neat bullet points
+    recs = [
+        line.strip("-•1234567890. ").strip()
+        for line in text.split("\n")
+        if line.strip()
+    ]
+    return recs
+
+# --- Streamlit UI ---
+st.title("Freakquency - Disease Risk Prediction & AI Recommendations")
+st.write("Fill out the questionnaire below to see your predicted disease and AI-generated lifestyle recommendations.")
+
+# --- Streamlit Form ---
+with st.form("health_form"):
+    age = st.number_input("Age", min_value=0, max_value=120, value=30)
+    gender = st.selectbox("Gender", ["Male", "Female", "Other"])
+    bmi = st.number_input("BMI", min_value=10.0, max_value=50.0, value=25.0)
+    blood_pressure = st.number_input("Blood Pressure", min_value=50, max_value=200, value=120)
+    cholesterol_level = st.number_input("Cholesterol Level", min_value=100, max_value=400, value=180)
+    glucose_level = st.number_input("Glucose Level", min_value=50, max_value=300, value=100)
+    physical_activity = st.number_input("Physical Activity (hours/week)", min_value=0, max_value=50, value=2)
+    smoking_status = st.selectbox("Smoking Status", ["Never", "Former", "Current"])
+    alcohol_intake = st.selectbox("Alcohol Intake", ["None", "Moderate", "High"])
+    family_history = st.selectbox("Family History of Disease?", ["No", "Yes"])
+    
+    submit_button = st.form_submit_button(label="Predict")
+
+# --- Preprocess and Predict ---
+if submit_button:
+    # Map categorical values to numeric
+    gender_map = {"Male": 0, "Female": 1, "Other": 2}
+    smoking_map = {"Never": 0, "Former": 1, "Current": 2}
+    alcohol_map = {"None": 0, "Moderate": 1, "High": 2}
+    family_map = {"No": 0, "Yes": 1}
+    
+    input_data = np.array([[ 
+        age,
+        gender_map[gender],
+        bmi,
+        blood_pressure,
+        cholesterol_level,
+        glucose_level,
+        physical_activity,
+        smoking_map[smoking_status],
+        alcohol_map[alcohol_intake],
+        family_map[family_history]
+    ]])
+    
+    # Scale features
+    input_scaled = scaler.transform(input_data)
+    
+    # Predict disease class
+    prediction = model.predict(input_scaled)[0]
+    disease = disease_info[prediction]
+    
+    # Display prediction in a card-like format
+    with st.container():
+        st.markdown("### Predicted Disease Category")
+        st.markdown(f"**{disease['name']}**")
+        st.write(disease["description"])
+        
+        st.markdown("### AI-Generated Lifestyle Recommendations")
+        ai_recs = generate_recommendations(disease['name'], disease['description'])
+        for rec in ai_recs:
+            st.write(f"- {rec}")
+
+# --- Minimal Chatbot ---
+st.subheader("Ask a health-related question:")
+user_input = st.text_input("Type your question here...")
+
+if user_input:
+    response_given = False
+    for key, resp in chat_responses.items():
+        if key in user_input.lower():
+            st.write(resp)
+            response_given = True
+            break
+    if not response_given:
+        st.write("Sorry, I don't have advice for that yet. Try asking about exercise, diet, or blood pressure.")
diff --git a/chronic_disease_dataset.csv b/chronic_disease_dataset.csv
new file mode 100644
index 0000000..71bf528
--- /dev/null
+++ b/chronic_disease_dataset.csv
@@ -0,0 +1,3499 @@
+age,gender,bmi,blood_pressure,cholesterol_level,glucose_level,physical_activity,smoking_status,alcohol_intake,family_history,biomarker_A,biomarker_B,biomarker_C,biomarker_D,target
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+27,0,28.3,122.0,182.4,117.5,1.4,0,0.5,1,51.32,102.04,97.91,96.01,2
diff --git a/disease_prediction.ipynb b/disease_prediction.ipynb
new file mode 100644
index 0000000..7484ef3
--- /dev/null
+++ b/disease_prediction.ipynb
@@ -0,0 +1,860 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0787aa8f-4ebb-489e-a0d2-3d36ae685238",
+   "metadata": {},
+   "source": [
+    "### Importing Libraries & Loading Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "65e46c75-e65f-4f2e-9702-6d12f3d67d74",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-04T17:38:39.449312Z",
+     "iopub.status.busy": "2025-10-04T17:38:39.448929Z",
+     "iopub.status.idle": "2025-10-04T17:38:39.453719Z",
+     "shell.execute_reply": "2025-10-04T17:38:39.453004Z",
+     "shell.execute_reply.started": "2025-10-04T17:38:39.449291Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Data manipulation\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# Visualization\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Machine learning\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "\n",
+    "# For ignoring warnings\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e439fd3f-0f82-4c4c-bc78-ca0617376c5a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-04T17:38:44.309049Z",
+     "iopub.status.busy": "2025-10-04T17:38:44.308640Z",
+     "iopub.status.idle": "2025-10-04T17:38:44.333382Z",
+     "shell.execute_reply": "2025-10-04T17:38:44.332653Z",
+     "shell.execute_reply.started": "2025-10-04T17:38:44.309026Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<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>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>bmi</th>\n",
+       "      <th>blood_pressure</th>\n",
+       "      <th>cholesterol_level</th>\n",
+       "      <th>glucose_level</th>\n",
+       "      <th>physical_activity</th>\n",
+       "      <th>smoking_status</th>\n",
+       "      <th>alcohol_intake</th>\n",
+       "      <th>family_history</th>\n",
+       "      <th>biomarker_A</th>\n",
+       "      <th>biomarker_B</th>\n",
+       "      <th>biomarker_C</th>\n",
+       "      <th>biomarker_D</th>\n",
+       "      <th>target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>69</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27.27</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>185.1</td>\n",
+       "      <td>129.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>44.66</td>\n",
+       "      <td>96.29</td>\n",
+       "      <td>83.01</td>\n",
+       "      <td>106.63</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>32</td>\n",
+       "      <td>0</td>\n",
+       "      <td>28.21</td>\n",
+       "      <td>133.2</td>\n",
+       "      <td>190.4</td>\n",
+       "      <td>133.7</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>55.92</td>\n",
+       "      <td>119.94</td>\n",
+       "      <td>72.49</td>\n",
+       "      <td>105.04</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>89</td>\n",
+       "      <td>1</td>\n",
+       "      <td>32.28</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>230.0</td>\n",
+       "      <td>96.3</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>55.11</td>\n",
+       "      <td>114.20</td>\n",
+       "      <td>67.23</td>\n",
+       "      <td>141.94</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>78</td>\n",
+       "      <td>1</td>\n",
+       "      <td>27.05</td>\n",
+       "      <td>115.4</td>\n",
+       "      <td>207.1</td>\n",
+       "      <td>100.1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>44.86</td>\n",
+       "      <td>99.49</td>\n",
+       "      <td>78.40</td>\n",
+       "      <td>130.89</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>38</td>\n",
+       "      <td>1</td>\n",
+       "      <td>37.68</td>\n",
+       "      <td>128.4</td>\n",
+       "      <td>219.4</td>\n",
+       "      <td>114.6</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>50.15</td>\n",
+       "      <td>59.04</td>\n",
+       "      <td>66.49</td>\n",
+       "      <td>131.33</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age  gender    bmi  blood_pressure  cholesterol_level  glucose_level  \\\n",
+       "0   69       0  27.27            95.0              185.1          129.1   \n",
+       "1   32       0  28.21           133.2              190.4          133.7   \n",
+       "2   89       1  32.28            96.0              230.0           96.3   \n",
+       "3   78       1  27.05           115.4              207.1          100.1   \n",
+       "4   38       1  37.68           128.4              219.4          114.6   \n",
+       "\n",
+       "   physical_activity  smoking_status  alcohol_intake  family_history  \\\n",
+       "0                1.8               1             1.7               0   \n",
+       "1                3.3               1             2.0               0   \n",
+       "2                5.4               2             3.6               1   \n",
+       "3                1.0               0             2.5               1   \n",
+       "4                0.3               0             0.6               1   \n",
+       "\n",
+       "   biomarker_A  biomarker_B  biomarker_C  biomarker_D  target  \n",
+       "0        44.66        96.29        83.01       106.63       0  \n",
+       "1        55.92       119.94        72.49       105.04       0  \n",
+       "2        55.11       114.20        67.23       141.94       4  \n",
+       "3        44.86        99.49        78.40       130.89       0  \n",
+       "4        50.15        59.04        66.49       131.33       1  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the dataset\n",
+    "df = pd.read_csv(\"chronic_disease_dataset.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "abc06071-3496-40ee-8c6a-efa45aa7a615",
+   "metadata": {},
+   "source": [
+    "### Intial EDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2f9a705c-851f-4574-a898-9291aa70c691",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-04T17:39:07.056898Z",
+     "iopub.status.busy": "2025-10-04T17:39:07.056484Z",
+     "iopub.status.idle": "2025-10-04T17:39:07.065289Z",
+     "shell.execute_reply": "2025-10-04T17:39:07.064552Z",
+     "shell.execute_reply.started": "2025-10-04T17:39:07.056871Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 3498 entries, 0 to 3497\n",
+      "Data columns (total 15 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   age                3498 non-null   int64  \n",
+      " 1   gender             3498 non-null   int64  \n",
+      " 2   bmi                3498 non-null   float64\n",
+      " 3   blood_pressure     3498 non-null   float64\n",
+      " 4   cholesterol_level  3498 non-null   float64\n",
+      " 5   glucose_level      3498 non-null   float64\n",
+      " 6   physical_activity  3498 non-null   float64\n",
+      " 7   smoking_status     3498 non-null   int64  \n",
+      " 8   alcohol_intake     3498 non-null   float64\n",
+      " 9   family_history     3498 non-null   int64  \n",
+      " 10  biomarker_A        3498 non-null   float64\n",
+      " 11  biomarker_B        3498 non-null   float64\n",
+      " 12  biomarker_C        3498 non-null   float64\n",
+      " 13  biomarker_D        3498 non-null   float64\n",
+      " 14  target             3498 non-null   int64  \n",
+      "dtypes: float64(10), int64(5)\n",
+      "memory usage: 410.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Quick info\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5db39477-44ed-4688-99b0-12320650b45f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-04T17:40:48.737472Z",
+     "iopub.status.busy": "2025-10-04T17:40:48.737088Z",
+     "iopub.status.idle": "2025-10-04T17:40:48.744705Z",
+     "shell.execute_reply": "2025-10-04T17:40:48.743981Z",
+     "shell.execute_reply.started": "2025-10-04T17:40:48.737450Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "age                  0\n",
+       "gender               0\n",
+       "bmi                  0\n",
+       "blood_pressure       0\n",
+       "cholesterol_level    0\n",
+       "glucose_level        0\n",
+       "physical_activity    0\n",
+       "smoking_status       0\n",
+       "alcohol_intake       0\n",
+       "family_history       0\n",
+       "biomarker_A          0\n",
+       "biomarker_B          0\n",
+       "biomarker_C          0\n",
+       "biomarker_D          0\n",
+       "target               0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check for missing values\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "46c4d7e3-b944-4c46-8eac-170c7ffea82d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-04T17:40:58.099847Z",
+     "iopub.status.busy": "2025-10-04T17:40:58.099485Z",
+     "iopub.status.idle": "2025-10-04T17:40:58.138160Z",
+     "shell.execute_reply": "2025-10-04T17:40:58.137514Z",
+     "shell.execute_reply.started": "2025-10-04T17:40:58.099825Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<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>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>bmi</th>\n",
+       "      <th>blood_pressure</th>\n",
+       "      <th>cholesterol_level</th>\n",
+       "      <th>glucose_level</th>\n",
+       "      <th>physical_activity</th>\n",
+       "      <th>smoking_status</th>\n",
+       "      <th>alcohol_intake</th>\n",
+       "      <th>family_history</th>\n",
+       "      <th>biomarker_A</th>\n",
+       "      <th>biomarker_B</th>\n",
+       "      <th>biomarker_C</th>\n",
+       "      <th>biomarker_D</th>\n",
+       "      <th>target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3498.000000</td>\n",
+       "      <td>3498.000000</td>\n",
+       "      <td>3498.000000</td>\n",
+       "      <td>3498.000000</td>\n",
+       "      <td>3498.000000</td>\n",
+       "      <td>3498.000000</td>\n",
+       "      <td>3498.000000</td>\n",
+       "      <td>3498.000000</td>\n",
+       "      <td>3498.000000</td>\n",
+       "      <td>3498.000000</td>\n",
+       "      <td>3498.000000</td>\n",
+       "      <td>3498.000000</td>\n",
+       "      <td>3498.000000</td>\n",
+       "      <td>3498.000000</td>\n",
+       "      <td>3498.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>53.054317</td>\n",
+       "      <td>0.497427</td>\n",
+       "      <td>27.068645</td>\n",
+       "      <td>119.837107</td>\n",
+       "      <td>199.884019</td>\n",
+       "      <td>99.743768</td>\n",
+       "      <td>3.067439</td>\n",
+       "      <td>0.565752</td>\n",
+       "      <td>1.965409</td>\n",
+       "      <td>0.516581</td>\n",
+       "      <td>49.810666</td>\n",
+       "      <td>99.465346</td>\n",
+       "      <td>75.508694</td>\n",
+       "      <td>119.789969</td>\n",
+       "      <td>1.382790</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>20.692678</td>\n",
+       "      <td>0.500065</td>\n",
+       "      <td>4.989415</td>\n",
+       "      <td>15.029603</td>\n",
+       "      <td>30.033575</td>\n",
+       "      <td>19.724608</td>\n",
+       "      <td>3.110761</td>\n",
+       "      <td>0.740467</td>\n",
+       "      <td>1.967543</td>\n",
+       "      <td>0.499796</td>\n",
+       "      <td>10.231295</td>\n",
+       "      <td>24.969855</td>\n",
+       "      <td>15.082765</td>\n",
+       "      <td>19.970911</td>\n",
+       "      <td>1.375097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>8.520000</td>\n",
+       "      <td>68.600000</td>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>31.600000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12.190000</td>\n",
+       "      <td>9.080000</td>\n",
+       "      <td>22.390000</td>\n",
+       "      <td>53.070000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>23.710000</td>\n",
+       "      <td>109.700000</td>\n",
+       "      <td>179.200000</td>\n",
+       "      <td>86.525000</td>\n",
+       "      <td>0.900000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>43.112500</td>\n",
+       "      <td>82.650000</td>\n",
+       "      <td>65.695000</td>\n",
+       "      <td>106.260000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>53.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>27.105000</td>\n",
+       "      <td>120.000000</td>\n",
+       "      <td>199.700000</td>\n",
+       "      <td>99.650000</td>\n",
+       "      <td>2.100000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.400000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>49.730000</td>\n",
+       "      <td>99.740000</td>\n",
+       "      <td>75.495000</td>\n",
+       "      <td>119.805000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>71.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>30.410000</td>\n",
+       "      <td>130.100000</td>\n",
+       "      <td>220.200000</td>\n",
+       "      <td>113.200000</td>\n",
+       "      <td>4.200000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.700000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>56.815000</td>\n",
+       "      <td>115.762500</td>\n",
+       "      <td>85.437500</td>\n",
+       "      <td>133.570000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>89.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>47.260000</td>\n",
+       "      <td>168.100000</td>\n",
+       "      <td>302.600000</td>\n",
+       "      <td>167.400000</td>\n",
+       "      <td>27.400000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>17.200000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>85.420000</td>\n",
+       "      <td>186.260000</td>\n",
+       "      <td>128.610000</td>\n",
+       "      <td>190.240000</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               age       gender          bmi  blood_pressure  \\\n",
+       "count  3498.000000  3498.000000  3498.000000     3498.000000   \n",
+       "mean     53.054317     0.497427    27.068645      119.837107   \n",
+       "std      20.692678     0.500065     4.989415       15.029603   \n",
+       "min      18.000000     0.000000     8.520000       68.600000   \n",
+       "25%      35.000000     0.000000    23.710000      109.700000   \n",
+       "50%      53.000000     0.000000    27.105000      120.000000   \n",
+       "75%      71.000000     1.000000    30.410000      130.100000   \n",
+       "max      89.000000     1.000000    47.260000      168.100000   \n",
+       "\n",
+       "       cholesterol_level  glucose_level  physical_activity  smoking_status  \\\n",
+       "count        3498.000000    3498.000000        3498.000000     3498.000000   \n",
+       "mean          199.884019      99.743768           3.067439        0.565752   \n",
+       "std            30.033575      19.724608           3.110761        0.740467   \n",
+       "min            99.000000      31.600000           0.000000        0.000000   \n",
+       "25%           179.200000      86.525000           0.900000        0.000000   \n",
+       "50%           199.700000      99.650000           2.100000        0.000000   \n",
+       "75%           220.200000     113.200000           4.200000        1.000000   \n",
+       "max           302.600000     167.400000          27.400000        2.000000   \n",
+       "\n",
+       "       alcohol_intake  family_history  biomarker_A  biomarker_B  biomarker_C  \\\n",
+       "count     3498.000000     3498.000000  3498.000000  3498.000000  3498.000000   \n",
+       "mean         1.965409        0.516581    49.810666    99.465346    75.508694   \n",
+       "std          1.967543        0.499796    10.231295    24.969855    15.082765   \n",
+       "min          0.000000        0.000000    12.190000     9.080000    22.390000   \n",
+       "25%          0.600000        0.000000    43.112500    82.650000    65.695000   \n",
+       "50%          1.400000        1.000000    49.730000    99.740000    75.495000   \n",
+       "75%          2.700000        1.000000    56.815000   115.762500    85.437500   \n",
+       "max         17.200000        1.000000    85.420000   186.260000   128.610000   \n",
+       "\n",
+       "       biomarker_D       target  \n",
+       "count  3498.000000  3498.000000  \n",
+       "mean    119.789969     1.382790  \n",
+       "std      19.970911     1.375097  \n",
+       "min      53.070000     0.000000  \n",
+       "25%     106.260000     0.000000  \n",
+       "50%     119.805000     1.000000  \n",
+       "75%     133.570000     3.000000  \n",
+       "max     190.240000     4.000000  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Summary statistics\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2ada3a2b-75af-49ca-aab7-65ddb541c319",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-04T17:41:01.140701Z",
+     "iopub.status.busy": "2025-10-04T17:41:01.140368Z",
+     "iopub.status.idle": "2025-10-04T17:41:01.284058Z",
+     "shell.execute_reply": "2025-10-04T17:41:01.283305Z",
+     "shell.execute_reply.started": "2025-10-04T17:41:01.140679Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Inspect target distribution\n",
+    "df['target'].value_counts(normalize=True)\n",
+    "sns.countplot(x='target', data=df)\n",
+    "plt.title(\"Target Class Distribution\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61e5ecde-91b4-4007-ad87-fe89f0dc1aba",
+   "metadata": {},
+   "source": [
+    "### Preprocessing Features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "26b3e8f0-f08b-47c9-a9cb-53698dd2b5e1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-04T17:44:25.069850Z",
+     "iopub.status.busy": "2025-10-04T17:44:25.069221Z",
+     "iopub.status.idle": "2025-10-04T17:44:25.082442Z",
+     "shell.execute_reply": "2025-10-04T17:44:25.081409Z",
+     "shell.execute_reply.started": "2025-10-04T17:44:25.069815Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Encode categorical variables if needed\n",
+    "categorical_cols = ['gender', 'smoking_status', 'alcohol_intake', 'physical_activity', 'family_history']\n",
+    "for col in categorical_cols:\n",
+    "    if df[col].dtype == 'object':\n",
+    "        le = LabelEncoder()\n",
+    "        df[col] = le.fit_transform(df[col])\n",
+    "\n",
+    "# Separate features and target\n",
+    "X = df.drop(\"target\", axis=1)\n",
+    "y = df[\"target\"]\n",
+    "\n",
+    "# Standardize numerical features\n",
+    "scaler = StandardScaler()\n",
+    "X_scaled = scaler.fit_transform(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "570dbd4f-a777-4f05-aa69-d4fa9ed4e4cb",
+   "metadata": {},
+   "source": [
+    "### Train/Test Split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0427edf3-40c7-4945-8886-e8b1b53f1b0f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-04T17:44:49.514098Z",
+     "iopub.status.busy": "2025-10-04T17:44:49.513546Z",
+     "iopub.status.idle": "2025-10-04T17:44:49.526096Z",
+     "shell.execute_reply": "2025-10-04T17:44:49.525178Z",
+     "shell.execute_reply.started": "2025-10-04T17:44:49.514061Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X_scaled, y, test_size=0.2, random_state=42, stratify=y\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8856794d-9541-4cdc-9693-3cf6b7a205f6",
+   "metadata": {},
+   "source": [
+    "### Train a Simple Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f712ecab-b3ce-413f-8955-1e8ccb29f806",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-04T17:45:23.315603Z",
+     "iopub.status.busy": "2025-10-04T17:45:23.315194Z",
+     "iopub.status.idle": "2025-10-04T17:45:24.540278Z",
+     "shell.execute_reply": "2025-10-04T17:45:24.539642Z",
+     "shell.execute_reply.started": "2025-10-04T17:45:23.315578Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification Report:\n",
+      "\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.38      0.93      0.54       265\n",
+      "           1       0.17      0.04      0.06       150\n",
+      "           2       0.33      0.02      0.04       104\n",
+      "           3       0.20      0.02      0.03       114\n",
+      "           4       0.00      0.00      0.00        67\n",
+      "\n",
+      "    accuracy                           0.37       700\n",
+      "   macro avg       0.22      0.20      0.13       700\n",
+      "weighted avg       0.26      0.37      0.23       700\n",
+      "\n",
+      "Confusion Matrix:\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Random Forest Classifier (works well for tabular data and multi-class)\n",
+    "model = RandomForestClassifier(n_estimators=100, random_state=42, class_weight=\"balanced\")\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Predictions\n",
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "# Evaluate\n",
+    "print(\"Classification Report:\\n\")\n",
+    "print(classification_report(y_test, y_pred))\n",
+    "print(\"Confusion Matrix:\\n\")\n",
+    "sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, fmt=\"d\", cmap=\"Blues\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "355751d1-05af-4f64-ba69-d1ae478b2fd6",
+   "metadata": {},
+   "source": [
+    "### Feature Importance Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "bffead9e-5bfc-4d0f-8daa-565cba4af412",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-04T17:45:49.530227Z",
+     "iopub.status.busy": "2025-10-04T17:45:49.529815Z",
+     "iopub.status.idle": "2025-10-04T17:45:49.755277Z",
+     "shell.execute_reply": "2025-10-04T17:45:49.754461Z",
+     "shell.execute_reply.started": "2025-10-04T17:45:49.530203Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Feature importance\n",
+    "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
+    "feat_importances = feat_importances.sort_values(ascending=False)\n",
+    "\n",
+    "plt.figure(figsize=(10,6))\n",
+    "sns.barplot(x=feat_importances, y=feat_importances.index)\n",
+    "plt.title(\"Feature Importance\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd676cea-c7b8-4cad-8802-e4cc7b25b4d4",
+   "metadata": {},
+   "source": [
+    "### Saving the trained model and scaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1c21106f-4302-423a-b72e-93b92af4b795",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-10-04T17:51:46.576434Z",
+     "iopub.status.busy": "2025-10-04T17:51:46.575997Z",
+     "iopub.status.idle": "2025-10-04T17:51:46.692502Z",
+     "shell.execute_reply": "2025-10-04T17:51:46.691847Z",
+     "shell.execute_reply.started": "2025-10-04T17:51:46.576408Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['scaler.pkl']"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import joblib\n",
+    "\n",
+    "# Save the trained Random Forest model\n",
+    "joblib.dump(model, \"model.pkl\")\n",
+    "\n",
+    "# Save the scaler\n",
+    "joblib.dump(scaler, \"scaler.pkl\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "anaconda-2024.02-py310",
+   "language": "python",
+   "name": "conda-env-anaconda-2024.02-py310-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/llm_recommender.pkl b/llm_recommender.pkl
new file mode 100644
index 0000000..840eb75
Binary files /dev/null and b/llm_recommender.pkl differ
diff --git a/model.pkl b/model.pkl
new file mode 100644
index 0000000..adf4440
Binary files /dev/null and b/model.pkl differ
diff --git a/recommendations_llm.ipynb b/recommendations_llm.ipynb
new file mode 100644
index 0000000..e47bcf7
--- /dev/null
+++ b/recommendations_llm.ipynb
@@ -0,0 +1,106 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf63074d",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'openai'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mopenai\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m OpenAI\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjoblib\u001b[39;00m\n\u001b[32m      4\u001b[39m client = OpenAI(api_key=\u001b[33m\"\u001b[39m\u001b[33mYOUR_API_KEY\u001b[39m\u001b[33m\"\u001b[39m)  \u001b[38;5;66;03m# Or load from environment variable\u001b[39;00m\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'openai'"
+     ]
+    }
+   ],
+   "source": [
+    "from dotenv import load_dotenv\n",
+    "import os\n",
+    "from openai import OpenAI\n",
+    "import joblib\n",
+    "\n",
+    "load_dotenv()  # loads environment variables from .env\n",
+    "\n",
+    "client = OpenAI(api_key=os.environ[\"OPENAI_API_KEY\"])\n",
+    "\n",
+    "def generate_recommendations_llm(user_data: dict, disease_name: str, n=3):\n",
+    "    \"\"\"\n",
+    "    user_data: dictionary containing age, bmi, bp, etc.\n",
+    "    disease_name: string like 'Type 2 Diabetes'\n",
+    "    n: number of recommendations to generate\n",
+    "    \"\"\"\n",
+    "    # Build the prompt dynamically\n",
+    "    prompt = f\"Patient Data:\\n\"\n",
+    "    for key, val in user_data.items():\n",
+    "        prompt += f\"- {key}: {val}\\n\"\n",
+    "    prompt += f\"\\nPredicted Disease: {disease_name}\\n\\n\"\n",
+    "    prompt += f\"Generate {n} concise lifestyle recommendations for this patient. Be actionable and clear.\"\n",
+    "\n",
+    "    response = client.chat.completions.create(\n",
+    "        model=\"gpt-4.1-mini\",\n",
+    "        messages=[{\"role\": \"user\", \"content\": prompt}],\n",
+    "        temperature=0.7\n",
+    "    )\n",
+    "    \n",
+    "    # Parse the response (split into bullet points)\n",
+    "    text = response.choices[0].message.content\n",
+    "    recs = [line.strip(\"- \").strip() for line in text.split(\"\\n\") if line.strip()]\n",
+    "    return recs\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f194dd1e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "user_data = {\n",
+    "    \"Age\": 45,\n",
+    "    \"Gender\": \"Male\",\n",
+    "    \"BMI\": 29.5,\n",
+    "    \"Blood Pressure\": 135,\n",
+    "    \"Cholesterol\": 210,\n",
+    "    \"Glucose\": 115,\n",
+    "    \"Physical Activity\": 1.5,\n",
+    "    \"Smoking Status\": \"Former\",\n",
+    "    \"Alcohol Intake\": \"Moderate\",\n",
+    "    \"Family History\": \"Yes\"\n",
+    "}\n",
+    "\n",
+    "disease_name = \"Type 2 Diabetes\"\n",
+    "\n",
+    "recs = generate_recommendations_llm(user_data, disease_name)\n",
+    "print(\"Generated Recommendations:\")\n",
+    "for r in recs:\n",
+    "    print(\"-\", r)"
+   ]
+  }
+ ],
+ "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": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..08b17a1
Binary files /dev/null and b/requirements.txt differ
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