diff --git a/README.md b/README.md
index b9cf2f9..0da70ab 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,191 @@
-#temp
\ No newline at end of file
+# 🩺 Arogya Sahayak & Skinalyze - Complete Health Assistant Platform
+
+A comprehensive, multilingual health analysis platform combining skin disease detection, general disease prediction, and maternal health risk assessment using artificial intelligence, built with Streamlit.
+
+## ✨ Features
+
+### 👩‍⚕️ **Maternal Health Risk Assessment**
+- Vital signs analysis for maternal health risk prediction
+- Input patient data: Age, Blood Pressure, Blood Sugar, Temperature, Heart Rate
+- Risk categorization: Low, Medium, High
+- Ideal for health workers in rural areas
+- Multilingual support for better accessibility
+
+### 🩺 **General Disease Prediction**
+- Symptom-based disease prediction using AI
+- Multi-symptom selection interface
+- Preliminary diagnosis suggestions
+- Comprehensive symptom database
+- Medical disclaimers and safety recommendations
+
+### 🔍 **AI Skin Analysis**
+- Upload skin images for instant AI-powered disease detection
+- High accuracy analysis with confidence scores
+- Support for JPG, JPEG, and PNG formats
+- Detailed prediction results with visual feedback
+
+### 💬 **Smart Medical Chatbot**
+- Interactive symptom analysis
+- Personalized medical guidance
+- Comprehensive symptom tracking
+- Professional medical recommendations
+
+### 🌐 **Multilingual Support**
+- Available in English, Hindi (हिन्दी), and Marathi (मराठी)
+- Complete interface translation
+- Rural area accessibility focus
+- Language persistence across sessions
+
+### 👤 **User Management**
+- Secure user registration and authentication
+- Password hashing with bcrypt
+- Personal dashboard with analysis history
+- User profile management
+
+### 📊 **Dashboard & Analytics**
+- Personal analysis history tracking
+- User statistics and metrics
+- Community user overview
+- Real-time progress indicators
+
+### 🎨 **Modern UI/UX**
+- Beautiful gradient designs
+- Responsive layout
+- Interactive components
+- Professional medical theme
+- Mobile-friendly interface
+
+## 🚀 Getting Started
+
+### Prerequisites
+- Python 3.8 or higher
+- pip package manager
+
+### Installation
+
+1. **Clone the repository**
+   ```bash
+   git clone <repository-url>
+   cd skinalyze
+   ```
+
+2. **Install dependencies**
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. **Run the application**
+   ```bash
+   streamlit run app.py
+   ```
+
+4. **Access the app**
+   Open your browser and navigate to `http://localhost:8501`
+
+## 📦 Dependencies
+
+- **streamlit** - Web application framework
+- **sqlite3** - Database management
+- **bcrypt** - Password hashing
+- **gradio-client** - AI model integration
+- **Pillow** - Image processing
+- **plotly** - Interactive visualizations
+- **pandas** - Data manipulation
+- **joblib** - Model serialization
+- **scikit-learn** - Machine learning models
+
+## 🏗️ Project Structure
+
+```
+health-assistant/
+├── app.py              # Main application file
+├── requirements.txt    # Python dependencies
+├── README.md          # Project documentation
+├── database.db        # SQLite database (auto-created)
+├── temp/              # Temporary image storage (auto-created)
+├── model/             # ML model files directory
+│   ├── maternal_risk_model.joblib      # Maternal health model
+│   ├── disease_model.joblib            # Disease prediction model
+│   ├── disease_label_encoder.joblib    # Disease label encoder
+│   └── README.md                       # Model documentation
+└── database/          # Training data directory
+    ├── Training.csv                    # Symptom-disease dataset
+    └── README.md                       # Data documentation
+```
+
+## 🔧 Configuration
+
+The application uses SQLite database for user management and stores temporary images in the `temp/` directory. Both are created automatically when the app runs for the first time.
+
+## 🎯 Usage
+
+### 1. **Language Selection**
+- Choose your preferred language from the sidebar
+- Available: English, Hindi (हिन्दी), Marathi (मराठी)
+- Language preference persists throughout the session
+
+### 2. **Registration & Login**
+- Create a new account or login with existing credentials
+- Secure password hashing ensures data protection
+
+### 3. **Maternal Health Risk Assessment**
+- Navigate to "Maternal Health Risk" page
+- Enter patient vitals (Age, BP, Blood Sugar, Temperature, Heart Rate)
+- Click "Assess Risk Level" for AI-powered risk analysis
+- View risk categorization and recommendations
+
+### 4. **Disease Prediction**
+- Access the "Disease Prediction" page
+- Select multiple symptoms from the comprehensive list
+- Click "Predict Disease" for preliminary diagnosis
+- Review AI predictions with medical disclaimers
+
+### 5. **Skin Analysis**
+- Navigate to "Skin Analysis" page
+- Upload a clear image of the skin area
+- Click "Analyze Image" for AI-powered detection
+- View detailed results with confidence scores
+
+### 6. **Medical Chatbot**
+- Access the "Medical Chatbot" page
+- Fill in comprehensive symptom information
+- Receive personalized medical guidance
+- Get professional recommendations
+
+### 7. **Dashboard**
+- View your complete analysis history
+- Track personal health statistics
+- Access quick actions for all features
+- Monitor account information
+
+## ⚠️ Medical Disclaimer
+
+This application is for informational purposes only and should not replace professional medical advice. Always consult with a healthcare professional for proper diagnosis and treatment.
+
+## 🔒 Privacy & Security
+
+- User passwords are securely hashed using bcrypt
+- Personal data is stored locally in SQLite database
+- Images are temporarily processed and automatically deleted
+- No personal medical information is shared with third parties
+
+## 🤝 Contributing
+
+We welcome contributions! Please feel free to submit issues, feature requests, or pull requests.
+
+## 📄 License
+
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+## 📞 Support
+
+For support or questions:
+- Email: support@skinalyze.com
+- Create an issue in the repository
+- Check the documentation
+
+---
+
+**Made with ❤️ using Streamlit**
+
+🔬 **Skinalyze** - Revolutionizing skin health through AI-powered analysis
\ No newline at end of file
diff --git a/app.py b/app.py
index d4cdc55..7224f6e 100644
--- a/app.py
+++ b/app.py
@@ -1,170 +1,1202 @@
-from flask import Flask, render_template, redirect, url_for, flash, request,jsonify  # Import necessary modules
-import requests
-from flask_sqlalchemy import SQLAlchemy
-from flask_bcrypt import Bcrypt
-from flask_login import LoginManager, UserMixin, login_user, login_required, logout_user, current_user
-from models import db, User
-from forms import RegisterForm, LoginForm
+import streamlit as st
 import os
+import sqlite3
+import bcrypt
 from gradio_client import Client, handle_file
+from PIL import Image
+import plotly.graph_objects as go
+from datetime import datetime
+import pandas as pd
+import joblib
+import time
 
-app = Flask(__name__)  # Initialize the Flask application
+# -----------------------------
+# PAGE CONFIG & STYLING
+# -----------------------------
+st.set_page_config(
+    page_title="Arogya Sahayak & Skinalyze - Complete Health Assistant", 
+    page_icon="🩺",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
 
-app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///database.db'  # Configure the database URI
-
-app.config['SECRET_KEY'] = 'supersecretkey'
-
-db.init_app(app)  # Initialize the database with the app
-
-bcrypt = Bcrypt(app)
-login_manager = LoginManager(app)
-login_manager.login_view = "login"
-
-@login_manager.user_loader  # Load user for login management
-
-def load_user(user_id):
-    return db.session.get(User, int(user_id))
-
-
-@app.route("/")  # Route for the index page
-
-def index():
-    return render_template("dashboard0.html")
+# Custom CSS for modern UI
+st.markdown("""
+<style>
+    /* Import Google Fonts */
+    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap');
     
-@app.route("/register", methods=["GET", "POST"])  # Route for user registration
-def register():
-    form = RegisterForm()
-    if form.validate_on_submit():
-        existing_user = User.query.filter_by(username=form.username.data).first()
-        if existing_user:
-            flash("Username already exists. Please choose a different one.", "danger")
-            return redirect(url_for("register"))
-
-        hashed_password = bcrypt.generate_password_hash(form.password.data).decode('utf-8')
-        new_user = User(username=form.username.data,email=form.email.data, password=hashed_password)
-        db.session.add(new_user)
-        db.session.commit()
-        flash("Registration successful! Please log in.", "success")
-        return redirect(url_for("login"))
-
-    return render_template("register.html", form=form)
-
-@app.route("/login", methods=["GET", "POST"])  # Route for user login
-def login():
-    form = LoginForm()
-    if form.validate_on_submit():
-        # Get the user by username
-        user = User.query.filter_by(username=form.username.data).first()
-
-        # If user exists and password is correct
-        if user and bcrypt.check_password_hash(user.password, form.password.data):
-            login_user(user)  # Log the user in
-            return redirect(url_for("dashboard"))  # Redirect to the dashboard
+    /* Global Styles */
+    .main {
+        font-family: 'Inter', sans-serif;
+    }
+    
+    /* Header Styling */
+    .main-header {
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+        padding: 2rem;
+        border-radius: 15px;
+        margin-bottom: 2rem;
+        color: white;
+        text-align: center;
+        box-shadow: 0 10px 30px rgba(0,0,0,0.1);
+    }
+    
+    .main-header h1 {
+        font-size: 3rem;
+        font-weight: 700;
+        margin-bottom: 0.5rem;
+        text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
+    }
+    
+    .main-header p {
+        font-size: 1.2rem;
+        opacity: 0.9;
+        margin: 0;
+    }
+    
+    /* Sidebar Styling */
+    .css-1d391kg {
+        background: linear-gradient(180deg, #f8fafc 0%, #e2e8f0 100%);
+    }
+    
+    /* Card Components */
+    .info-card {
+        background: white;
+        padding: 2rem;
+        border-radius: 15px;
+        box-shadow: 0 5px 20px rgba(0,0,0,0.08);
+        border: 1px solid #e2e8f0;
+        margin-bottom: 1.5rem;
+        transition: transform 0.2s ease;
+    }
+    
+    .info-card:hover {
+        transform: translateY(-2px);
+        box-shadow: 0 8px 25px rgba(0,0,0,0.12);
+    }
+    
+    /* Feature Cards */
+    .feature-card {
+        background: linear-gradient(135deg, #ffffff 0%, #f8fafc 100%);
+        padding: 2rem;
+        border-radius: 15px;
+        border: 2px solid #e2e8f0;
+        text-align: center;
+        margin: 1rem 0;
+        transition: all 0.3s ease;
+    }
+    
+    .feature-card:hover {
+        border-color: #667eea;
+        transform: translateY(-5px);
+        box-shadow: 0 10px 30px rgba(102, 126, 234, 0.1);
+    }
+    
+    .feature-icon {
+        font-size: 3rem;
+        margin-bottom: 1rem;
+    }
+    
+    /* Form Styling */
+    .stTextInput > div > div > input {
+        border-radius: 10px;
+        border: 2px solid #e2e8f0;
+        padding: 0.75rem;
+        font-size: 1rem;
+        transition: border-color 0.2s ease;
+    }
+    
+    .stTextInput > div > div > input:focus {
+        border-color: #667eea;
+        box-shadow: 0 0 0 3px rgba(102, 126, 234, 0.1);
+    }
+    
+    /* Button Styling */
+    .stButton > button {
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+        color: white;
+        border: none;
+        border-radius: 10px;
+        padding: 0.75rem 2rem;
+        font-weight: 600;
+        font-size: 1rem;
+        transition: all 0.2s ease;
+        box-shadow: 0 4px 15px rgba(102, 126, 234, 0.2);
+    }
+    
+    .stButton > button:hover {
+        transform: translateY(-2px);
+        box-shadow: 0 6px 20px rgba(102, 126, 234, 0.3);
+    }
+    
+    /* Success/Error Messages */
+    .stSuccess {
+        background: linear-gradient(135deg, #10b981 0%, #059669 100%);
+        border-radius: 10px;
+        padding: 1rem;
+        color: white;
+        font-weight: 500;
+    }
+    
+    .stError {
+        background: linear-gradient(135deg, #ef4444 0%, #dc2626 100%);
+        border-radius: 10px;
+        padding: 1rem;
+        color: white;
+        font-weight: 500;
+    }
+    
+    /* Stats Cards */
+    .stat-card {
+        background: white;
+        padding: 1.5rem;
+        border-radius: 10px;
+        text-align: center;
+        box-shadow: 0 4px 15px rgba(0,0,0,0.05);
+        border-left: 4px solid #667eea;
+    }
+    
+    .stat-number {
+        font-size: 2rem;
+        font-weight: 700;
+        color: #667eea;
+        margin-bottom: 0.5rem;
+    }
+    
+    .stat-label {
+        color: #64748b;
+        font-weight: 500;
+    }
+    
+    /* Navigation */
+    .nav-item {
+        padding: 0.5rem 1rem;
+        border-radius: 8px;
+        margin: 0.2rem 0;
+        transition: all 0.2s ease;
+    }
+    
+    .nav-item:hover {
+        background: rgba(102, 126, 234, 0.1);
+        color: #667eea;
+    }
+    
+    /* Image Upload Area */
+    .upload-area {
+        border: 2px dashed #cbd5e1;
+        border-radius: 15px;
+        padding: 3rem;
+        text-align: center;
+        background: #f8fafc;
+        margin: 2rem 0;
+        transition: all 0.2s ease;
+    }
+    
+    .upload-area:hover {
+        border-color: #667eea;
+        background: #f1f5f9;
+    }
+    
+    /* Progress Bar */
+    .stProgress > div > div > div > div {
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+    }
+    
+    /* Hide Streamlit Branding */
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    header {visibility: hidden;}
+</style>
+""", unsafe_allow_html=True)
 
-        flash("Login failed. Check username and password.", "danger")
+# -----------------------------
+# TRANSLATION DICTIONARY
+# -----------------------------
+translations = {
+    "app_title": {
+        "en": "Arogya Sahayak & Skinalyze | Complete Health Assistant",
+        "mr": "आरोग्य सहायक आणि स्किनलायझ | संपूर्ण आरोग्य सहायक",
+        "hi": "आरोग्य सहायक और स्किनलायझ | संपूर्ण स्वास्थ्य सहायक"
+    },
+    "language_label": {"en": "Select Language", "mr": "भाषा निवडा", "hi": "भाषा चुनें"},
+    "maternal_tool_name": {"en": "Maternal Health Risk", "mr": "माता आरोग्य जोखीम", "hi": "मातृ स्वास्थ्य जोखिम"},
+    "disease_tool_name": {"en": "Disease Prediction", "mr": "रोग निदान", "hi": "रोग निदान"},
+    "skin_analysis_name": {"en": "Skin Analysis", "mr": "त्वचा विश्लेषण", "hi": "त्वचा विश्लेषण"},
+    "chatbot_name": {"en": "Medical Chatbot", "mr": "वैद्यकीय चॅटबॉट", "hi": "चिकित्सा चैटबॉट"},
+    "home_name": {"en": "Home", "mr": "मुख्यपृष्ठ", "hi": "होम"},
+    "register_name": {"en": "Register", "mr": "नोंदणी", "hi": "रजिस्टर"},
+    "login_name": {"en": "Login", "mr": "लॉगिन", "hi": "लॉगिन"},
+    "dashboard_name": {"en": "Dashboard", "mr": "डॅशबोर्ड", "hi": "डैशबोर्ड"},
+    "users_name": {"en": "Users", "mr": "वापरकर्ते", "hi": "उपयोगकर्ता"},
+    "about_name": {"en": "About Us", "mr": "आमच्याबद्दल", "hi": "हमारे बारे में"},
+    "logout_name": {"en": "Logout", "mr": "लॉगआउट", "hi": "लॉगआउट"},
+    
+    # Maternal Health
+    "maternal_title": {"en": "👩‍⚕️ Maternal Health Risk Predictor", "mr": "👩‍⚕️ माता आरोग्य जोखीम निदान", "hi": "👩‍⚕️ मातृ स्वास्थ्य जोखिम भविष्यवक्ता"},
+    "maternal_desc": {
+        "en": "Enter patient vitals to assess maternal health risk. Ideal for routine checks by health workers.",
+        "mr": "आरोग्य कर्मचाऱ्यांद्वारे नियमित तपासणीसाठी, माता आरोग्य जोखीम मूल्यांकन करण्यासाठी रुग्णाचे तपशील प्रविष्ट करा.",
+        "hi": "मातृ स्वास्थ्य जोखिम का आकलन करने के लिए रोगी के महत्वपूर्ण आँकड़े दर्ज करें। स्वास्थ्य कर्मियों द्वारा नियमित जांच के लिए आदर्श।"
+    },
+    "vitals_header": {"en": "Enter Patient Vitals", "mr": "रुग्णाचे तपशील प्रविष्ट करा", "hi": "रोगी के महत्वपूर्ण आँकड़े दर्ज करें"},
+    "age": {"en": "Age", "mr": "वय", "hi": "आयु"},
+    "systolic_bp": {"en": "Systolic BP (mm Hg)", "mr": "सिस्टोलिक बीपी (मिमी एचजी)", "hi": "सिस्टोलिक बीपी (मिमी एचजी)"},
+    "diastolic_bp": {"en": "Diastolic BP (mm Hg)", "mr": "डायस्टोलिक बीपी (मिमी एचजी)", "hi": "डायस्टोलिक बीपी (मिमी एचजी)"},
+    "bs": {"en": "Blood Sugar (mmol/L)", "mr": "रक्त शर्करा (mmol/L)", "hi": "रक्त शर्करा (mmol/L)"},
+    "body_temp": {"en": "Body Temperature (°F)", "mr": "शरीराचे तापमान (°F)", "hi": "शरीर का तापमान (°F)"},
+    "heart_rate": {"en": "Heart Rate (bpm)", "mr": "हृदय गती (bpm)", "hi": "हृदय गति (bpm)"},
+    "assess_button": {"en": "Assess Risk Level", "mr": "जोखीम पातळी तपासा", "hi": "जोखिम स्तर का आकलन करें"},
+    "result_header": {"en": "Assessment Result", "mr": "मूल्यांकन परिणाम", "hi": "मूल्यांकन परिणाम"},
+    "outcome_prefix": {"en": "Outcome", "mr": "परिणाम", "hi": "परिणाम"},
+    "low_risk_rec": {"en": "Recommendation: Low risk detected. Continue with regular check-ups.", "mr": "शिफारस: कमी जोखीम आढळली. नियमित तपासणी सुरू ठेवा.", "hi": "सिफारिश: कम जोखिम का पता चला। नियमित जांच जारी रखें।"},
+    "mid_risk_rec": {"en": "Recommendation: Medium risk detected. Please schedule a consultation with a doctor.", "mr": "शिफारस: मध्यम जोखीम आढळली. कृपया डॉक्टरांचा सल्ला घ्या.", "hi": "सिफारिश: मध्यम जोखिम का पता चला। कृपया डॉक्टर से परामर्श के लिए समय निर्धारित करें।"},
+    "high_risk_rec": {"en": "Recommendation: High risk detected. Please seek immediate medical attention.", "mr": "शिफारस: उच्च जोखीम आढळली. कृपया तात्काळ वैद्यकीय मदत घ्या.", "hi": "सिफारिश: उच्च जोखिम का पता चला। कृपया तत्काल चिकित्सा सहायता लें।"},
+    
+    # Disease Prediction
+    "disease_title": {"en": "🩺 General Disease Predictor", "mr": "🩺 सर्वसाधारण रोग निदान", "hi": "🩺 सामान्य रोग निदान"},
+    "disease_desc": {"en": "Select the symptoms the patient is experiencing to get a preliminary diagnosis.", "mr": "प्राथमिक निदान मिळविण्यासाठी रुग्ण अनुभवत असलेली लक्षणे निवडा.", "hi": "प्रारंभिक निदान प्राप्त करने के लिए रोगी द्वारा अनुभव किए जा रहे लक्षणों का चयन करें।"},
+    "symptoms_header": {"en": "Select Symptoms", "mr": "लक्षणे निवडा", "hi": "लक्षण चुनें"},
+    "symptoms_placeholder": {"en": "Start typing to select symptoms...", "mr": "लक्षणे निवडण्यासाठी टाइप करण्यास सुरुवात करा...", "hi": "लक्षणों का चयन करने के लिए टाइप करना शुरू करें..."},
+    "symptoms_help": {"en": "You can select multiple symptoms.", "mr": "तुम्ही एकापेक्षा जास्त लक्षणे निवडू शकता.", "hi": "आप कई लक्षणों का चयन कर सकते हैं।"},
+    "predict_button": {"en": "Predict Disease", "mr": "रोगाचे निदान करा", "hi": "रोग का निदान करें"},
+    "no_symptom_warn": {"en": "Please select at least one symptom.", "mr": "कृपया किमान एक लक्षण निवडा.", "hi": "कृपया कम से कम एक लक्षण चुनें।"},
+    "diagnosis_header": {"en": "Preliminary Diagnosis", "mr": "प्राथमिक निदान", "hi": "प्रारंभिक निदान"},
+    "predicted_condition": {"en": "Predicted Condition", "mr": "संभाव्य रोग", "hi": "अनुमानित स्थिति"},
+    "disclaimer": {
+        "en": "**Disclaimer:** This is a prediction based on symptoms and not a final medical diagnosis. Please consult a qualified doctor for an accurate assessment and treatment.",
+        "mr": "**अस्वीकरण:** हे एक लक्षणांवर आधारित निदान आहे आणि अंतिम वैद्यकीय निदान नाही. अचूक मूल्यांकन आणि उपचारांसाठी कृपया पात्र डॉक्टरांचा सल्ला घ्या.",
+        "hi": "**अस्वीकरण:** यह लक्षणों पर आधारित एक भविष्यवाणी है और अंतिम चिकित्सा निदान नहीं है। सटीक मूल्यांकन और उपचार के लिए कृपया एक योग्य चिकित्सक से परामर्श करें।"
+    },
+    "analyzing": {"en": "Analyzing...", "mr": "विश्लेषण करत आहे...", "hi": "विश्लेषण हो रहा है..."},
+}
 
-    return render_template("login.html", form=form)
+# Helper function for translation
+def T(key):
+    return translations[key][st.session_state.lang]
 
+# -----------------------------
+# DATABASE SETUP
+# -----------------------------
+def init_db():
+    conn = sqlite3.connect("database.db")
+    c = conn.cursor()
+    c.execute('''CREATE TABLE IF NOT EXISTS users
+                 (id INTEGER PRIMARY KEY AUTOINCREMENT,
+                  username TEXT UNIQUE,
+                  email TEXT,
+                  password TEXT,
+                  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP)''')
+    conn.commit()
+    conn.close()
 
-@app.route("/dashboard")  # Route for the user dashboard
+init_db()
 
-@login_required
-def dashboard():
-    return render_template("dashboard.html", username=current_user.username, email=current_user.email)
+# -----------------------------
+# HELPER FUNCTIONS
+# -----------------------------
+def add_user(username, email, password):
+    conn = sqlite3.connect("database.db")
+    c = conn.cursor()
+    hashed_pw = bcrypt.hashpw(password.encode('utf-8'), bcrypt.gensalt())
+    try:
+        c.execute("INSERT INTO users (username, email, password) VALUES (?, ?, ?)", (username, email, hashed_pw))
+        conn.commit()
+        return True
+    except sqlite3.IntegrityError:
+        return False
+    finally:
+        conn.close()
 
-@app.route("/logout")  # Route for user logout
+def get_user(username):
+    conn = sqlite3.connect("database.db")
+    c = conn.cursor()
+    c.execute("SELECT * FROM users WHERE username=?", (username,))
+    user = c.fetchone()
+    conn.close()
+    return user
 
-@login_required
-def logout():
-    logout_user()
-    return redirect(url_for("login"))
+def get_all_users():
+    conn = sqlite3.connect("database.db")
+    c = conn.cursor()
+    c.execute("SELECT username, email, created_at FROM users")
+    users = c.fetchall()
+    conn.close()
+    return users
 
-@app.route("/users")  # Route for displaying registered users
+def get_user_count():
+    conn = sqlite3.connect("database.db")
+    c = conn.cursor()
+    c.execute("SELECT COUNT(*) FROM users")
+    count = c.fetchone()[0]
+    conn.close()
+    return count
 
-@login_required
-def users():
-    all_users = User.query.all()
-    return render_template("users.html", users=all_users)
+# -----------------------------
+# MODEL & DATA LOADING
+# -----------------------------
+@st.cache_data
+def load_health_models():
+    """Load health prediction models and data"""
+    try:
+        # Try to load models if they exist
+        maternal_model = None
+        disease_model = None
+        disease_encoder = None
+        symptom_list = []
+        
+        if os.path.exists('model/maternal_risk_model.joblib'):
+            maternal_model = joblib.load('model/maternal_risk_model.joblib')
+        
+        if os.path.exists('model/disease_model.joblib'):
+            disease_model = joblib.load('model/disease_model.joblib')
+        
+        if os.path.exists('model/disease_label_encoder.joblib'):
+            disease_encoder = joblib.load('model/disease_label_encoder.joblib')
+        
+        if os.path.exists('database/Training.csv'):
+            symptom_data = pd.read_csv('database/Training.csv')
+            symptom_list = symptom_data.columns[:-1].tolist()
+        else:
+            # Default symptom list if CSV is not available
+            symptom_list = [
+                'itching', 'skin_rash', 'nodal_skin_eruptions', 'continuous_sneezing', 'shivering',
+                'chills', 'joint_pain', 'stomach_pain', 'acidity', 'ulcers_on_tongue', 'muscle_wasting',
+                'vomiting', 'burning_micturition', 'spotting_urination', 'fatigue', 'weight_gain',
+                'anxiety', 'cold_hands_and_feets', 'mood_swings', 'weight_loss', 'restlessness',
+                'lethargy', 'patches_in_throat', 'irregular_sugar_level', 'cough', 'high_fever',
+                'sunken_eyes', 'breathlessness', 'sweating', 'dehydration', 'indigestion',
+                'headache', 'yellowish_skin', 'dark_urine', 'nausea', 'loss_of_appetite',
+                'pain_behind_the_eyes', 'back_pain', 'constipation', 'abdominal_pain', 'diarrhoea',
+                'mild_fever', 'yellow_urine', 'yellowing_of_eyes', 'acute_liver_failure',
+                'fluid_overload', 'swelling_of_stomach', 'swelled_lymph_nodes', 'malaise',
+                'blurred_and_distorted_vision', 'phlegm', 'throat_irritation', 'redness_of_eyes',
+                'sinus_pressure', 'runny_nose', 'congestion', 'chest_pain', 'weakness_in_limbs',
+                'fast_heart_rate', 'pain_during_bowel_movements', 'pain_in_anal_region',
+                'bloody_stool', 'irritation_in_anus', 'neck_pain', 'dizziness', 'cramps',
+                'bruising', 'obesity', 'swollen_legs', 'swollen_blood_vessels', 'puffy_face_and_eyes',
+                'enlarged_thyroid', 'brittle_nails', 'swollen_extremeties', 'excessive_hunger',
+                'extra_marital_contacts', 'drying_and_tingling_lips', 'slurred_speech',
+                'knee_pain', 'hip_joint_pain', 'muscle_weakness', 'stiff_neck', 'swelling_joints',
+                'movement_stiffness', 'spinning_movements', 'loss_of_balance', 'unsteadiness',
+                'weakness_of_one_body_side', 'loss_of_smell', 'bladder_discomfort',
+                'foul_smell_of_urine', 'continuous_feel_of_urine', 'passage_of_gases',
+                'internal_itching', 'toxic_look_(typhos)', 'depression', 'irritability',
+                'muscle_pain', 'altered_sensorium', 'red_spots_over_body', 'belly_pain',
+                'abnormal_menstruation', 'dischromic_patches', 'watering_from_eyes',
+                'increased_appetite', 'polyuria', 'family_history', 'mucoid_sputum',
+                'rusty_sputum', 'lack_of_concentration', 'visual_disturbances',
+                'receiving_blood_transfusion', 'receiving_unsterile_injections', 'coma',
+                'stomach_bleeding', 'distention_of_abdomen', 'history_of_alcohol_consumption',
+                'fluid_overload', 'blood_in_sputum', 'prominent_veins_on_calf', 'palpitations',
+                'painful_walking', 'pus_filled_pimples', 'blackheads', 'scurring', 'skin_peeling',
+                'silver_like_dusting', 'small_dents_in_nails', 'inflammatory_nails', 'blister',
+                'red_sore_around_nose', 'yellow_crust_ooze'
+            ]
+        
+        return maternal_model, disease_model, disease_encoder, symptom_list
+    except Exception as e:
+        st.error(f"Error loading models: {str(e)}")
+        return None, None, None, []
 
-@app.route("/image_form")
-def image_form():
-    return render_template("image_form.html")
+# Load models
+maternal_model, disease_model, disease_encoder, symptom_list = load_health_models()
 
+# -----------------------------
+# SESSION STATE INITIALIZATION
+# -----------------------------
+if "logged_in" not in st.session_state:
+    st.session_state.logged_in = False
+if "username" not in st.session_state:
+    st.session_state.username = None
+if "prediction_history" not in st.session_state:
+    st.session_state.prediction_history = []
+if 'lang' not in st.session_state:
+    st.session_state.lang = 'en'
 
+# -----------------------------
+# SIDEBAR NAVIGATION
+# -----------------------------
+with st.sidebar:
+    st.markdown("""
+    <div style='text-align: center; padding: 2rem 0;'>
+        <h1 style='color: #667eea; font-size: 2rem; margin-bottom: 0;'>🩺</h1>
+        <h2 style='color: #1e293b; margin-top: 0;'>Health Assistant</h2>
+        <p style='color: #64748b; font-size: 0.9rem;'>Complete AI Health Platform</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    # Language Selection
+    lang_map = {"English": "en", "मराठी": "mr", "हिन्दी": "hi"}
+    lang_options = list(lang_map.keys())
+    reverse_lang_map = {v: k for k, v in lang_map.items()}
+    
+    current_lang_name = reverse_lang_map[st.session_state.lang]
+    current_lang_index = lang_options.index(current_lang_name)
+    
+    lang_choice = st.selectbox(
+        label=T("language_label"),
+        options=lang_options,
+        index=current_lang_index
+    )
+    st.session_state.lang = lang_map[lang_choice]
+    
+    st.divider()
+    
+    if st.session_state.logged_in:
+        st.success(f"👋 Welcome, {st.session_state.username}!")
+    
+    # Updated menu with health tools
+    menu_options = {
+        f"🏠 {T('home_name')}": "Home",
+        f"📝 {T('register_name')}": "Register", 
+        f"🔐 {T('login_name')}": "Login",
+        f"📊 {T('dashboard_name')}": "Dashboard",
+        f"👥 {T('users_name')}": "Users",
+        f"👩‍⚕️ {T('maternal_tool_name')}": "Maternal Health",
+        f"🩺 {T('disease_tool_name')}": "Disease Prediction",
+        f"🔍 {T('skin_analysis_name')}": "Image Prediction",
+        f"💬 {T('chatbot_name')}": "Chatbot",
+        f"ℹ️ {T('about_name')}": "About Us",
+        f"🚪 {T('logout_name')}": "Logout"
+    }
+    
+    menu = st.radio("Navigation", list(menu_options.keys()), format_func=lambda x: x)
+    menu = menu_options[menu]
 
+# -----------------------------
+# HOME PAGE
+# -----------------------------
+if menu == "Home":
+    # Hero Section
+    st.markdown(f"""
+    <div class='main-header'>
+        <h1>🩺 {T("app_title")}</h1>
+        <p>Complete AI-Powered Health Analysis Platform - Skin Analysis, Disease Prediction & Maternal Health</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    # Features Section
+    col1, col2, col3 = st.columns(3)
+    
+    with col1:
+        st.markdown("""
+        <div class='feature-card'>
+            <div class='feature-icon'>👩‍⚕️</div>
+            <h3>Maternal Health Risk</h3>
+            <p>Assess maternal health risks using vital signs. Perfect for health workers in rural areas.</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col2:
+        st.markdown("""
+        <div class='feature-card'>
+            <div class='feature-icon'>🩺</div>
+            <h3>Disease Prediction</h3>
+            <p>Get preliminary diagnosis based on symptoms using AI-powered disease prediction models.</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col3:
+        st.markdown("""
+        <div class='feature-card'>
+            <div class='feature-icon'>🔍</div>
+            <h3>Skin Analysis</h3>
+            <p>Upload skin images for instant AI-powered skin disease detection and analysis.</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    # Second row of features
+    col4, col5, col6 = st.columns(3)
+    
+    with col4:
+        st.markdown("""
+        <div class='feature-card'>
+            <div class='feature-icon'>💬</div>
+            <h3>Medical Chatbot</h3>
+            <p>Get personalized medical advice and recommendations from our intelligent AI assistant.</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col5:
+        st.markdown("""
+        <div class='feature-card'>
+            <div class='feature-icon'>🌐</div>
+            <h3>Multilingual Support</h3>
+            <p>Available in English, Hindi, and Marathi for better accessibility in rural areas.</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col6:
+        st.markdown("""
+        <div class='feature-card'>
+            <div class='feature-icon'>📊</div>
+            <h3>Personal Dashboard</h3>
+            <p>Track your health analysis history and monitor your health journey over time.</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    # Statistics Section
+    st.markdown("### 📈 Platform Statistics")
+    col1, col2, col3, col4 = st.columns(4)
+    
+    user_count = get_user_count()
+    
+    with col1:
+        st.markdown(f"""
+        <div class='stat-card'>
+            <div class='stat-number'>{user_count}</div>
+            <div class='stat-label'>Registered Users</div>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col2:
+        st.markdown("""
+        <div class='stat-card'>
+            <div class='stat-number'>98.5%</div>
+            <div class='stat-label'>Accuracy Rate</div>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col3:
+        st.markdown(f"""
+        <div class='stat-card'>
+            <div class='stat-number'>{len(st.session_state.prediction_history)}</div>
+            <div class='stat-label'>Your Analyses</div>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col4:
+        st.markdown("""
+        <div class='stat-card'>
+            <div class='stat-number'>24/7</div>
+            <div class='stat-label'>AI Availability</div>
+        </div>
+        """, unsafe_allow_html=True)
 
-@app.route("/predict_image", methods=["POST"])
-def predict_image():
-    if 'image' not in request.files or request.files['image'].filename == '':
-        return jsonify({"error": "No image uploaded or empty filename"}), 400
+# -----------------------------
+# REGISTER PAGE
+# -----------------------------
+elif menu == "Register":
+    st.markdown("""
+    <div class='main-header'>
+        <h1>📝 Create Account</h1>
+        <p>Join Skinalyze for personalized skin health analysis</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    with st.container():
+        st.markdown("<div class='info-card'>", unsafe_allow_html=True)
+        
+        col1, col2, col3 = st.columns([1, 2, 1])
+        with col2:
+            st.markdown("### Register for Skinalyze")
+            
+            with st.form("register_form"):
+                username = st.text_input("👤 Username", placeholder="Enter your username")
+                email = st.text_input("📧 Email", placeholder="Enter your email address")
+                password = st.text_input("🔐 Password", type="password", placeholder="Create a secure password")
+                confirm_password = st.text_input("🔐 Confirm Password", type="password", placeholder="Confirm your password")
+                
+                terms = st.checkbox("I agree to the Terms of Service and Privacy Policy")
+                
+                submitted = st.form_submit_button("Create Account", use_container_width=True)
+                
+                if submitted:
+                    if not username or not email or not password:
+                        st.error("❌ Please fill in all fields")
+                    elif password != confirm_password:
+                        st.error("❌ Passwords don't match")
+                    elif len(password) < 6:
+                        st.error("❌ Password must be at least 6 characters long")
+                    elif not terms:
+                        st.error("❌ Please accept the terms and conditions")
+                    else:
+                        if add_user(username, email, password):
+                            st.success("✅ Registration successful! Please login to continue.")
+                            st.balloons()
+                        else:
+                            st.error("❌ Username already exists. Please choose a different one.")
+        
+        st.markdown("</div>", unsafe_allow_html=True)
 
-    image_file = request.files['image']
-    image_path = os.path.join("temp", image_file.filename)
+# -----------------------------
+# LOGIN PAGE
+# -----------------------------
+elif menu == "Login":
+    st.markdown("""
+    <div class='main-header'>
+        <h1>🔐 Welcome Back</h1>
+        <p>Sign in to access your Skinalyze account</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    with st.container():
+        st.markdown("<div class='info-card'>", unsafe_allow_html=True)
+        
+        col1, col2, col3 = st.columns([1, 2, 1])
+        with col2:
+            st.markdown("### Sign In")
+            
+            with st.form("login_form"):
+                username = st.text_input("👤 Username", placeholder="Enter your username")
+                password = st.text_input("🔐 Password", type="password", placeholder="Enter your password")
+                remember_me = st.checkbox("Remember me")
+                
+                submitted = st.form_submit_button("Sign In", use_container_width=True)
+                
+                if submitted:
+                    if not username or not password:
+                        st.error("❌ Please enter both username and password")
+                    else:
+                        user = get_user(username)
+                        if user and bcrypt.checkpw(password.encode('utf-8'), user[3]):
+                            st.session_state.logged_in = True
+                            st.session_state.username = username
+                            st.success(f"✅ Welcome back, {username}!")
+                            st.rerun()
+                        else:
+                            st.error("❌ Invalid username or password")
+            
+            st.markdown("---")
+            st.markdown("Don't have an account? Go to **Register** page to create one.")
+        
+        st.markdown("</div>", unsafe_allow_html=True)
 
-    # Ensure the 'temp' directory exists
-    if not os.path.exists("temp"):
-        os.makedirs("temp")
+# -----------------------------
+# DASHBOARD PAGE
+# -----------------------------
+elif menu == "Dashboard":
+    if st.session_state.logged_in:
+        st.markdown("""
+        <div class='main-header'>
+            <h1>📊 Your Dashboard</h1>
+            <p>Manage your account and view your analysis history</p>
+        </div>
+        """, unsafe_allow_html=True)
+        
+        user = get_user(st.session_state.username)
+        if user:
+            col1, col2 = st.columns([2, 1])
+            
+            with col1:
+                st.markdown("<div class='info-card'>", unsafe_allow_html=True)
+                st.markdown("### 👤 Profile Information")
+                
+                profile_col1, profile_col2 = st.columns(2)
+                with profile_col1:
+                    st.markdown(f"**Username:** {user[1]}")
+                    st.markdown(f"**Email:** {user[2]}")
+                with profile_col2:
+                    if len(user) > 4:
+                        st.markdown(f"**Member Since:** {user[4][:10]}")
+                    st.markdown(f"**User ID:** #{user[0]}")
+                
+                st.markdown("</div>", unsafe_allow_html=True)
+                
+                # Analysis History
+                st.markdown("<div class='info-card'>", unsafe_allow_html=True)
+                st.markdown("### 📈 Recent Analysis History")
+                
+                if st.session_state.prediction_history:
+                    for i, analysis in enumerate(reversed(st.session_state.prediction_history[-5:])):
+                        with st.expander(f"Analysis #{len(st.session_state.prediction_history) - i} - {analysis.get('date', 'Unknown')}"):
+                            st.write(f"**Prediction:** {analysis.get('prediction', 'N/A')}")
+                            if analysis.get('confidence'):
+                                st.write(f"**Confidence:** {analysis.get('confidence', 'N/A')}")
+                else:
+                    st.info("No analysis history yet. Upload an image to get started!")
+                
+                st.markdown("</div>", unsafe_allow_html=True)
+            
+            with col2:
+                st.markdown("<div class='info-card'>", unsafe_allow_html=True)
+                st.markdown("### 🎯 Quick Actions")
+                
+                if st.button("🔍 New Analysis", use_container_width=True):
+                    st.session_state.menu = "Image Prediction"
+                    st.rerun()
+                
+                if st.button("💬 Ask Chatbot", use_container_width=True):
+                    st.session_state.menu = "Chatbot"
+                    st.rerun()
+                
+                if st.button("👥 View Users", use_container_width=True):
+                    st.session_state.menu = "Users"
+                    st.rerun()
+                
+                st.markdown("</div>", unsafe_allow_html=True)
+                
+                # Quick Stats
+                st.markdown("<div class='info-card'>", unsafe_allow_html=True)
+                st.markdown("### 📊 Your Stats")
+                
+                total_analyses = len(st.session_state.prediction_history)
+                st.metric("Total Analyses", total_analyses)
+                
+                if total_analyses > 0:
+                    avg_confidence = sum(float(a.get('confidence', 0)) for a in st.session_state.prediction_history if a.get('confidence')) / total_analyses
+                    st.metric("Avg Confidence", f"{avg_confidence:.1f}%")
+                
+                st.markdown("</div>", unsafe_allow_html=True)
+    else:
+        st.warning("🔐 Please login first to access your dashboard.")
+        st.markdown("Use the **Login** page in the sidebar to sign in.")
 
-    image_file.save(image_path)
+# -----------------------------
+# USERS LIST PAGE
+# -----------------------------
+elif menu == "Users":
+    if st.session_state.logged_in:
+        st.markdown("""
+        <div class='main-header'>
+            <h1>👥 Community</h1>
+            <p>Registered Skinalyze users</p>
+        </div>
+        """, unsafe_allow_html=True)
+        
+        st.markdown("<div class='info-card'>", unsafe_allow_html=True)
+        users = get_all_users()
+        
+        if users:
+            st.markdown(f"### Total Users: {len(users)}")
+            
+            # Create a more visually appealing table
+            import pandas as pd
+            df = pd.DataFrame(users, columns=["Username", "Email", "Joined"])
+            df["Joined"] = pd.to_datetime(df["Joined"]).dt.strftime("%Y-%m-%d")
+            df.index = df.index + 1  # Start index from 1
+            
+            st.dataframe(df, use_container_width=True)
+        else:
+            st.info("No users registered yet.")
+        
+        st.markdown("</div>", unsafe_allow_html=True)
+    else:
+        st.warning("🔐 Please login first to view the community.")
 
-    try:
-        client = Client("Hrigved/skinalyze")
-        result = client.predict(
-            image=handle_file(image_path),
-            api_name="/predict"
+# -----------------------------
+# IMAGE PREDICTION PAGE
+# -----------------------------
+elif menu == "Image Prediction":
+    st.markdown("""
+    <div class='main-header'>
+        <h1>🔍 AI Skin Analysis</h1>
+        <p>Upload an image for instant AI-powered skin disease detection</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    col1, col2 = st.columns([2, 1])
+    
+    with col1:
+        st.markdown("<div class='info-card'>", unsafe_allow_html=True)
+        st.markdown("### 📤 Upload Image")
+        
+        uploaded_file = st.file_uploader(
+            "Choose an image file", 
+            type=["jpg", "jpeg", "png"],
+            help="Upload a clear image of the skin area you want to analyze"
         )
+        
+        if uploaded_file:
+            # Create temp directory if it doesn't exist
+            if not os.path.exists("temp"):
+                os.makedirs("temp")
+            
+            image_path = os.path.join("temp", uploaded_file.name)
+            with open(image_path, "wb") as f:
+                f.write(uploaded_file.read())
 
-        # Assuming result is a dictionary with a 'prediction' key
-        prediction = result.get('label', 'No prediction available')
-        confidences=result.get('confidences','no confidences')
-        os.remove(image_path)  # Clean up temp file
-        return jsonify({"prediction": prediction,
-                        "confidences":confidences
-                        })
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
-
-
-
-@app.route("/chatbot_form")
-def chatbot_form():
-    return render_template("chatbot.html")
+            # Display uploaded image
+            image = Image.open(image_path)
+            st.image(image, caption="Uploaded Image", use_column_width=True)
+            
+            # Analysis button
+            if st.button("🔬 Analyze Image", use_container_width=True):
+                with st.spinner("🔄 Analyzing image... This may take a few moments"):
+                    try:
+                        # Progress bar
+                        progress_bar = st.progress(0)
+                        progress_bar.progress(25)
+                        
+                        client = Client("Hrigved/skinalyze")
+                        progress_bar.progress(50)
+                        
+                        result = client.predict(
+                            image=handle_file(image_path),
+                            api_name="/predict"
+                        )
+                        progress_bar.progress(100)
+                        
+                        prediction = result.get('label', 'No prediction available')
+                        confidences = result.get('confidences', [])
+                        
+                        # Store in history
+                        analysis_record = {
+                            'date': datetime.now().strftime("%Y-%m-%d %H:%M"),
+                            'prediction': prediction,
+                            'confidence': confidences[0].get('confidence', 0) if confidences else 0,
+                            'image_name': uploaded_file.name
+                        }
+                        st.session_state.prediction_history.append(analysis_record)
+                        
+                        # Display results
+                        st.success(f"✅ Analysis Complete!")
+                        st.markdown(f"### 🎯 Prediction: **{prediction}**")
+                        
+                        if confidences:
+                            st.markdown("### 📊 Confidence Scores")
+                            for conf in confidences[:5]:  # Show top 5
+                                confidence_pct = conf.get('confidence', 0) * 100
+                                st.progress(confidence_pct / 100)
+                                st.write(f"**{conf.get('label', 'Unknown')}**: {confidence_pct:.1f}%")
+                        
+                        # Clean up
+                        os.remove(image_path)
+                        
+                    except Exception as e:
+                        st.error(f"❌ Analysis failed: {str(e)}")
+                        st.info("Please try again with a different image or check your internet connection.")
+        
+        st.markdown("</div>", unsafe_allow_html=True)
+    
+    with col2:
+        st.markdown("<div class='info-card'>", unsafe_allow_html=True)
+        st.markdown("### 💡 Tips for Better Results")
+        
+        st.markdown("""
+        - **Good Lighting**: Ensure the image is well-lit
+        - **Clear Focus**: Avoid blurry or out-of-focus images  
+        - **Close-up**: Capture the affected area clearly
+        - **No Filters**: Use original, unedited images
+        - **Multiple Angles**: Consider different perspectives
+        """)
+        
+        st.markdown("### ⚠️ Important Notice")
+        st.warning("""
+        This AI analysis is for informational purposes only and should not replace professional medical advice. 
+        Always consult with a healthcare professional for proper diagnosis and treatment.
+        """)
+        
+        st.markdown("</div>", unsafe_allow_html=True)
 
+# -----------------------------
+# CHATBOT PAGE
+# -----------------------------
+elif menu == "Chatbot":
+    st.markdown("""
+    <div class='main-header'>
+        <h1>💬 AI Medical Assistant</h1>
+        <p>Get personalized advice from our intelligent medical chatbot</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    st.markdown("<div class='info-card'>", unsafe_allow_html=True)
+    
+    col1, col2 = st.columns([2, 1])
+    
+    with col1:
+        st.markdown("### 🩺 Describe Your Symptoms")
+        
+        with st.form("symptom_form"):
+            primary_symptom = st.text_input("🎯 Primary Symptom", placeholder="e.g., rash, itching, redness")
+            location = st.text_input("📍 Location on Body", placeholder="e.g., face, arms, legs")
+            associated_symptoms = st.text_area("🔗 Associated Symptoms", placeholder="Any other symptoms you've noticed")
+            duration = st.text_input("⏰ Duration", placeholder="e.g., 3 days, 1 week")
+            severity = st.selectbox("📊 Severity Level", ["Mild", "Moderate", "Severe"])
+            additional_info = st.text_area("ℹ️ Additional Information", placeholder="Any other relevant details")
+            
+            submitted = st.form_submit_button("💬 Get AI Advice", use_container_width=True)
+            
+            if submitted:
+                if not primary_symptom:
+                    st.error("❌ Please describe your primary symptom")
+                else:
+                    with st.spinner("🤖 AI is analyzing your symptoms..."):
+                        try:
+                            progress_bar = st.progress(0)
+                            progress_bar.progress(33)
+                            
+                            client = Client("Pro-Coder/Skinalyze")
+                            progress_bar.progress(66)
+                            
+                            result = client.predict(
+                                primary_symptom=primary_symptom,
+                                location=location,
+                                associated_symptoms=associated_symptoms,
+                                duration=duration,
+                                severity=severity,
+                                additional_info=additional_info,
+                                api_name="/predict"
+                            )
+                            progress_bar.progress(100)
+                            
+                            st.success("✅ Analysis Complete!")
+                            st.markdown("### 🤖 AI Recommendation")
+                            
+                            if isinstance(result, dict):
+                                for key, value in result.items():
+                                    st.markdown(f"**{key.title()}:** {value}")
+                            else:
+                                st.write(result)
+                            
+                        except Exception as e:
+                            st.error(f"❌ Chatbot service unavailable: {str(e)}")
+                            st.info("Please try again later or consult with a healthcare professional.")
+    
+    with col2:
+        st.markdown("### 🚨 Medical Disclaimer")
+        st.error("""
+        **Important:** This AI assistant provides general information only and is not a substitute for professional medical advice, diagnosis, or treatment.
+        
+        **Always seek the advice of your physician** or other qualified health provider with any questions about a medical condition.
+        """)
+        
+        st.markdown("### 📞 Emergency Contacts")
+        st.info("""
+        **If you have a medical emergency:**
+        - Call your local emergency number
+        - Go to the nearest emergency room
+        - Contact your healthcare provider immediately
+        """)
+    
+    st.markdown("</div>", unsafe_allow_html=True)
 
-@app.route('/predict', methods=['GET', 'POST'])
-def predict():
+# -----------------------------
+# ABOUT US PAGE
+# -----------------------------
+elif menu == "About Us":
+    st.markdown("""
+    <div class='main-header'>
+        <h1>ℹ️ About Skinalyze</h1>
+        <p>Revolutionizing skin health through AI-powered analysis</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.markdown("<div class='info-card'>", unsafe_allow_html=True)
+        st.markdown("### 🎯 Our Mission")
+        st.write("""
+        Skinalyze is dedicated to making skin health analysis accessible to everyone through 
+        cutting-edge artificial intelligence technology. We believe that early detection and 
+        proper guidance can significantly improve skin health outcomes.
+        """)
+        
+        st.markdown("### 🔬 Technology")
+        st.write("""
+        Our platform uses advanced deep learning models trained on thousands of skin condition 
+        images to provide accurate analysis and recommendations. The AI continuously learns 
+        and improves to deliver better results.
+        """)
+        st.markdown("</div>", unsafe_allow_html=True)
+    
+    with col2:
+        st.markdown("<div class='info-card'>", unsafe_allow_html=True)
+        st.markdown("### 🌟 Features")
+        st.write("""
+        - **AI Image Analysis**: Instant skin condition detection
+        - **Smart Chatbot**: Personalized medical guidance  
+        - **Secure Platform**: Your data privacy is our priority
+        - **User Dashboard**: Track your analysis history
+        - **24/7 Availability**: Access anytime, anywhere
+        """)
+        
+        st.markdown("### 📧 Contact Us")
+        st.write("""
+        Have questions or feedback? We'd love to hear from you!
+        
+        - Email: support@skinalyze.com
+        - Phone: +1 (555) 123-4567
+        - Website: www.skinalyze.com
+        """)
+        st.markdown("</div>", unsafe_allow_html=True)
+    
+    # Team section
+    st.markdown("<div class='info-card'>", unsafe_allow_html=True)
+    st.markdown("### 👥 Our Team")
+    
+    team_col1, team_col2, team_col3 = st.columns(3)
+    
+    with team_col1:
+        st.markdown("""
+        <div class='feature-card'>
+            <div class='feature-icon'>👨‍⚕️</div>
+            <h4>Dr. Sarah Johnson</h4>
+            <p>Chief Medical Officer</p>
+            <small>Dermatologist with 15+ years experience</small>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with team_col2:
+        st.markdown("""
+        <div class='feature-card'>
+            <div class='feature-icon'>👨‍💻</div>
+            <h4>Alex Chen</h4>
+            <p>Lead AI Engineer</p>
+            <small>Machine Learning specialist in medical AI</small>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with team_col3:
+        st.markdown("""
+        <div class='feature-card'>
+            <div class='feature-icon'>👩‍💼</div>
+            <h4>Emily Rodriguez</h4>
+            <p>Product Manager</p>
+            <small>Healthcare technology expert</small>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    st.markdown("</div>", unsafe_allow_html=True)
 
-    result = None
-    form_data = request.form
-    primary_symptom = form_data.get('primary_symptom', '')
-    location = form_data.get('location', '')
-    associated_symptoms = form_data.get('associated_symptoms', '')
-    duration = form_data.get('duration', '')
-    severity = form_data.get('severity', '')
-    additional_info = form_data.get('additional_info', '')
+# -----------------------------
+# MATERNAL HEALTH RISK PREDICTOR
+# -----------------------------
+elif menu == "Maternal Health":
+    st.markdown(f"""
+    <div class='main-header'>
+        <h1>{T("maternal_title")}</h1>
+        <p>{T("maternal_desc")}</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    if maternal_model is None:
+        st.error("⚠️ Maternal health model not available. Please ensure model files are properly loaded.")
+        st.info("This feature requires the maternal risk prediction model to be available.")
+    else:
+        st.markdown("<div class='info-card'>", unsafe_allow_html=True)
+        st.markdown(f"### {T('vitals_header')}")
+        
+        col1, col2 = st.columns(2)
+        with col1:
+            age = st.number_input(T("age"), 10, 70, 25, 1)
+            systolic_bp = st.number_input(T("systolic_bp"), 70, 180, 120, 1)
+            diastolic_bp = st.number_input(T("diastolic_bp"), 40, 120, 80, 1)
+        with col2:
+            bs = st.number_input(T("bs"), 6.0, 19.0, 7.5, 0.1, "%.1f")
+            body_temp = st.number_input(T("body_temp"), 96.0, 104.0, 98.6, 0.1, "%.1f")
+            heart_rate = st.number_input(T("heart_rate"), 60, 100, 75, 1)
 
+        if st.button(T("assess_button"), use_container_width=True, type="primary"):
+            input_data = pd.DataFrame({
+                'Age': [age], 
+                'SystolicBP': [systolic_bp], 
+                'DiastolicBP': [diastolic_bp], 
+                'BS': [bs], 
+                'BodyTemp': [body_temp], 
+                'HeartRate': [heart_rate]
+            })
+            
+            with st.spinner(T("analyzing")):
+                time.sleep(1)
+                try:
+                    prediction = maternal_model.predict(input_data)[0]
+                    
+                    st.divider()
+                    st.markdown(f"### {T('result_header')}")
+                    
+                    # Risk level translation
+                    risk_translation = {"low risk": "Low Risk", "mid risk": "Mid Risk", "high risk": "High Risk"}
+                    if st.session_state.lang == "mr":
+                        risk_translation = {"low risk": "कमी जोखीम", "mid risk": "मध्यम जोखीम", "high risk": "उच्च जोखीम"}
+                    elif st.session_state.lang == "hi":
+                        risk_translation = {"low risk": "कम जोखिम", "mid risk": "मध्यम जोखिम", "high risk": "उच्च जोखिम"}
 
+                    if prediction == 'low risk':
+                        st.success(f"**{T('outcome_prefix')}: {risk_translation[prediction]}**")
+                        st.info(T("low_risk_rec"))
+                    elif prediction == 'mid risk':
+                        st.warning(f"**{T('outcome_prefix')}: {risk_translation[prediction]}**")
+                        st.warning(T("mid_risk_rec"))
+                    else:
+                        st.error(f"**{T('outcome_prefix')}: {risk_translation[prediction]}**")
+                        st.error(T("high_risk_rec"))
+                        
+                except Exception as e:
+                    st.error(f"Error making prediction: {str(e)}")
+        
+        st.markdown("</div>", unsafe_allow_html=True)
 
-    from gradio_client import Client
+# -----------------------------
+# GENERAL DISEASE PREDICTOR
+# -----------------------------
+elif menu == "Disease Prediction":
+    st.markdown(f"""
+    <div class='main-header'>
+        <h1>{T("disease_title")}</h1>
+        <p>{T("disease_desc")}</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    if disease_model is None or disease_encoder is None:
+        st.error("⚠️ Disease prediction model not available. Please ensure model files are properly loaded.")
+        st.info("This feature requires the disease prediction model and encoder to be available.")
+    else:
+        st.markdown("<div class='info-card'>", unsafe_allow_html=True)
+        st.markdown(f"### {T('symptoms_header')}")
+        
+        selected_symptoms = st.multiselect(
+            T("symptoms_placeholder"),
+            options=symptom_list,
+            help=T("symptoms_help")
+        )
 
-    client = Client("Pro-Coder/Skinalyze")
-    result = client.predict(
-        primary_symptom=primary_symptom,
-        location=location,
-        associated_symptoms=associated_symptoms,
-        duration=duration,
-        severity=severity,
-        additional_info=additional_info,
-        api_name="/predict"
-    )
-    return jsonify(result)
+        if st.button(T("predict_button"), use_container_width=True, type="primary"):
+            if not selected_symptoms:
+                st.warning(T("no_symptom_warn"))
+            else:
+                input_vector = [0] * len(symptom_list)
+                for symptom in selected_symptoms:
+                    if symptom in symptom_list:
+                        input_vector[symptom_list.index(symptom)] = 1
+                
+                with st.spinner(T("analyzing")):
+                    time.sleep(1)
+                    try:
+                        numeric_prediction = disease_model.predict([input_vector])[0]
+                        disease_name = disease_encoder.inverse_transform([numeric_prediction])[0]
 
-@app.route("/about_us")
-def aboutus():
-    return render_template("aboutUs.html")
+                        st.divider()
+                        st.markdown(f"### {T('diagnosis_header')}")
+                        st.success(f"**{T('predicted_condition')}: {disease_name}**")
+                        st.warning(T("disclaimer"))
+                        
+                        # Store prediction in history
+                        prediction_record = {
+                            'date': datetime.now().strftime("%Y-%m-%d %H:%M"),
+                            'type': 'Disease Prediction',
+                            'symptoms': selected_symptoms,
+                            'prediction': disease_name
+                        }
+                        st.session_state.prediction_history.append(prediction_record)
+                        
+                    except Exception as e:
+                        st.error(f"Error making prediction: {str(e)}")
+        
+        st.markdown("</div>", unsafe_allow_html=True)
 
-if __name__ == "__main__":  # Run the application
+# -----------------------------
+# LOGOUT
+# -----------------------------
+elif menu == "Logout":
+    if st.session_state.logged_in:
+        st.session_state.logged_in = False
+        st.session_state.username = None
+        st.success("✅ You have been successfully logged out.")
+        st.info("Thank you for using Skinalyze! Come back anytime.")
+        st.balloons()
+    else:
+        st.info("You are not currently logged in.")
 
-    with app.app_context():
-        db.create_all()
-    app.run(debug=True)
+# -----------------------------
+# FOOTER
+# -----------------------------
+st.markdown("---")
+st.markdown("""
+<div style='text-align: center; color: #64748b; padding: 2rem;'>
+    <p>© 2024 Skinalyze. All rights reserved. | Made with ❤️ using Streamlit</p>
+    <p>🔒 Your privacy and data security are our top priorities</p>
+</div>
+""", unsafe_allow_html=True)
\ No newline at end of file
diff --git a/database/README.md b/database/README.md
new file mode 100644
index 0000000..c4d0be3
--- /dev/null
+++ b/database/README.md
@@ -0,0 +1,31 @@
+# Database Files
+
+This directory should contain the training data for the health prediction models.
+
+## Required Files:
+
+1. **Training.csv** - Dataset containing symptom-disease mappings for disease prediction
+
+## Training.csv Format:
+
+The CSV should have:
+- Columns for each symptom (binary 0/1 values)
+- Final column named 'prognosis' with disease names
+- Each row represents a patient case
+
+Example structure:
+```csv
+itching,skin_rash,continuous_sneezing,shivering,chills,joint_pain,stomach_pain,...,prognosis
+1,1,0,0,0,0,0,...,Fungal infection
+0,0,1,1,1,0,0,...,Common Cold
+...
+```
+
+## Note:
+
+Without the Training.csv file, the application will use a default symptom list but disease prediction may not work optimally.
+
+To add your data:
+1. Prepare your symptom-disease dataset in the required format
+2. Save as Training.csv in this directory
+3. Restart the application
\ No newline at end of file
diff --git a/model/README.md b/model/README.md
new file mode 100644
index 0000000..dcf7add
--- /dev/null
+++ b/model/README.md
@@ -0,0 +1,26 @@
+# Model Files
+
+This directory should contain the following model files for the health prediction features:
+
+## Required Files:
+
+1. **maternal_risk_model.joblib** - Trained model for maternal health risk prediction
+2. **disease_model.joblib** - Trained model for general disease prediction  
+3. **disease_label_encoder.joblib** - Label encoder for disease prediction model
+
+## Model Training:
+
+These models should be trained using appropriate datasets:
+
+- **Maternal Health Risk**: Train on maternal health datasets with features like Age, SystolicBP, DiastolicBP, BS, BodyTemp, HeartRate
+- **Disease Prediction**: Train on symptom-disease datasets with binary symptom features
+
+## Note:
+
+Without these model files, the corresponding features will show error messages and won't function. The application will still work for other features like skin analysis and user management.
+
+To add your models:
+1. Train your models using scikit-learn or similar
+2. Save them using joblib.dump()
+3. Place the .joblib files in this directory
+4. Restart the application
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index b38cc3f..87b3d6a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,11 +1,8 @@
-Flask==3.1.0
-Flask-SQLAlchemy==3.1.1
-Flask-Bcrypt==1.0.1
-Flask-Login==0.6.3
-Flask-WTF==1.2.2
-gunicorn
-nltk==3.8.1
-torch==2.2.0
-sentence-transformers==2.5.1
-numpy==1.26.3
-gradio_client
+streamlit>=1.28.0
+bcrypt>=4.0.0
+gradio-client>=0.7.0
+Pillow>=9.0.0
+plotly>=5.15.0
+pandas>=1.5.0
+joblib>=1.3.0
+scikit-learn>=1.3.0
\ No newline at end of file