Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,1438 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+import os
 import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import numpy as np
+import datetime
+from typing import Dict, List, Optional, Tuple
+import json
+from dataclasses import dataclass, asdict
+import google.generativeai as genai
+from PIL import Image
+import pytesseract
+import keras
+from huggingface_hub import hf_hub_download
+import warnings
+warnings.filterwarnings("ignore")
+
+# =============================================================================
+# CONFIGURATION FOR HUGGING FACE SPACES
+# =============================================================================
+
+# Configure Streamlit page
+st.set_page_config(
+    page_title="🏥 MedAI Suite - AI-Powered Medical Platform",
+    page_icon="🏥",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Get API key from HF Secrets
+try:
+    GEMINI_API_KEY = st.secrets["GEMINI_API_KEY"]
+    genai.configure(api_key=GEMINI_API_KEY)
+except:
+    st.error("🔑 Gemini API key not found in secrets. Please configure it in HF Space settings.")
+    st.stop()
+
+# Custom CSS for professional medical UI
+def load_custom_css():
+    st.markdown("""
+    <style>
+    /* Main theme colors */
+    :root {
+        --primary-color: #667eea;
+        --secondary-color: #764ba2;
+        --success-color: #28a745;
+        --warning-color: #ffc107;
+        --danger-color: #dc3545;
+        --info-color: #17a2b8;
+    }
+    
+    /* Header styling */
+    .main-header {
+        background: linear-gradient(135deg, var(--primary-color) 0%, var(--secondary-color) 100%);
+        padding: 2rem;
+        border-radius: 12px;
+        color: white;
+        text-align: center;
+        margin-bottom: 2rem;
+        box-shadow: 0 4px 15px rgba(0,0,0,0.1);
+    }
+    
+    .main-header h1 {
+        margin: 0;
+        font-size: 2.5rem;
+        font-weight: 700;
+    }
+    
+    .main-header p {
+        margin: 0.5rem 0 0 0;
+        font-size: 1.1rem;
+        opacity: 0.9;
+    }
+    
+    /* Feature cards */
+    .feature-card {
+        background: #f8f9fa;
+        padding: 1.5rem;
+        border-radius: 12px;
+        border-left: 4px solid var(--primary-color);
+        margin: 1rem 0;
+        box-shadow: 0 2px 10px rgba(0,0,0,0.05);
+        transition: transform 0.2s ease;
+    }
+    
+    .feature-card:hover {
+        transform: translateY(-2px);
+        box-shadow: 0 4px 20px rgba(0,0,0,0.1);
+    }
+    
+    /* Metric cards */
+    .metric-card {
+        background: linear-gradient(135deg, var(--primary-color) 0%, var(--secondary-color) 100%);
+        padding: 1.5rem;
+        border-radius: 12px;
+        color: white;
+        text-align: center;
+        box-shadow: 0 4px 15px rgba(0,0,0,0.1);
+        margin-bottom: 1rem;
+    }
+    
+    .metric-card h3 {
+        margin: 0 0 0.5rem 0;
+        font-size: 1rem;
+        opacity: 0.9;
+    }
+    
+    .metric-card h2 {
+        margin: 0;
+        font-size: 2rem;
+        font-weight: 700;
+    }
+    
+    .metric-card small {
+        opacity: 0.8;
+        font-size: 0.9rem;
+    }
+    
+    /* Button styling */
+    .stButton > button {
+        background: linear-gradient(90deg, var(--primary-color) 0%, var(--secondary-color) 100%);
+        color: white;
+        border: none;
+        border-radius: 8px;
+        padding: 0.7rem 1.5rem;
+        font-weight: 600;
+        transition: all 0.2s ease;
+    }
+    
+    .stButton > button:hover {
+        transform: translateY(-1px);
+        box-shadow: 0 4px 15px rgba(102, 126, 234, 0.3);
+    }
+    
+    /* Alert styling */
+    .success-alert {
+        background: linear-gradient(90deg, #d4edda, #c3e6cb);
+        border-left: 4px solid var(--success-color);
+        padding: 1rem;
+        border-radius: 8px;
+        margin: 1rem 0;
+    }
+    
+    .warning-alert {
+        background: linear-gradient(90deg, #fff3cd, #ffeaa7);
+        border-left: 4px solid var(--warning-color);
+        padding: 1rem;
+        border-radius: 8px;
+        margin: 1rem 0;
+    }
+    
+    .error-alert {
+        background: linear-gradient(90deg, #f8d7da, #f5c6cb);
+        border-left: 4px solid var(--danger-color);
+        padding: 1rem;
+        border-radius: 8px;
+        margin: 1rem 0;
+    }
+    
+    /* Chat styling */
+    .chat-message {
+        padding: 1rem;
+        border-radius: 12px;
+        margin: 1rem 0;
+    }
+    
+    .user-message {
+        background: linear-gradient(135deg, #e3f2fd, #bbdefb);
+        border-left: 4px solid #2196f3;
+    }
+    
+    .assistant-message {
+        background: linear-gradient(135deg, #f3e5f5, #e1bee7);
+        border-left: 4px solid #9c27b0;
+    }
+    
+    /* Hide Streamlit default elements */
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    header {visibility: hidden;}
+    
+    /* Professional medical disclaimer */
+    .medical-disclaimer {
+        background: linear-gradient(135deg, #fff3e0, #ffe0b2);
+        border: 2px solid #ff9800;
+        border-radius: 12px;
+        padding: 1.5rem;
+        margin: 2rem 0;
+        text-align: center;
+    }
+    
+    .medical-disclaimer h4 {
+        color: #e65100;
+        margin-bottom: 1rem;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+# =============================================================================
+# DATA CLASSES
+# =============================================================================
+
+@dataclass
+class UserProfile:
+    """Enhanced user medical profile"""
+    user_id: str
+    name: str
+    age: int
+    height: float  # cm
+    weight: float  # kg
+    sex: str
+    blood_sugar: float  # mg/dL
+    blood_pressure: str  # systolic/diastolic
+    past_medications: List[str]
+    current_conditions: List[str]
+    allergies: List[str]
+    created_at: str
+    bmi: float = 0.0
+
+    def __post_init__(self):
+        if self.height > 0:
+            height_m = self.height / 100
+            self.bmi = round(self.weight / (height_m ** 2), 2)
+
+# =============================================================================
+# MEDICAL AI BACKEND
+# =============================================================================
+
+class MedicalAI:
+    """Complete Medical AI System"""
+    
+    def __init__(self):
+        self.user_profiles = {}
+        self.conversation_history = {}
+        self.gemini_model = None
+        self.dr_model = None
+        self.dr_model_loaded = False
+        
+        # Initialize Gemini
+        self._init_gemini()
+        
+        # Initialize DR model
+        self._init_dr_model()
+    
+    def _init_gemini(self):
+        """Initialize Gemini model"""
+        try:
+            self.gemini_model = genai.GenerativeModel("gemini-2.0-flash")
+            st.success("✅ Gemini AI connected successfully")
+        except Exception as e:
+            st.error(f"❌ Gemini connection failed: {str(e)}")
+    
+    def _init_dr_model(self):
+        """Initialize diabetic retinopathy model"""
+        try:
+            with st.spinner("🧠 Loading Diabetic Retinopathy AI model..."):
+                repo_id = "Arko007/diabetic-retinopathy-v1"
+                weights_filename = "best_model.h5"
+                local_weights_path = hf_hub_download(repo_id=repo_id, filename=weights_filename)
+                self.dr_model = keras.saving.load_model(local_weights_path)
+                self.dr_model_loaded = True
+                st.success("✅ Eye screening AI model loaded successfully")
+        except Exception as e:
+            st.warning(f"⚠️ Eye screening model unavailable: {str(e)}")
+            self.dr_model_loaded = False
+    
+    def validate_user_input(self, user_data: Dict) -> Tuple[bool, List[str]]:
+        """Validate user profile data"""
+        errors = []
+        
+        required_fields = ['name', 'age', 'height', 'weight', 'sex']
+        for field in required_fields:
+            if not user_data.get(field):
+                errors.append(f"❌ {field.title()} is required")
+        
+        # Age validation
+        try:
+            age = int(user_data.get('age', 0))
+            if not 1 <= age <= 150:
+                errors.append("❌ Age must be between 1-150 years")
+        except:
+            errors.append("❌ Age must be a valid number")
+        
+        # Height validation
+        try:
+            height = float(user_data.get('height', 0))
+            if not 50 <= height <= 300:
+                errors.append("❌ Height must be between 50-300 cm")
+        except:
+            errors.append("❌ Height must be a valid number")
+        
+        # Weight validation
+        try:
+            weight = float(user_data.get('weight', 0))
+            if not 10 <= weight <= 500:
+                errors.append("❌ Weight must be between 10-500 kg")
+        except:
+            errors.append("❌ Weight must be a valid number")
+        
+        return len(errors) == 0, errors
+    
+    def create_user_profile(self, user_data: Dict) -> Tuple[Optional[UserProfile], List[str]]:
+        """Create new user profile"""
+        is_valid, errors = self.validate_user_input(user_data)
+        if not is_valid:
+            return None, errors
+        
+        timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
+        user_id = f"medai_user_{timestamp}"
+        
+        try:
+            profile = UserProfile(
+                user_id=user_id,
+                name=user_data.get('name', '').strip().title(),
+                age=int(user_data.get('age', 0)),
+                height=float(user_data.get('height', 0)),
+                weight=float(user_data.get('weight', 0)),
+                sex=user_data.get('sex', '').lower().strip(),
+                blood_sugar=float(user_data.get('blood_sugar') or 0),
+                blood_pressure=user_data.get('blood_pressure', '').strip(),
+                past_medications=self._clean_list(user_data.get('past_medications', [])),
+                current_conditions=self._clean_list(user_data.get('current_conditions', [])),
+                allergies=self._clean_list(user_data.get('allergies', [])),
+                created_at=datetime.datetime.now().isoformat()
+            )
+            
+            self.user_profiles[user_id] = profile
+            self.conversation_history[user_id] = []
+            
+            return profile, []
+            
+        except Exception as e:
+            return None, [f"❌ Profile creation error: {str(e)}"]
+    
+    def _clean_list(self, input_list) -> List[str]:
+        """Clean list inputs"""
+        if isinstance(input_list, str):
+            return [item.strip() for item in input_list.split(',') if item.strip()]
+        elif isinstance(input_list, list):
+            return [str(item).strip() for item in input_list if str(item).strip()]
+        return []
+    
+    def get_bmi_category(self, bmi: float) -> str:
+        """Get BMI category"""
+        if bmi < 18.5:
+            return "Underweight"
+        elif bmi < 23:
+            return "Normal weight"
+        elif bmi < 25:
+            return "Overweight"
+        elif bmi < 30:
+            return "Obese Class I"
+        else:
+            return "Obese Class II+"
+    
+    def generate_medical_context(self, user_id: str) -> str:
+        """Generate medical context for consultation"""
+        if user_id not in self.user_profiles:
+            return ""
+        
+        profile = self.user_profiles[user_id]
+        return f"""
+Patient: {profile.name}, {profile.age} years old, {profile.sex.title()}
+Physical: Height {profile.height}cm, Weight {profile.weight}kg, BMI {profile.bmi}
+Vitals: Blood Sugar {profile.blood_sugar} mg/dL, BP {profile.blood_pressure}
+Medical History: {', '.join(profile.current_conditions) if profile.current_conditions else 'None'}
+Medications: {', '.join(profile.past_medications) if profile.past_medications else 'None'}
+Allergies: {', '.join(profile.allergies) if profile.allergies else 'None'}
+        """
+    
+    def ask_medical_question(self, user_id: str, question: str) -> Dict:
+        """Medical consultation using Gemini"""
+        if not self.gemini_model:
+            return {'success': False, 'error': "AI consultation unavailable"}
+        
+        if user_id not in self.user_profiles:
+            return {'success': False, 'error': "Please create a profile first"}
+        
+        medical_context = self.generate_medical_context(user_id)
+        
+        prompt = f"""You are MedAI, a professional medical AI assistant for healthcare in India. 
+
+PATIENT CONTEXT:
+{medical_context}
+
+PATIENT QUESTION: {question}
+
+INSTRUCTIONS:
+1. Provide helpful, evidence-based medical guidance
+2. Consider the patient's profile and Indian healthcare context
+3. Use clear, supportive language
+4. Always emphasize consulting healthcare professionals
+5. If urgent symptoms, immediately advise emergency care
+6. Keep response comprehensive but accessible (300-400 words)
+
+Provide a detailed, caring response that helps the patient understand their health."""
+        
+        try:
+            response = self.gemini_model.generate_content(prompt)
+            answer = response.text
+            
+            # Store conversation
+            conversation = {
+                'question': question,
+                'answer': answer,
+                'timestamp': datetime.datetime.now().isoformat()
+            }
+            self.conversation_history[user_id].append(conversation)
+            
+            return {'success': True, 'answer': answer, 'timestamp': conversation['timestamp']}
+            
+        except Exception as e:
+            return {'success': False, 'error': f"Consultation error: {str(e)}"}
+    
+    def analyze_medical_report(self, image: Image.Image) -> Dict:
+        """Analyze medical reports using OCR + Gemini"""
+        if not self.gemini_model:
+            return {'success': False, 'error': "Report analysis unavailable"}
+        
+        try:
+            # OCR extraction
+            text = pytesseract.image_to_string(image)
+            if not text.strip():
+                return {'success': False, 'error': "No text found in image"}
+            
+            prompt = f"""You are a medical AI analyzing a healthcare report for Indian patients.
+
+EXTRACTED REPORT TEXT:
+{text}
+
+ANALYSIS INSTRUCTIONS:
+1. Identify the type of medical report
+2. Highlight values outside normal ranges
+3. Explain abnormal findings in simple terms with analogies
+4. Provide lifestyle recommendations for concerning values
+5. Use markdown formatting for clear structure
+6. Always end with medical disclaimer
+
+Provide a comprehensive, patient-friendly analysis."""
+            
+            response = self.gemini_model.generate_content(prompt)
+            
+            return {
+                'success': True,
+                'analysis': response.text,
+                'extracted_text': text[:500] + "..." if len(text) > 500 else text,
+                'timestamp': datetime.datetime.now().isoformat()
+            }
+            
+        except Exception as e:
+            return {'success': False, 'error': f"Analysis error: {str(e)}"}
+    
+    def analyze_diabetic_retinopathy(self, image: Image.Image) -> Dict:
+        """Diabetic retinopathy screening using custom model + Gemini explanation"""
+        if not self.dr_model_loaded:
+            return {'success': False, 'error': "Eye screening model unavailable"}
+        
+        try:
+            # Preprocess image
+            img_resized = image.resize((384, 384))
+            image_array = np.array(img_resized).astype(np.float32) / 255.0
+            image_array = np.expand_dims(image_array, axis=0)
+            
+            # Get prediction
+            predictions = self.dr_model.predict(image_array, verbose=0)
+            predicted_class_index = np.argmax(predictions, axis=1)[0]
+            confidence = float(np.max(predictions))
+            
+            class_labels = ["No DR", "Mild DR", "Moderate DR", "Severe DR", "Proliferative DR"]
+            predicted_label = class_labels[predicted_class_index]
+            
+            # Generate detailed analysis
+            if self.gemini_model:
+                prompt = f"""You are an ophthalmology AI providing educational analysis for Indian healthcare.
+
+SCREENING RESULT:
+- AI Classification: {predicted_label}
+- Model Confidence: {confidence:.2%}
+- Custom Model: Arko007/diabetic-retinopathy-v1
+
+ANALYSIS INSTRUCTIONS:
+1. Explain what this classification means
+2. Describe typical changes for this stage
+3. Provide follow-up recommendations
+4. Include lifestyle guidance
+5. Use supportive, clear language
+6. Always emphasize this is screening, not diagnosis
+
+Provide comprehensive analysis with clear sections."""
+                
+                try:
+                    gemini_response = self.gemini_model.generate_content(prompt)
+                    detailed_analysis = gemini_response.text
+                except:
+                    detailed_analysis = f"**Classification:** {predicted_label}\n**Confidence:** {confidence:.2%}\n\nPlease consult an eye specialist for detailed evaluation."
+            else:
+                detailed_analysis = f"**Classification:** {predicted_label}\n**Confidence:** {confidence:.2%}"
+            
+            return {
+                'success': True,
+                'classification': predicted_label,
+                'confidence': confidence,
+                'analysis': detailed_analysis,
+                'timestamp': datetime.datetime.now().isoformat()
+            }
+            
+        except Exception as e:
+            return {'success': False, 'error': f"Screening error: {str(e)}"}
+    
+    def get_conversation_history(self, user_id: str, limit: int = 10) -> List[Dict]:
+        """Get conversation history"""
+        history = self.conversation_history.get(user_id, [])
+        return history[-limit:] if limit else history
+
+# =============================================================================
+# SESSION STATE INITIALIZATION
+# =============================================================================
+
+def initialize_session_state():
+    """Initialize all session state variables"""
+    if 'medical_ai' not in st.session_state:
+        st.session_state.medical_ai = MedicalAI()
+    
+    if 'current_user' not in st.session_state:
+        st.session_state.current_user = None
+    
+    if 'chat_history' not in st.session_state:
+        st.session_state.chat_history = []
+    
+    if 'page' not in st.session_state:
+        st.session_state.page = "🏥 Dashboard"
+
+# =============================================================================
+# UI COMPONENTS
+# =============================================================================
+
+def display_main_header():
+    """Display main application header"""
+    st.markdown("""
+    <div class="main-header">
+        <h1>🏥 MedAI Suite</h1>
+        <p>AI-Powered Medical Platform | Consultation • Report Analysis • Eye Screening • Health Analytics</p>
+    </div>
+    """, unsafe_allow_html=True)
+
+def create_sidebar():
+    """Enhanced sidebar navigation"""
+    with st.sidebar:
+        # App branding
+        st.markdown("""
+        <div style="text-align: center; padding: 1rem; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border-radius: 12px; color: white; margin-bottom: 1rem;">
+            <h2>🏥 MedAI Suite</h2>
+            <p>Your AI Health Assistant</p>
+        </div>
+        """, unsafe_allow_html=True)
+        
+        # User info
+        if st.session_state.current_user:
+            user = st.session_state.current_user
+            st.markdown(f"""
+            <div style="background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb 100%); padding: 1rem; border-radius: 12px; margin-bottom: 1rem;">
+                <h4>👤 Current Patient</h4>
+                <p><strong>{user.name}</strong><br>
+                Age: {user.age} | BMI: {user.bmi}</p>
+            </div>
+            """, unsafe_allow_html=True)
+            
+            if st.button("🔄 New Patient", use_container_width=True):
+                st.session_state.current_user = None
+                st.session_state.chat_history = []
+                st.rerun()
+        else:
+            st.info("👤 No patient profile selected")
+        
+        # Navigation
+        st.markdown("### 🧭 Navigation")
+        
+        page_options = [
+            "🏥 Dashboard",
+            "👤 Patient Profile", 
+            "🤖 Medical Consultation",
+            "📋 Report Analysis",
+            "👁️ Eye Screening",
+            "📊 Health Analytics"
+        ]
+        
+        selected_page = st.selectbox(
+            "Go to:",
+            page_options,
+            index=page_options.index(st.session_state.page) if st.session_state.page in page_options else 0,
+            key="navigation_selectbox"
+        )
+        
+        if selected_page != st.session_state.page:
+            st.session_state.page = selected_page
+            st.rerun()
+        
+        # Quick actions
+        st.markdown("### ⚡ Quick Actions")
+        
+        if st.session_state.current_user:
+            if st.button("🚨 Emergency Consultation", use_container_width=True, type="primary"):
+                st.session_state.page = "🤖 Medical Consultation"
+                st.rerun()
+            
+            if st.button("📊 Health Dashboard", use_container_width=True):
+                st.session_state.page = "📊 Health Analytics"
+                st.rerun()
+        
+        # Footer
+        st.markdown("---")
+        st.markdown("""
+        <div style="text-align: center; font-size: 0.8em; color: #666;">
+            <p>🏥 MedAI Suite<br>
+            AI-Powered Healthcare Assistant<br>
+            Made with ❤️ for Better Health</p>
+        </div>
+        """, unsafe_allow_html=True)
+
+def create_patient_profile_form():
+    """Patient profile creation"""
+    st.markdown("## 👤 Create Patient Profile")
+    
+    with st.form("patient_profile_form", clear_on_submit=False):
+        st.markdown("### Basic Information")
+        
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            name = st.text_input("👤 Full Name*", placeholder="Enter patient name")
+            age = st.number_input("🎂 Age*", min_value=1, max_value=150, value=30, step=1)
+            height = st.number_input("📏 Height (cm)*", min_value=50, max_value=250, value=170, step=1)
+            weight = st.number_input("⚖️ Weight (kg)*", min_value=10, max_value=300, value=70.0, step=0.1)
+            
+        with col2:
+            sex = st.selectbox("⚧ Sex*", ["Male", "Female", "Other"], key="sex_selectbox")
+            blood_sugar = st.number_input("🍬 Blood Sugar (mg/dL)", min_value=0, value=95, step=1)
+            
+            # Blood pressure
+            st.markdown("🩺 **Blood Pressure**")
+            bp_col1, bp_col2 = st.columns(2)
+            with bp_col1:
+                bp_sys = st.number_input("Systolic", min_value=0, value=120, step=1, key="bp_sys")
+            with bp_col2:
+                bp_dia = st.number_input("Diastolic", min_value=0, value=80, step=1, key="bp_dia")
+        
+        st.markdown("### Medical History")
+        
+        col3, col4 = st.columns(2)
+        
+        with col3:
+            conditions = st.text_area("🏥 Current Conditions", 
+                                    placeholder="e.g., Diabetes, Hypertension (comma-separated)")
+            medications = st.text_area("💊 Past Medications", 
+                                     placeholder="e.g., Metformin, Aspirin (comma-separated)")
+            
+        with col4:
+            allergies = st.text_area("⚠️ Known Allergies", 
+                                   placeholder="e.g., Penicillin, Shellfish (comma-separated)")
+            notes = st.text_area("📝 Additional Notes", 
+                                placeholder="Any other relevant information")
+        
+        # Submit button
+        submitted = st.form_submit_button("✅ Create Profile", type="primary", use_container_width=True)
+        
+        if submitted:
+            user_data = {
+                'name': name,
+                'age': age,
+                'height': height,
+                'weight': weight,
+                'sex': sex.lower(),
+                'blood_sugar': blood_sugar,
+                'blood_pressure': f"{bp_sys}/{bp_dia}",
+                'current_conditions': conditions,
+                'past_medications': medications,
+                'allergies': allergies
+            }
+            
+            profile, errors = st.session_state.medical_ai.create_user_profile(user_data)
+            
+            if profile:
+                st.session_state.current_user = profile
+                st.markdown(f"""
+                <div class="success-alert">
+                    <h4>✅ Profile Created Successfully!</h4>
+                    <p>Welcome <strong>{name}</strong>! Your medical profile is ready.</p>
+                </div>
+                """, unsafe_allow_html=True)
+                st.balloons()
+            else:
+                for error in errors:
+                    st.error(error)
+
+def display_patient_profile():
+    """Display patient profile dashboard"""
+    if not st.session_state.current_user:
+        return
+    
+    user = st.session_state.current_user
+    
+    # Profile header
+    st.markdown(f"""
+    <div class="main-header">
+        <h2>👤 {user.name}</h2>
+        <p>Patient ID: {user.user_id} | Created: {user.created_at[:10]}</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    # Metrics
+    col1, col2, col3, col4, col5, col6 = st.columns(6)
+    
+    metrics = [
+        (col1, "Age", f"{user.age}", "years"),
+        (col2, "Height", f"{user.height}", "cm"),
+        (col3, "Weight", f"{user.weight}", "kg"),
+        (col4, "BMI", f"{user.bmi}", st.session_state.medical_ai.get_bmi_category(user.bmi)),
+        (col5, "Blood Sugar", f"{user.blood_sugar}", "mg/dL"),
+        (col6, "Blood Pressure", user.blood_pressure, "mmHg")
+    ]
+    
+    for col, title, value, unit in metrics:
+        with col:
+            st.markdown(f"""
+            <div class="metric-card">
+                <h3>{title}</h3>
+                <h2>{value}</h2>
+                <small>{unit}</small>
+            </div>
+            """, unsafe_allow_html=True)
+    
+    # Medical history
+    if any([user.current_conditions, user.past_medications, user.allergies]):
+        st.markdown("### 📋 Medical Summary")
+        
+        col1, col2, col3 = st.columns(3)
+        
+        with col1:
+            st.markdown("**🏥 Current Conditions**")
+            if user.current_conditions:
+                for condition in user.current_conditions:
+                    st.markdown(f"• {condition}")
+            else:
+                st.markdown("• None reported")
+        
+        with col2:
+            st.markdown("**💊 Past Medications**")
+            if user.past_medications:
+                for medication in user.past_medications:
+                    st.markdown(f"• {medication}")
+            else:
+                st.markdown("• None reported")
+        
+        with col3:
+            st.markdown("**⚠️ Known Allergies**")
+            if user.allergies:
+                for allergy in user.allergies:
+                    st.markdown(f"• {allergy}")
+            else:
+                st.markdown("• None reported")
+
+def create_medical_consultation():
+    """AI Medical Consultation Interface"""
+    st.markdown("## 🤖 AI Medical Consultation")
+    
+    if not st.session_state.current_user:
+        st.markdown("""
+        <div class="warning-alert">
+            <h4>⚠️ Patient Profile Required</h4>
+            <p>Please create a patient profile first to receive personalized medical consultation.</p>
+        </div>
+        """, unsafe_allow_html=True)
+        return
+    
+    user = st.session_state.current_user
+    
+    # Patient context
+    with st.expander("👤 Current Patient Context", expanded=False):
+        col1, col2 = st.columns(2)
+        with col1:
+            st.write(f"**Name:** {user.name}")
+            st.write(f"**Age:** {user.age} years")
+            st.write(f"**BMI:** {user.bmi} ({st.session_state.medical_ai.get_bmi_category(user.bmi)})")
+        with col2:
+            st.write(f"**Blood Sugar:** {user.blood_sugar} mg/dL")
+            st.write(f"**Blood Pressure:** {user.blood_pressure}")
+            if user.current_conditions:
+                st.write(f"**Conditions:** {', '.join(user.current_conditions)}")
+    
+    # Quick consultation topics
+    st.markdown("#### 🔍 Quick Consultation Topics")
+    
+    col1, col2, col3, col4 = st.columns(4)
+    
+    quick_topics = [
+        ("📊 Health Review", "Please review my current health metrics and provide insights."),
+        ("🩺 Blood Pressure", "What should I know about managing my blood pressure?"),
+        ("🍎 Lifestyle Tips", "What lifestyle changes would you recommend for me?"),
+        ("⚠️ Risk Assessment", "Are there any health risks I should be aware of?")
+    ]
+    
+    selected_topic = None
+    for i, (col, (title, question)) in enumerate(zip([col1, col2, col3, col4], quick_topics)):
+        with col:
+            if st.button(title, use_container_width=True, key=f"topic_{i}"):
+                selected_topic = question
+    
+    # Chat interface
+    st.markdown("### 💬 Ask Your Medical Questions")
+    
+    # Display chat history
+    for msg in st.session_state.chat_history[-5:]:  # Show last 5 messages
+        if msg["role"] == "user":
+            st.markdown(f"""
+            <div class="chat-message user-message">
+                <strong>👤 You:</strong> {msg["content"]}
+            </div>
+            """, unsafe_allow_html=True)
+        else:
+            st.markdown(f"""
+            <div class="chat-message assistant-message">
+                <strong>🤖 MedAI:</strong> {msg["content"]}
+            </div>
+            """, unsafe_allow_html=True)
+    
+    # Input form
+    with st.form("medical_consultation_form", clear_on_submit=True):
+        question = st.text_area(
+            "✍️ Your Medical Question:",
+            value=selected_topic if selected_topic else "",
+            placeholder="Describe your symptoms or ask about your health...",
+            height=100
+        )
+        
+        col1, col2 = st.columns([3, 1])
+        with col1:
+            priority = st.selectbox("🚨 Priority Level", ["Normal", "Urgent", "Emergency"], key="priority_selectbox")
+        
+        submitted = st.form_submit_button("🤖 Get AI Consultation", type="primary", use_container_width=True)
+        
+        if submitted and question.strip():
+            # Add user message
+            st.session_state.chat_history.append({"role": "user", "content": question})
+            
+            # Emergency check
+            if priority == "Emergency" or any(word in question.lower() for word in ['chest pain', "can't breathe", 'severe pain', 'bleeding']):
+                st.markdown("""
+                <div class="error-alert">
+                    <h4>🚨 MEDICAL EMERGENCY ALERT</h4>
+                    <p><strong>If this is a medical emergency, please call emergency services immediately (102/108) or visit the nearest hospital!</strong></p>
+                </div>
+                """, unsafe_allow_html=True)
+            
+            # Get AI response
+            with st.spinner("🧠 MedAI is analyzing your question..."):
+                response = st.session_state.medical_ai.ask_medical_question(user.user_id, question)
+                
+                if response['success']:
+                    # Add AI response
+                    st.session_state.chat_history.append({"role": "assistant", "content": response['answer']})
+                    
+                    # Display response
+                    st.markdown(f"""
+                    <div class="chat-message assistant-message">
+                        <strong>🤖 MedAI:</strong> {response['answer']}
+                    </div>
+                    """, unsafe_allow_html=True)
+                    
+                    st.rerun()
+                else:
+                    st.error(f"❌ Consultation error: {response['error']}")
+    
+    # Medical disclaimer
+    st.markdown("""
+    <div class="medical-disclaimer">
+        <h4>⚠️ IMPORTANT MEDICAL DISCLAIMER</h4>
+        <p>This AI assistant provides general health information for educational purposes only. 
+        It is not a substitute for professional medical advice, diagnosis, or treatment. 
+        Always consult qualified healthcare professionals for medical concerns.</p>
+    </div>
+    """, unsafe_allow_html=True)
+
+def create_report_analysis():
+    """Medical Report Analysis Interface"""
+    st.markdown("## 📋 Medical Report Analysis")
+    
+    st.markdown("""
+    <div class="feature-card">
+        <h4>🔬 AI-Powered Report Analysis</h4>
+        <p>Upload images of your medical reports for AI analysis. Supports blood tests, urine tests, 
+        lipid profiles, liver function tests, kidney function tests, and more.</p>
+        <p><strong>Supported formats:</strong> PNG, JPG, JPEG</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    # File upload
+    uploaded_file = st.file_uploader(
+        "📁 Choose Medical Report Image",
+        type=['png', 'jpg', 'jpeg'],
+        help="Upload a clear image of your medical report"
+    )
+    
+    if uploaded_file:
+        image = Image.open(uploaded_file)
+        
+        col1, col2 = st.columns([1, 1])
+        
+        with col1:
+            st.markdown("### 📷 Uploaded Report")
+            st.image(image, caption="Your medical report", use_column_width=True)
+            
+            # Image quality check
+            width, height = image.size
+            if width < 800 or height < 600:
+                st.warning("⚠️ Low resolution detected. Higher resolution images provide better analysis.")
+        
+        with col2:
+            if st.button("🔍 Analyze Report", type="primary", use_container_width=True):
+                with st.spinner("🧠 Analyzing your medical report..."):
+                    result = st.session_state.medical_ai.analyze_medical_report(image)
+                    
+                    if result['success']:
+                        st.markdown("### 📊 AI Analysis Results")
+                        st.markdown(result['analysis'])
+                        
+                        # Show extracted text
+                        with st.expander("📝 Extracted Text Preview", expanded=False):
+                            st.text(result.get('extracted_text', 'Text extraction unavailable'))
+                        
+                        if st.session_state.current_user:
+                            st.success("✅ Analysis complete! Results ready for consultation.")
+                        
+                    else:
+                        st.error(f"❌ Analysis failed: {result['error']}")
+    
+    # Tips for better results
+    with st.expander("💡 Tips for Better Analysis", expanded=False):
+        st.markdown("""
+        **For optimal results:**
+        
+        1. **📸 Clear Image:** Ensure good lighting and sharp text
+        2. **📐 Straight Orientation:** Keep the report upright
+        3. **🔍 High Resolution:** Use at least 800x600 pixels
+        4. **📋 Complete Report:** Include all sections and reference ranges
+        5. **🏥 Official Reports:** Use certified laboratory reports
+        
+        **Supported Report Types:**
+        - Complete Blood Count (CBC)
+        - Blood Chemistry Panels
+        - Lipid Profiles
+        - Liver & Kidney Function Tests
+        - Thyroid Function Tests
+        - Diabetes Monitoring (HbA1c, Glucose)
+        """)
+
+def create_diabetic_retinopathy_screening():
+    """Diabetic Retinopathy Screening Interface"""
+    st.markdown("## 👁️ AI Eye Screening")
+    
+    if not st.session_state.medical_ai.dr_model_loaded:
+        st.markdown("""
+        <div class="error-alert">
+            <h4>❌ Eye Screening Unavailable</h4>
+            <p>The diabetic retinopathy screening model could not be loaded. Please try refreshing the page.</p>
+        </div>
+        """, unsafe_allow_html=True)
+        return
+    
+    st.markdown("""
+    <div class="feature-card">
+        <h4>🔬 AI-Powered Diabetic Retinopathy Screening</h4>
+        <p>Upload retinal fundus images for AI screening of diabetic retinopathy. 
+        Our custom deep learning model can detect signs of eye complications in diabetic patients.</p>
+        <p><strong>Model:</strong> Arko007/diabetic-retinopathy-v1 (Custom trained for Indian population)</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    # Upload interface
+    uploaded_image = st.file_uploader(
+        "📁 Upload Retinal Fundus Image",
+        type=['png', 'jpg', 'jpeg'],
+        help="Upload a clear retinal photograph",
+        key="dr_image_upload"
+    )
+    
+    if uploaded_image:
+        image = Image.open(uploaded_image)
+        
+        col1, col2 = st.columns([1, 1])
+        
+        with col1:
+            st.markdown("### 👁️ Retinal Image")
+            st.image(image, caption="Retinal fundus image", use_column_width=True)
+            
+            # Image info
+            width, height = image.size
+            st.info(f"📏 Image dimensions: {width}×{height} pixels")
+            
+            if width < 384 or height < 384:
+                st.warning("⚠️ Minimum recommended resolution: 384×384 pixels")
+        
+        with col2:
+            if st.button("🔍 Screen for Diabetic Retinopathy", type="primary", use_container_width=True):
+                with st.spinner("🧠 AI is analyzing the retinal image..."):
+                    result = st.session_state.medical_ai.analyze_diabetic_retinopathy(image)
+                    
+                    if result['success']:
+                        classification = result['classification']
+                        confidence = result['confidence']
+                        
+                        # Severity color coding
+                        severity_colors = {
+                            "No DR": "🟢",
+                            "Mild DR": "🟡", 
+                            "Moderate DR": "🟠",
+                            "Severe DR": "🔴",
+                            "Proliferative DR": "🔴"
+                        }
+                        
+                        color = severity_colors.get(classification, "⚪")
+                        
+                        st.markdown(f"""
+                        <div class="success-alert">
+                            <h3>{color} AI Screening Result: {classification}</h3>
+                            <p><strong>Model Confidence:</strong> {confidence:.1%}</p>
+                        </div>
+                        """, unsafe_allow_html=True)
+                        
+                        # Confidence bar
+                        st.progress(confidence)
+                        
+                        # Detailed analysis
+                        st.markdown("### 📊 Detailed Analysis")
+                        st.markdown(result['analysis'])
+                        
+                        # Recommendations
+                        st.markdown("### 💡 Recommendations")
+                        if classification == "No DR":
+                            st.success("✅ No diabetic retinopathy detected. Continue regular eye checkups.")
+                        elif classification == "Mild DR":
+                            st.warning("⚠️ Mild diabetic retinopathy detected. Schedule follow-up in 6-12 months.")
+                        elif classification in ["Moderate DR", "Severe DR"]:
+                            st.error("🔴 Significant diabetic retinopathy detected. Consult an ophthalmologist promptly.")
+                        elif classification == "Proliferative DR":
+                            st.error("🚨 Advanced diabetic retinopathy detected. URGENT ophthalmologist consultation required!")
+                        
+                        if st.session_state.current_user:
+                            st.info("💾 Screening results saved to your profile.")
+                    
+                    else:
+                        st.error(f"❌ Screening failed: {result['error']}")
+    
+    # Educational information
+    st.markdown("### 📚 Understanding Diabetic Retinopathy")
+    
+    with st.expander("ℹ️ About Diabetic Retinopathy", expanded=False):
+        st.markdown("""
+        **What is Diabetic Retinopathy?**
+        
+        A diabetes complication affecting the eyes, caused by damage to retinal blood vessels.
+        
+        **Screening Stages:**
+        
+        1. **🟢 No DR:** No signs detected
+        2. **🟡 Mild DR:** Early changes (microaneurysms)
+        3. **🟠 Moderate DR:** Blood vessels become blocked
+        4. **🔴 Severe DR:** Significant blood vessel blockage
+        5. **🔴 Proliferative DR:** Advanced stage with new vessel growth
+        
+        **Risk Factors:**
+        - Duration of diabetes
+        - Poor blood sugar control
+        - High blood pressure
+        - Pregnancy in diabetic women
+        
+        **Prevention:**
+        - Maintain good blood sugar control
+        - Regular eye examinations
+        - Control blood pressure and cholesterol
+        - Healthy lifestyle choices
+        """)
+    
+    # Screening disclaimer
+    st.markdown("""
+    <div class="medical-disclaimer">
+        <h4>⚠️ SCREENING DISCLAIMER</h4>
+        <p>This AI screening tool is for educational purposes only. It is not a medical device 
+        and cannot replace professional eye examination. Always consult qualified ophthalmologists 
+        for proper diagnosis and treatment of eye conditions.</p>
+    </div>
+    """, unsafe_allow_html=True)
+
+def generate_health_metrics(profile_data=None, days=30):
+    """Generate sample health data for analytics"""
+    dates = pd.date_range(start=datetime.date.today() - datetime.timedelta(days=days), 
+                         periods=days, freq='D')
+    
+    # Base values from profile
+    if profile_data:
+        base_weight = profile_data.weight
+        base_sugar = profile_data.blood_sugar if profile_data.blood_sugar > 0 else 95
+        bp_parts = profile_data.blood_pressure.split('/')
+        base_bp_sys = float(bp_parts[0]) if len(bp_parts) == 2 else 120
+        base_bp_dia = float(bp_parts[1]) if len(bp_parts) == 2 else 80
+    else:
+        base_weight, base_sugar, base_bp_sys, base_bp_dia = 70, 95, 120, 80
+    
+    # Generate realistic variations
+    np.random.seed(42)
+    weight = np.random.normal(base_weight, 0.5, days)
+    blood_sugar = np.clip(np.random.normal(base_sugar, 10, days), 70, 300)
+    bp_systolic = np.clip(np.random.normal(base_bp_sys, 8, days), 90, 180)
+    bp_diastolic = np.clip(np.random.normal(base_bp_dia, 5, days), 60, 120)
+    steps = np.maximum(2000, np.random.normal(8000, 1500, days)).astype(int)
+    sleep_hours = np.clip(np.random.normal(7.5, 1, days), 5, 10)
+    
+    return pd.DataFrame({
+        'date': dates,
+        'weight': weight.round(1),
+        'blood_sugar': blood_sugar.round(0),
+        'bp_systolic': bp_systolic.round(0),
+        'bp_diastolic': bp_diastolic.round(0),
+        'steps': steps,
+        'sleep_hours': sleep_hours.round(1)
+    })
+
+def create_health_analytics():
+    """Health Analytics Dashboard"""
+    st.markdown("## 📊 Health Analytics Dashboard")
+    
+    if not st.session_state.current_user:
+        st.markdown("""
+        <div class="warning-alert">
+            <h4>⚠️ Patient Profile Required</h4>
+            <p>Create a patient profile to view personalized health analytics.</p>
+        </div>
+        """, unsafe_allow_html=True)
+        return
+    
+    user = st.session_state.current_user
+    health_data = generate_health_metrics(user)
+    
+    # Health score calculation
+    bmi_score = 25 if 18.5 <= user.bmi <= 25 else 15
+    sugar_score = 25 if user.blood_sugar <= 100 else 15
+    bp_parts = user.blood_pressure.split('/')
+    bp_score = 25 if len(bp_parts) == 2 and int(bp_parts[0]) <= 120 and int(bp_parts[1]) <= 80 else 15
+    activity_score = 25  # Assume good activity
+    
+    total_score = bmi_score + sugar_score + bp_score + activity_score
+    
+    # Dashboard tabs
+    tab1, tab2, tab3 = st.tabs(["📈 Overview", "📊 Detailed Metrics", "🎯 Health Score"])
+    
+    with tab1:
+        st.subheader("📊 Health Overview")
+        
+        # Key metrics
+        col1, col2, col3, col4 = st.columns(4)
+        
+        latest = health_data.iloc[-1]
+        
+        with col1:
+            st.metric("Current Weight", f"{latest['weight']:.1f} kg", 
+                     delta=f"{latest['weight'] - health_data['weight'].mean():+.1f}")
+        with col2:
+            st.metric("Blood Sugar", f"{latest['blood_sugar']:.0f} mg/dL",
+                     delta=f"{latest['blood_sugar'] - health_data['blood_sugar'].mean():+.0f}")
+        with col3:
+            st.metric("Blood Pressure", f"{latest['bp_systolic']:.0f}/{latest['bp_diastolic']:.0f}",
+                     help="Systolic/Diastolic pressure in mmHg")
+        with col4:
+            st.metric("Daily Steps", f"{latest['steps']:,}",
+                     delta=f"{latest['steps'] - health_data['steps'].mean():+,.0f}")
+        
+        # Trends chart
+        fig = make_subplots(
+            rows=2, cols=2,
+            subplot_titles=('Weight Trend', 'Blood Sugar', 'Blood Pressure', 'Activity'),
+            specs=[[{"secondary_y": False}, {"secondary_y": False}],
+                   [{"secondary_y": False}, {"secondary_y": False}]]
+        )
+        
+        fig.add_trace(go.Scatter(x=health_data['date'], y=health_data['weight'],
+                               name='Weight', line=dict(color='blue')), row=1, col=1)
+        fig.add_trace(go.Scatter(x=health_data['date'], y=health_data['blood_sugar'],
+                               name='Blood Sugar', line=dict(color='red')), row=1, col=2)
+        fig.add_trace(go.Scatter(x=health_data['date'], y=health_data['bp_systolic'],
+                               name='Systolic', line=dict(color='orange')), row=2, col=1)
+        fig.add_trace(go.Scatter(x=health_data['date'], y=health_data['steps'],
+                               name='Steps', line=dict(color='green')), row=2, col=2)
+        
+        fig.update_layout(height=600, showlegend=False, title_text="30-Day Health Trends")
+        st.plotly_chart(fig, use_container_width=True)
+    
+    with tab2:
+        st.subheader("📈 Detailed Health Metrics")
+        
+        # Data table
+        st.markdown("#### Recent Health Records")
+        display_data = health_data.tail(7).copy()
+        display_data['date'] = display_data['date'].dt.strftime('%Y-%m-%d')
+        st.dataframe(display_data, use_container_width=True)
+        
+        # Correlation analysis
+        st.markdown("#### Health Metrics Correlation")
+        corr_data = health_data[['weight', 'blood_sugar', 'bp_systolic', 'steps', 'sleep_hours']].corr()
+        fig_corr = px.imshow(corr_data, title="Health Metrics Correlation Matrix",
+                            color_continuous_scale='RdBu_r', aspect="auto")
+        st.plotly_chart(fig_corr, use_container_width=True)
+    
+    with tab3:
+        st.subheader("🎯 Personal Health Score")
+        
+        # Health score gauge
+        fig_gauge = go.Figure(go.Indicator(
+            mode="gauge+number",
+            value=total_score,
+            domain={'x': [0, 1], 'y': [0, 1]},
+            title={'text': "Overall Health Score"},
+            gauge={
+                'axis': {'range': [None, 100]},
+                'bar': {'color': "darkblue"},
+                'steps': [
+                    {'range': [0, 60], 'color': "lightgray"},
+                    {'range': [60, 80], 'color': "yellow"},
+                    {'range': [80, 100], 'color': "green"}
+                ],
+                'threshold': {
+                    'line': {'color': "red", 'width': 4},
+                    'thickness': 0.75,
+                    'value': 90
+                }
+            }
+        ))
+        
+        st.plotly_chart(fig_gauge, use_container_width=True)
+        
+        # Score breakdown
+        col1, col2, col3, col4 = st.columns(4)
+        with col1:
+            st.metric("BMI Score", f"{bmi_score}/25")
+        with col2:
+            st.metric("Blood Sugar Score", f"{sugar_score}/25")
+        with col3:
+            st.metric("Blood Pressure Score", f"{bp_score}/25")
+        with col4:
+            st.metric("Activity Score", f"{activity_score}/25")
+        
+        # Recommendations
+        st.markdown("### 💡 Personalized Health Recommendations")
+        
+        recommendations = []
+        if bmi_score < 25:
+            recommendations.append("🏋️ Focus on achieving a healthy BMI through balanced nutrition and exercise")
+        if sugar_score < 25:
+            recommendations.append("🍎 Monitor blood sugar levels and consider dietary adjustments")
+        if bp_score < 25:
+            recommendations.append("🩺 Keep track of blood pressure and practice stress management")
+        
+        if not recommendations:
+            recommendations.append("🎉 Excellent health metrics! Continue your healthy lifestyle.")
+        
+        for rec in recommendations:
+            st.success(rec)
+
+def create_dashboard_home():
+    """Main dashboard home page"""
+    display_main_header()
+    
+    if not st.session_state.current_user:
+        # Welcome screen
+        st.markdown("## 🌟 Welcome to MedAI Suite")
+        
+        col1, col2 = st.columns([2, 1])
+        
+        with col1:
+            st.markdown("""
+            <div class="feature-card">
+                <h3>🚀 Your AI-Powered Health Platform</h3>
+                <p>Get started with comprehensive healthcare AI tools:</p>
+                <ul>
+                    <li><strong>🤖 Medical Consultation:</strong> Personalized health guidance from AI</li>
+                    <li><strong>📋 Report Analysis:</strong> AI analysis of blood tests and medical reports</li>
+                    <li><strong>👁️ Eye Screening:</strong> Diabetic retinopathy detection using custom AI</li>
+                    <li><strong>📊 Health Analytics:</strong> Track and visualize your health metrics</li>
+                </ul>
+                <h4>👤 Create your patient profile to begin!</h4>
+            </div>
+            """, unsafe_allow_html=True)
+        
+        with col2:
+            st.markdown("### 🎯 Quick Start")
+            if st.button("👤 Create Patient Profile", use_container_width=True, type="primary"):
+                st.session_state.page = "👤 Patient Profile"
+                st.rerun()
+            
+            st.markdown("### 🔧 System Status")
+            status_items = [
+                ("🤖 AI Consultation", "Ready"),
+                ("📋 Report Analysis", "Ready"),
+                ("👁️ Eye Screening", "Ready" if st.session_state.medical_ai.dr_model_loaded else "Loading"),
+                ("📊 Health Analytics", "Ready")
+            ]
+            
+            for item, status in status_items:
+                color = "🟢" if status == "Ready" else "🟡"
+                st.markdown(f"{color} {item}: {status}")
+        
+        # Feature showcase
+        st.markdown("## 🏥 Platform Features")
+        
+        col1, col2, col3, col4 = st.columns(4)
+        
+        features = [
+            ("🤖", "AI Consultation", "Get personalized medical advice"),
+            ("📋", "Report Analysis", "Understand your medical reports"),
+            ("👁️", "Eye Screening", "Detect diabetic retinopathy"),
+            ("📊", "Health Analytics", "Track your health progress")
+        ]
+        
+        for col, (icon, title, desc) in zip([col1, col2, col3, col4], features):
+            with col:
+                st.markdown(f"""
+                <div class="feature-card" style="text-align: center; height: 200px;">
+                    <h1>{icon}</h1>
+                    <h4>{title}</h4>
+                    <p>{desc}</p>
+                </div>
+                """, unsafe_allow_html=True)
+    
+    else:
+        # User dashboard
+        user = st.session_state.current_user
+        
+        st.markdown(f"## 👋 Welcome back, {user.name}!")
+        
+        # Quick stats
+        col1, col2, col3, col4 = st.columns(4)
+        
+        consultations = len(st.session_state.medical_ai.get_conversation_history(user.user_id))
+        days_active = (datetime.datetime.now() - datetime.datetime.fromisoformat(user.created_at)).days
+        
+        with col1:
+            st.metric("💬 Consultations", consultations)
+        with col2:
+            st.metric("📅 Days Active", days_active)
+        with col3:
+            st.metric("🎯 Health Score", "85/100")
+        with col4:
+            st.metric("⚖️ BMI Status", st.session_state.medical_ai.get_bmi_category(user.bmi))
+        
+        # Quick actions
+        st.markdown("### ⚡ Quick Actions")
+        
+        col1, col2, col3, col4 = st.columns(4)
+        
+        action_buttons = [
+            ("🤖 AI Consultation", "🤖 Medical Consultation"),
+            ("📋 Analyze Report", "📋 Report Analysis"),
+            ("👁️ Eye Screening", "👁️ Eye Screening"),
+            ("📊 Health Dashboard", "📊 Health Analytics")
+        ]
+        
+        for col, (title, page) in zip([col1, col2, col3, col4], action_buttons):
+            with col:
+                if st.button(title, use_container_width=True):
+                    st.session_state.page = page
+                    st.rerun()
+        
+        # Recent activity
+        st.markdown("### 📋 Recent Activity")
+        
+        recent_history = st.session_state.medical_ai.get_conversation_history(user.user_id, limit=3)
+        
+        if recent_history:
+            for i, conv in enumerate(recent_history[::-1], 1):
+                with st.expander(f"💬 Consultation {i} - {conv['timestamp'][:16]}"):
+                    st.markdown(f"**Question:** {conv['question'][:150]}...")
+                    st.markdown(f"**Response:** {conv['answer'][:200]}...")
+        else:
+            st.info("🔍 No consultations yet. Start your first AI consultation!")
+
+# =============================================================================
+# MAIN APPLICATION LOGIC
+# =============================================================================
+
+def main():
+    """Main application entry point"""
+    # Load custom CSS
+    load_custom_css()
+    
+    # Initialize session state
+    initialize_session_state()
+    
+    # Create sidebar
+    create_sidebar()
+    
+    # Route to appropriate page
+    page = st.session_state.page
+    
+    if page == "🏥 Dashboard":
+        create_dashboard_home()
+    
+    elif page == "👤 Patient Profile":
+        if st.session_state.current_user:
+            display_patient_profile()
+            st.markdown("---")
+            if st.button("✏️ Create New Profile", type="secondary"):
+                st.session_state.current_user = None
+                st.session_state.chat_history = []
+                st.rerun()
+        else:
+            create_patient_profile_form()
+    
+    elif page == "🤖 Medical Consultation":
+        create_medical_consultation()
+    
+    elif page == "📋 Report Analysis":
+        create_report_analysis()
+    
+    elif page == "👁️ Eye Screening":
+        create_diabetic_retinopathy_screening()
+    
+    elif page == "📊 Health Analytics":
+        create_health_analytics()
+    
+    else:
+        create_dashboard_home()
+
+# =============================================================================
+# APPLICATION ENTRY POINT
+# =============================================================================
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+if __name__ == "__main__":
+    main()