Update app.py

app.py

@@ -1,557 +1,557 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-import joblib
-import pickle
-from PIL import Image
-import io
-import cv2
-import pytesseract
-from sklearn.metrics import roc_auc_score, accuracy_score, classification_report
-import plotly.graph_objects as go
-import plotly.express as px
-from datetime import datetime
-import requests
-import json
-import base64
-
-# Custom CSS for styling
-def local_css():
-    st.markdown("""
-    <style>
-    .main-header {
-        font-size: 3rem;
-        color: #2E86AB;
-        text-align: center;
-        margin-bottom: 2rem;
-        font-weight: bold;
-    }
-    .urdu-text {
-        font-family: 'Jameel Noori Nastaleeq', 'Noto Sans Arabic';
-        font-size: 1.2rem;
-        direction: rtl;
-    }
-    .risk-high { background-color: #ffcccc; padding: 10px; border-radius: 5px; }
-    .risk-medium { background-color: #fff3cd; padding: 10px; border-radius: 5px; }
-    .risk-low { background-color: #d4edda; padding: 10px; border-radius: 5px; }
-    .priority-box { 
-        border: 2px solid #2E86AB; 
-        padding: 20px; 
-        border-radius: 10px; 
-        margin: 10px 0;
-    }
-    </style>
-    """, unsafe_allow_html=True)
-
-# Initialize session state
-def init_session_state():
-    if 'language' not in st.session_state:
-        st.session_state.language = 'English'
-    if 'patient_data' not in st.session_state:
-        st.session_state.patient_data = {}
-    if 'risk_scores' not in st.session_state:
-        st.session_state.risk_scores = {}
-
-# Load models with error handling
-@st.cache_resource
-def load_models():
-    try:
-        heart_model = joblib.load("heart_model.pkl")
-        diabetes_model = joblib.load("diabetes_model.pkl")
-        hypertension_model = joblib.load("hypertension_model.pkl")
-        return heart_model, diabetes_model, hypertension_model
-    except Exception as e:
-        st.error(f"Error loading models: {str(e)}")
-        return None, None, None
-
-# Urdu translations
-URDU_TRANSLATIONS = {
-    "AI-Priority OPD System": "AI-ترجیحی OPD سسٹم",
-    "Patient Information": "مریض کی معلومات",
-    "Name": "نام",
-    "Age": "عمر",
-    "Gender": "جنس",
-    "Contact": "رابطہ نمبر",
-    "Medical History": "طبی تاریخ",
-    "Vital Signs": "اہم علامات",
-    "Blood Pressure (systolic)": "بلڈ پریشر (سسٹولک)",
-    "Blood Pressure (diastolic)": "بلڈ پریشر ڈائیسٹولک)",
-    "Heart Rate": "دل کی دھڑکن",
-    "Cholesterol Level": "کولیسٹرول کی سطح",
-    "Blood Glucose": "خون میں گلوکوز",
-    "BMI": "باڈی ماس انڈیکس",
-    "Symptoms": "علامات",
-    "Chest Pain": "سینے میں درد",
-    "Shortness of Breath": "سانس لینے میں دشواری",
-    "Fatigue": "تھکاوٹ",
-    "Upload Prescription": "نسخہ اپ لوڈ کریں",
-    "Calculate Risk Score": "خطرے کا اسکور معلوم کریں",
-    "High Priority - Emergency Care Required": "اعلی ترجیح - ہنگامی علاج کی ضرورت",
-    "Medium Priority - Same Day Consultation": "درمیانی ترجیح - اسی دن مشورہ",
-    "Low Priority - Routine Appointment": "کم ترجیح - معمول کی ملاقات",
-    "Healthcare Chatbot": "ہیلتھ کیئر چیٹ بوٹ",
-    "Ask health-related questions": "صحت سے متعلق سوالات پوچھیں"
-}
-
-class OCRProcessor:
-    def __init__(self):
-        pass
-    
-    def preprocess_image(self, image):
-        """Enhance image for better OCR accuracy"""
-        try:
-            # Convert to grayscale
-            gray = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY)
-            
-            # Apply thresholding
-            _, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
-            
-            # Remove noise
-            kernel = np.ones((1, 1), np.uint8)
-            processed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
-            
-            return processed
-        except Exception as e:
-            st.error(f"Image processing error: {str(e)}")
-            return np.array(image)
-    
-    def extract_text(self, image):
-        """Extract text from prescription image"""
-        try:
-            processed_image = self.preprocess_image(image)
-            
-            # Configure Tesseract for medical text
-            custom_config = r'--oem 3 --psm 6 -l eng'
-            text = pytesseract.image_to_string(processed_image, config=custom_config)
-            
-            return text.strip()
-        except Exception as e:
-            st.error(f"OCR Error: {str(e)}")
-            return ""
-
-class HealthcareChatbot:
-    def __init__(self):
-        self.health_tips = {
-            'heart': [
-                "Maintain a healthy diet low in saturated fats",
-                "Exercise regularly for at least 30 minutes daily",
-                "Monitor blood pressure regularly",
-                "Avoid smoking and limit alcohol consumption"
-            ],
-            'diabetes': [
-                "Monitor blood sugar levels regularly",
-                "Follow a balanced diet with controlled carbohydrates",
-                "Take medications as prescribed",
-                "Stay physically active"
-            ],
-            'hypertension': [
-                "Reduce salt intake in your diet",
-                "Practice stress management techniques",
-                "Maintain healthy body weight",
-                "Limit caffeine consumption"
-            ]
-        }
-    
-    def get_response(self, query, language='English'):
-        """Generate chatbot response"""
-        query_lower = query.lower()
-        
-        # Simple rule-based responses
-        if any(word in query_lower for word in ['heart', 'cardiac', 'chest pain']):
-            tips = self.health_tips['heart']
-        elif any(word in query_lower for word in ['diabetes', 'sugar', 'glucose']):
-            tips = self.health_tips['diabetes']
-        elif any(word in query_lower for word in ['blood pressure', 'hypertension']):
-            tips = self.health_tips['hypertension']
-        else:
-            tips = ["Maintain regular health checkups", "Follow your doctor's advice", "Stay hydrated and eat balanced meals"]
-        
-        response = f"Based on your query, here are some health tips:\n" + "\n".join([f"• {tip}" for tip in tips])
-        
-        if language == 'Urdu':
-            # Simple Urdu translations (in a real app, use proper translation API)
-            response = "آپ کے سوال کے مطابق، یہاں کچھ صحت کے نکات ہیں:\n" + "\n".join([f"• {tip}" for tip in tips])
-        
-        return response
-
-def calculate_priority_score(heart_risk, diabetes_risk, hypertension_risk):
-    """Calculate integrated priority score"""
-    # Weighted priority scoring based on clinical severity
-    priority = (
-        heart_risk * 0.4 + 
-        diabetes_risk * 0.3 + 
-        hypertension_risk * 0.3
-    )
-    
-    return priority
-
-def get_priority_recommendation(priority_score, language='English'):
-    """Get priority-based recommendation"""
-    if priority_score > 0.8:
-        if language == 'Urdu':
-            return "EMERGENCY_CARE", "اعلی ترجیح - ہنگامی علاج کی ضرورت", "risk-high"
-        else:
-            return "EMERGENCY_CARE", "High Priority - Emergency Care Required", "risk-high"
-    elif priority_score > 0.6:
-        if language == 'Urdu':
-            return "SAME_DAY_CONSULT", "درمیانی ترجیح - اسی دن مشورہ", "risk-medium"
-        else:
-            return "SAME_DAY_CONSULT", "Medium Priority - Same Day Consultation", "risk-medium"
-    else:
-        if language == 'Urdu':
-            return "ROUTINE_APPOINTMENT", "کم ترجیح - معمول کی ملاقات", "risk-low"
-        else:
-            return "ROUTINE_APPOINTMENT", "Low Priority - Routine Appointment", "risk-low"
-
-def main():
-    # Page configuration
-    st.set_page_config(
-        page_title="AI-Priority OPD System",
-        page_icon="🏥",
-        layout="wide",
-        initial_sidebar_state="expanded"
-    )
-    
-    # Load custom CSS
-    local_css()
-    init_session_state()
-    
-    # Load models
-    heart_model, diabetes_model, hypertension_model = load_models()
-    
-    if heart_model is None or diabetes_model is None or hypertension_model is None:
-        st.error("Failed to load ML models. Please check model files.")
-        return
-    
-    # Initialize processors
-    ocr_processor = OCRProcessor()
-    chatbot = HealthcareChatbot()
-    
-    # Language selector in sidebar
-    with st.sidebar:
-        st.image("https://via.placeholder.com/150x50/2E86AB/FFFFFF?text=AI-OPD", use_column_width=True)
-        language = st.radio("Select Language / زبان منتخب کریں", 
-                           ["English", "Urdu"], 
-                           key="language_selector")
-        
-        st.markdown("---")
-        st.subheader("Quick Actions")
-        if st.button("New Patient Assessment" if language == "English" else "نیا مریض تشخیص"):
-            st.session_state.patient_data = {}
-            st.session_state.risk_scores = {}
-    
-    # Main header
-    if language == "English":
-        st.markdown('<h1 class="main-header">🏥 AI-Priority OPD System</h1>', unsafe_allow_html=True)
-        st.markdown("### Smart Patient Triage and Priority Management")
-    else:
-        st.markdown('<h1 class="main-header">🏥 AI-ترجیحی OPD سسٹم</h1>', unsafe_allow_html=True)
-        st.markdown("### ذہین مریض کی درجہ بندی اور ترجیحی انتظام")
-    
-    # Create tabs
-    tab1, tab2, tab3, tab4 = st.tabs([
-        "Patient Assessment" if language == "English" else "مریض تشخیص",
-        "Prescription OCR" if language == "English" else "نسخہ OCR",
-        "Health Chatbot" if language == "English" else "ہیلتھ چیٹ بوٹ",
-        "Analytics" if language == "English" else "تجزیات"
-    ])
-    
-    with tab1:
-        # Patient Assessment Form
-        if language == "English":
-            st.header("Patient Information")
-        else:
-            st.header("مریض کی معلومات")
-        
-        col1, col2 = st.columns(2)
-        
-        with col1:
-            # Basic Information
-            if language == "English":
-                name = st.text_input("Full Name")
-                age = st.number_input("Age", min_value=0, max_value=120, value=30)
-                gender = st.selectbox("Gender", ["Male", "Female", "Other"])
-                contact = st.text_input("Contact Number")
-            else:
-                name = st.text_input("مکمل نام")
-                age = st.number_input("عمر", min_value=0, max_value=120, value=30)
-                gender = st.selectbox("جنس", ["مرد", "عورت", "دیگر"])
-                contact = st.text_input("رابطہ نمبر")
-        
-        with col2:
-            # Vital Signs
-            if language == "English":
-                st.subheader("Vital Signs")
-                bp_systolic = st.number_input("Blood Pressure (systolic)", min_value=50, max_value=250, value=120)
-                bp_diastolic = st.number_input("Blood Pressure (diastolic)", min_value=30, max_value=150, value=80)
-                heart_rate = st.number_input("Heart Rate (bpm)", min_value=30, max_value=200, value=72)
-                cholesterol = st.number_input("Cholesterol Level (mg/dL)", min_value=100, max_value=400, value=200)
-                glucose = st.number_input("Blood Glucose (mg/dL)", min_value=50, max_value=500, value=100)
-                bmi = st.number_input("BMI", min_value=10.0, max_value=50.0, value=22.0, step=0.1)
-            else:
-                st.subheader("اہم علامات")
-                bp_systolic = st.number_input("بلڈ پریشر (سسٹولک)", min_value=50, max_value=250, value=120)
-                bp_diastolic = st.number_input("بلڈ پریشر (ڈائیسٹولک)", min_value=30, max_value=150, value=80)
-                heart_rate = st.number_input("دل کی دھڑکن (bpm)", min_value=30, max_value=200, value=72)
-                cholesterol = st.number_input("کولیسٹرول کی سطح (mg/dL)", min_value=100, max_value=400, value=200)
-                glucose = st.number_input("خون میں گلوکوز (mg/dL)", min_value=50, max_value=500, value=100)
-                bmi = st.number_input("باڈی ماس انڈیکس", min_value=10.0, max_value=50.0, value=22.0, step=0.1)
-        
-        # Symptoms
-        if language == "English":
-            st.subheader("Symptoms")
-            col3, col4 = st.columns(2)
-            with col3:
-                chest_pain = st.checkbox("Chest Pain")
-                shortness_breath = st.checkbox("Shortness of Breath")
-            with col4:
-                fatigue = st.checkbox("Fatigue")
-                dizziness = st.checkbox("Dizziness")
-        else:
-            st.subheader("علامات")
-            col3, col4 = st.columns(2)
-            with col3:
-                chest_pain = st.checkbox("سینے میں درد")
-                shortness_breath = st.checkbox("سانس لینے میں دشواری")
-            with col4:
-                fatigue = st.checkbox("تھکاوٹ")
-                dizziness = st.checkbox("چکر آنا")
-        
-        # Risk Assessment Button
-        if language == "English":
-            assess_button = st.button("🚀 Calculate Risk Score & Priority", use_container_width=True)
-        else:
-            assess_button = st.button("🚀 خطرے کا اسکور اور ترجیح معلوم کریں", use_container_width=True)
-        
-        if assess_button:
-            try:
-                # Prepare feature arrays (adjust based on your model requirements)
-                heart_features = np.array([[age, bp_systolic, cholesterol, heart_rate, 1 if chest_pain else 0]])
-                diabetes_features = np.array([[age, glucose, bmi, cholesterol]])
-                hypertension_features = np.array([[age, bp_systolic, bp_diastolic, bmi]])
-                
-                # Get predictions
-                heart_risk = heart_model.predict_proba(heart_features)[0][1]
-                diabetes_risk = diabetes_model.predict_proba(diabetes_features)[0][1]
-                hypertension_risk = hypertension_model.predict_proba(hypertension_features)[0][1]
-                
-                # Calculate priority score
-                priority_score = calculate_priority_score(heart_risk, diabetes_risk, hypertension_risk)
-                priority_level, recommendation, risk_class = get_priority_recommendation(priority_score, language)
-                
-                # Store results
-                st.session_state.risk_scores = {
-                    'heart': heart_risk,
-                    'diabetes': diabetes_risk,
-                    'hypertension': hypertension_risk,
-                    'priority': priority_score,
-                    'recommendation': recommendation,
-                    'level': priority_level
-                }
-                
-                # Display results
-                st.markdown("---")
-                
-                # Risk Scores Visualization
-                col5, col6, col7, col8 = st.columns(4)
-                
-                with col5:
-                    fig = go.Figure(go.Indicator(
-                        mode = "gauge+number+delta",
-                        value = heart_risk,
-                        domain = {'x': [0, 1], 'y': [0, 1]},
-                        title = {'text': "Heart Disease Risk"},
-                        gauge = {'axis': {'range': [0, 1]},
-                                'bar': {'color': "red"},
-                                'steps': [{'range': [0, 0.3], 'color': "lightgreen"},
-                                         {'range': [0.3, 0.7], 'color': "yellow"},
-                                         {'range': [0.7, 1], 'color': "red"}]}))
-                    st.plotly_chart(fig, use_container_width=True)
-                
-                with col6:
-                    fig = go.Figure(go.Indicator(
-                        mode = "gauge+number+delta",
-                        value = diabetes_risk,
-                        domain = {'x': [0, 1], 'y': [0, 1]},
-                        title = {'text': "Diabetes Risk"},
-                        gauge = {'axis': {'range': [0, 1]},
-                                'bar': {'color': "orange"},
-                                'steps': [{'range': [0, 0.3], 'color': "lightgreen"},
-                                         {'range': [0.3, 0.7], 'color': "yellow"},
-                                         {'range': [0.7, 1], 'color': "red"}]}))
-                    st.plotly_chart(fig, use_container_width=True)
-                
-                with col7:
-                    fig = go.Figure(go.Indicator(
-                        mode = "gauge+number+delta",
-                        value = hypertension_risk,
-                        domain = {'x': [0, 1], 'y': [0, 1]},
-                        title = {'text': "Hypertension Risk"},
-                        gauge = {'axis': {'range': [0, 1]},
-                                'bar': {'color': "blue"},
-                                'steps': [{'range': [0, 0.3], 'color': "lightgreen"},
-                                         {'range': [0.3, 0.7], 'color': "yellow"},
-                                         {'range': [0.7, 1], 'color': "red"}]}))
-                    st.plotly_chart(fig, use_container_width=True)
-                
-                with col8:
-                    fig = go.Figure(go.Indicator(
-                        mode = "gauge+number+delta",
-                        value = priority_score,
-                        domain = {'x': [0, 1], 'y': [0, 1]},
-                        title = {'text': "Priority Score"},
-                        gauge = {'axis': {'range': [0, 1]},
-                                'bar': {'color': "purple"},
-                                'steps': [{'range': [0, 0.6], 'color': "lightgreen"},
-                                         {'range': [0.6, 0.8], 'color': "yellow"},
-                                         {'range': [0.8, 1], 'color': "red"}]}))
-                    st.plotly_chart(fig, use_container_width=True)
-                
-                # Priority Recommendation
-                st.markdown(f'<div class="priority-box {risk_class}">', unsafe_allow_html=True)
-                if language == "English":
-                    st.markdown(f"## 🎯 Priority Recommendation: {recommendation}")
-                    st.markdown(f"**Overall Risk Score:** {priority_score:.3f}")
-                    st.markdown(f"**Recommended Action:** {priority_level.replace('_', ' ').title()}")
-                else:
-                    st.markdown(f"## 🎯 ترجیحی سفارش: {recommendation}")
-                    st.markdown(f"**کل خطرے کا اسکور:** {priority_score:.3f}")
-                    st.markdown(f"**سفارش کردہ عمل:** {priority_level.replace('_', ' ').title()}")
-                st.markdown('</div>', unsafe_allow_html=True)
-                
-            except Exception as e:
-                st.error(f"Error in risk assessment: {str(e)}")
-    
-    with tab2:
-        # Prescription OCR
-        if language == "English":
-            st.header("Prescription OCR Analysis")
-            st.write("Upload a prescription image to extract medication information")
-        else:
-            st.header("نسخہ OCR تجزیہ")
-            st.write("دوائی کی معلومات نکالنے کے لیے ��سخہ کی تصویر اپ لوڈ کریں")
-        
-        uploaded_file = st.file_uploader(
-            "Upload Prescription Image" if language == "English" else "نسخہ تصویر اپ لوڈ کریں",
-            type=['png', 'jpg', 'jpeg']
-        )
-        
-        if uploaded_file is not None:
-            image = Image.open(uploaded_file)
-            st.image(image, caption="Uploaded Prescription", use_column_width=True)
-            
-            if st.button("Extract Text" if language == "English" else "متن نکالیں"):
-                with st.spinner("Processing prescription..." if language == "English" else "نسخہ پروسیس ہو رہا ہے..."):
-                    extracted_text = ocr_processor.extract_text(image)
-                    
-                    if extracted_text:
-                        if language == "English":
-                            st.success("✅ Text extracted successfully!")
-                            st.subheader("Extracted Text:")
-                        else:
-                            st.success("✅ متن کامیابی سے نکال لیا گیا!")
-                            st.subheader("نکالا گیا متن:")
-                        
-                        st.text_area("", extracted_text, height=200)
-                        
-                        # Simple accuracy estimation (in real scenario, use validation dataset)
-                        word_count = len(extracted_text.split())
-                        non_empty_chars = len([c for c in extracted_text if c.strip()])
-                        
-                        if word_count > 5 and non_empty_chars > 20:
-                            estimated_accuracy = min(85, (word_count / max(1, word_count)) * 100)
-                            st.metric("Estimated OCR Accuracy", f"{estimated_accuracy:.1f}%")
-                        else:
-                            st.warning("Low confidence in OCR extraction")
-                    
-                    else:
-                        if language == "English":
-                            st.error("No text could be extracted from the image")
-                        else:
-                            st.error("تصویر سے کوئی متن نہیں نکالا جا سکا")
-    
-    with tab3:
-        # Healthcare Chatbot
-        if language == "English":
-            st.header("Healthcare Assistant Chatbot")
-            st.write("Ask health-related questions and get personalized advice")
-        else:
-            st.header("ہیلتھ کیئر اسسٹنٹ چیٹ بوٹ")
-            st.write("صحت سے متعلق سوالات پوچھیں اور ذاتی مشورہ حاصل کریں")
-        
-        # Initialize chat history
-        if 'chat_history' not in st.session_state:
-            st.session_state.chat_history = []
-        
-        # Display chat history
-        for message in st.session_state.chat_history:
-            with st.chat_message(message["role"]):
-                st.markdown(message["content"])
-        
-        # Chat input
-        if prompt := st.chat_input("Type your health question here..." if language == "English" else "اپنا صحت کا سوال یہاں ٹائپ کریں..."):
-            # Add user message to chat history
-            st.session_state.chat_history.append({"role": "user", "content": prompt})
-            with st.chat_message("user"):
-                st.markdown(prompt)
-            
-            # Generate bot response
-            with st.chat_message("assistant"):
-                with st.spinner("Thinking..." if language == "English" else "سوچ رہا ہوں..."):
-                    response = chatbot.get_response(prompt, language)
-                    st.markdown(response)
-            
-            # Add assistant response to chat history
-            st.session_state.chat_history.append({"role": "assistant", "content": response})
-    
-    with tab4:
-        # Analytics Dashboard
-        if language == "English":
-            st.header("System Analytics & Performance")
-        else:
-            st.header("سسٹم تجزیات اور کارکردگی")
-        
-        col9, col10, col11 = st.columns(3)
-        
-        with col9:
-            # Mock accuracy metrics (replace with actual validation)
-            st.metric("Diagnostic Accuracy", "87%", "2%")
-        with col10:
-            st.metric("OCR Accuracy", "82%", "3%")
-        with col11:
-            st.metric("Risk Scoring AUC", "0.85", "0.02")
-        
-        # Performance charts
-        if language == "English":
-            st.subheader("Priority Distribution")
-        else:
-            st.subheader("ترجیحی تقسیم")
-        
-        # Mock data for demonstration
-        priority_data = pd.DataFrame({
-            'Priority': ['Emergency', 'Same Day', 'Routine'],
-            'Count': [25, 45, 30]
-        })
-        
-        fig = px.pie(priority_data, values='Count', names='Priority', 
-                     title="Patient Priority Distribution")
-        st.plotly_chart(fig, use_container_width=True)
-        
-        # Model performance
-        if language == "English":
-            st.subheader("Model Performance Metrics")
-        else:
-            st.subheader("ماڈل کارکردگی کے پیمانے")
-        
-        performance_data = pd.DataFrame({
-            'Model': ['Heart Disease', 'Diabetes', 'Hypertension'],
-            'Accuracy': [0.88, 0.85, 0.86],
-            'AUC': [0.89, 0.84, 0.87]
-        })
-        
-        fig = px.bar(performance_data, x='Model', y=['Accuracy', 'AUC'],
-                     title="Model Performance Comparison", barmode='group')
-        st.plotly_chart(fig, use_container_width=True)
-
-if __name__ == "__main__":
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+import pickle
+from PIL import Image
+import io
+import cv2
+import pytesseract
+from sklearn.metrics import roc_auc_score, accuracy_score, classification_report
+import plotly.graph_objects as go
+import plotly.express as px
+from datetime import datetime
+import requests
+import json
+import base64
+
+# Custom CSS for styling
+def local_css():
+    st.markdown("""
+    <style>
+    .main-header {
+        font-size: 3rem;
+        color: #2E86AB;
+        text-align: center;
+        margin-bottom: 2rem;
+        font-weight: bold;
+    }
+    .urdu-text {
+        font-family: 'Jameel Noori Nastaleeq', 'Noto Sans Arabic';
+        font-size: 1.2rem;
+        direction: rtl;
+    }
+    .risk-high { background-color: #ffcccc; padding: 10px; border-radius: 5px; }
+    .risk-medium { background-color: #fff3cd; padding: 10px; border-radius: 5px; }
+    .risk-low { background-color: #d4edda; padding: 10px; border-radius: 5px; }
+    .priority-box { 
+        border: 2px solid #2E86AB; 
+        padding: 20px; 
+        border-radius: 10px; 
+        margin: 10px 0;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+# Initialize session state
+def init_session_state():
+    if 'language' not in st.session_state:
+        st.session_state.language = 'English'
+    if 'patient_data' not in st.session_state:
+        st.session_state.patient_data = {}
+    if 'risk_scores' not in st.session_state:
+        st.session_state.risk_scores = {}
+
+# Load models with error handling
+@st.cache_resource
+def load_models():
+    try:
+        heart_model = joblib.load("best_model.pkl")
+        diabetes_model = joblib.load("best_diabetes_model.pkl")
+        hypertension_model = joblib.load("hypertension_model.pkl")
+        return heart_model, diabetes_model, hypertension_model
+    except Exception as e:
+        st.error(f"Error loading models: {str(e)}")
+        return None, None, None
+
+# Urdu translations
+URDU_TRANSLATIONS = {
+    "AI-Priority OPD System": "AI-ترجیحی OPD سسٹم",
+    "Patient Information": "مریض کی معلومات",
+    "Name": "نام",
+    "Age": "عمر",
+    "Gender": "جنس",
+    "Contact": "رابطہ نمبر",
+    "Medical History": "طبی تاریخ",
+    "Vital Signs": "اہم علامات",
+    "Blood Pressure (systolic)": "بلڈ پریشر (سسٹولک)",
+    "Blood Pressure (diastolic)": "بلڈ پریشر ڈائیسٹولک)",
+    "Heart Rate": "دل کی دھڑکن",
+    "Cholesterol Level": "کولیسٹرول کی سطح",
+    "Blood Glucose": "خون میں گلوکوز",
+    "BMI": "باڈی ماس انڈیکس",
+    "Symptoms": "علامات",
+    "Chest Pain": "سینے میں درد",
+    "Shortness of Breath": "سانس لینے میں دشواری",
+    "Fatigue": "تھکاوٹ",
+    "Upload Prescription": "نسخہ اپ لوڈ کریں",
+    "Calculate Risk Score": "خطرے کا اسکور معلوم کریں",
+    "High Priority - Emergency Care Required": "اعلی ترجیح - ہنگامی علاج کی ضرورت",
+    "Medium Priority - Same Day Consultation": "درمیانی ترجیح - اسی دن مشورہ",
+    "Low Priority - Routine Appointment": "کم ترجیح - معمول کی ملاقات",
+    "Healthcare Chatbot": "ہیلتھ کیئر چیٹ بوٹ",
+    "Ask health-related questions": "صحت سے متعلق سوالات پوچھیں"
+}
+
+class OCRProcessor:
+    def __init__(self):
+        pass
+    
+    def preprocess_image(self, image):
+        """Enhance image for better OCR accuracy"""
+        try:
+            # Convert to grayscale
+            gray = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY)
+            
+            # Apply thresholding
+            _, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+            
+            # Remove noise
+            kernel = np.ones((1, 1), np.uint8)
+            processed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
+            
+            return processed
+        except Exception as e:
+            st.error(f"Image processing error: {str(e)}")
+            return np.array(image)
+    
+    def extract_text(self, image):
+        """Extract text from prescription image"""
+        try:
+            processed_image = self.preprocess_image(image)
+            
+            # Configure Tesseract for medical text
+            custom_config = r'--oem 3 --psm 6 -l eng'
+            text = pytesseract.image_to_string(processed_image, config=custom_config)
+            
+            return text.strip()
+        except Exception as e:
+            st.error(f"OCR Error: {str(e)}")
+            return ""
+
+class HealthcareChatbot:
+    def __init__(self):
+        self.health_tips = {
+            'heart': [
+                "Maintain a healthy diet low in saturated fats",
+                "Exercise regularly for at least 30 minutes daily",
+                "Monitor blood pressure regularly",
+                "Avoid smoking and limit alcohol consumption"
+            ],
+            'diabetes': [
+                "Monitor blood sugar levels regularly",
+                "Follow a balanced diet with controlled carbohydrates",
+                "Take medications as prescribed",
+                "Stay physically active"
+            ],
+            'hypertension': [
+                "Reduce salt intake in your diet",
+                "Practice stress management techniques",
+                "Maintain healthy body weight",
+                "Limit caffeine consumption"
+            ]
+        }
+    
+    def get_response(self, query, language='English'):
+        """Generate chatbot response"""
+        query_lower = query.lower()
+        
+        # Simple rule-based responses
+        if any(word in query_lower for word in ['heart', 'cardiac', 'chest pain']):
+            tips = self.health_tips['heart']
+        elif any(word in query_lower for word in ['diabetes', 'sugar', 'glucose']):
+            tips = self.health_tips['diabetes']
+        elif any(word in query_lower for word in ['blood pressure', 'hypertension']):
+            tips = self.health_tips['hypertension']
+        else:
+            tips = ["Maintain regular health checkups", "Follow your doctor's advice", "Stay hydrated and eat balanced meals"]
+        
+        response = f"Based on your query, here are some health tips:\n" + "\n".join([f"• {tip}" for tip in tips])
+        
+        if language == 'Urdu':
+            # Simple Urdu translations (in a real app, use proper translation API)
+            response = "آپ کے سوال کے مطابق، یہاں کچھ صحت کے نکات ہیں:\n" + "\n".join([f"• {tip}" for tip in tips])
+        
+        return response
+
+def calculate_priority_score(heart_risk, diabetes_risk, hypertension_risk):
+    """Calculate integrated priority score"""
+    # Weighted priority scoring based on clinical severity
+    priority = (
+        heart_risk * 0.4 + 
+        diabetes_risk * 0.3 + 
+        hypertension_risk * 0.3
+    )
+    
+    return priority
+
+def get_priority_recommendation(priority_score, language='English'):
+    """Get priority-based recommendation"""
+    if priority_score > 0.8:
+        if language == 'Urdu':
+            return "EMERGENCY_CARE", "اعلی ترجیح - ہنگامی علاج کی ضرورت", "risk-high"
+        else:
+            return "EMERGENCY_CARE", "High Priority - Emergency Care Required", "risk-high"
+    elif priority_score > 0.6:
+        if language == 'Urdu':
+            return "SAME_DAY_CONSULT", "درمیانی ترجیح - اسی دن مشورہ", "risk-medium"
+        else:
+            return "SAME_DAY_CONSULT", "Medium Priority - Same Day Consultation", "risk-medium"
+    else:
+        if language == 'Urdu':
+            return "ROUTINE_APPOINTMENT", "کم ترجیح - معمول کی ملاقات", "risk-low"
+        else:
+            return "ROUTINE_APPOINTMENT", "Low Priority - Routine Appointment", "risk-low"
+
+def main():
+    # Page configuration
+    st.set_page_config(
+        page_title="AI-Priority OPD System",
+        page_icon="🏥",
+        layout="wide",
+        initial_sidebar_state="expanded"
+    )
+    
+    # Load custom CSS
+    local_css()
+    init_session_state()
+    
+    # Load models
+    heart_model, diabetes_model, hypertension_model = load_models()
+    
+    if heart_model is None or diabetes_model is None or hypertension_model is None:
+        st.error("Failed to load ML models. Please check model files.")
+        return
+    
+    # Initialize processors
+    ocr_processor = OCRProcessor()
+    chatbot = HealthcareChatbot()
+    
+    # Language selector in sidebar
+    with st.sidebar:
+        st.image("https://via.placeholder.com/150x50/2E86AB/FFFFFF?text=AI-OPD", use_column_width=True)
+        language = st.radio("Select Language / زبان منتخب کریں", 
+                           ["English", "Urdu"], 
+                           key="language_selector")
+        
+        st.markdown("---")
+        st.subheader("Quick Actions")
+        if st.button("New Patient Assessment" if language == "English" else "نیا مریض تشخیص"):
+            st.session_state.patient_data = {}
+            st.session_state.risk_scores = {}
+    
+    # Main header
+    if language == "English":
+        st.markdown('<h1 class="main-header">🏥 AI-Priority OPD System</h1>', unsafe_allow_html=True)
+        st.markdown("### Smart Patient Triage and Priority Management")
+    else:
+        st.markdown('<h1 class="main-header">🏥 AI-ترجیحی OPD سسٹم</h1>', unsafe_allow_html=True)
+        st.markdown("### ذہین مریض کی درجہ بندی اور ترجیحی انتظام")
+    
+    # Create tabs
+    tab1, tab2, tab3, tab4 = st.tabs([
+        "Patient Assessment" if language == "English" else "مریض تشخیص",
+        "Prescription OCR" if language == "English" else "نسخہ OCR",
+        "Health Chatbot" if language == "English" else "ہیلتھ چیٹ بوٹ",
+        "Analytics" if language == "English" else "تجزیات"
+    ])
+    
+    with tab1:
+        # Patient Assessment Form
+        if language == "English":
+            st.header("Patient Information")
+        else:
+            st.header("مریض کی معلومات")
+        
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            # Basic Information
+            if language == "English":
+                name = st.text_input("Full Name")
+                age = st.number_input("Age", min_value=0, max_value=120, value=30)
+                gender = st.selectbox("Gender", ["Male", "Female", "Other"])
+                contact = st.text_input("Contact Number")
+            else:
+                name = st.text_input("مکمل نام")
+                age = st.number_input("عمر", min_value=0, max_value=120, value=30)
+                gender = st.selectbox("جنس", ["مرد", "عورت", "دیگر"])
+                contact = st.text_input("رابطہ نمبر")
+        
+        with col2:
+            # Vital Signs
+            if language == "English":
+                st.subheader("Vital Signs")
+                bp_systolic = st.number_input("Blood Pressure (systolic)", min_value=50, max_value=250, value=120)
+                bp_diastolic = st.number_input("Blood Pressure (diastolic)", min_value=30, max_value=150, value=80)
+                heart_rate = st.number_input("Heart Rate (bpm)", min_value=30, max_value=200, value=72)
+                cholesterol = st.number_input("Cholesterol Level (mg/dL)", min_value=100, max_value=400, value=200)
+                glucose = st.number_input("Blood Glucose (mg/dL)", min_value=50, max_value=500, value=100)
+                bmi = st.number_input("BMI", min_value=10.0, max_value=50.0, value=22.0, step=0.1)
+            else:
+                st.subheader("اہم علامات")
+                bp_systolic = st.number_input("بلڈ پریشر (سسٹولک)", min_value=50, max_value=250, value=120)
+                bp_diastolic = st.number_input("بلڈ پریشر (ڈائیسٹولک)", min_value=30, max_value=150, value=80)
+                heart_rate = st.number_input("دل کی دھڑکن (bpm)", min_value=30, max_value=200, value=72)
+                cholesterol = st.number_input("کولیسٹرول کی سطح (mg/dL)", min_value=100, max_value=400, value=200)
+                glucose = st.number_input("خون میں گلوکوز (mg/dL)", min_value=50, max_value=500, value=100)
+                bmi = st.number_input("باڈی ماس انڈیکس", min_value=10.0, max_value=50.0, value=22.0, step=0.1)
+        
+        # Symptoms
+        if language == "English":
+            st.subheader("Symptoms")
+            col3, col4 = st.columns(2)
+            with col3:
+                chest_pain = st.checkbox("Chest Pain")
+                shortness_breath = st.checkbox("Shortness of Breath")
+            with col4:
+                fatigue = st.checkbox("Fatigue")
+                dizziness = st.checkbox("Dizziness")
+        else:
+            st.subheader("علامات")
+            col3, col4 = st.columns(2)
+            with col3:
+                chest_pain = st.checkbox("سینے میں درد")
+                shortness_breath = st.checkbox("سانس لینے میں دشواری")
+            with col4:
+                fatigue = st.checkbox("تھکاوٹ")
+                dizziness = st.checkbox("چکر آنا")
+        
+        # Risk Assessment Button
+        if language == "English":
+            assess_button = st.button("🚀 Calculate Risk Score & Priority", use_container_width=True)
+        else:
+            assess_button = st.button("🚀 خطرے کا اسکور اور ترجیح معلوم کریں", use_container_width=True)
+        
+        if assess_button:
+            try:
+                # Prepare feature arrays (adjust based on your model requirements)
+                heart_features = np.array([[age, bp_systolic, cholesterol, heart_rate, 1 if chest_pain else 0]])
+                diabetes_features = np.array([[age, glucose, bmi, cholesterol]])
+                hypertension_features = np.array([[age, bp_systolic, bp_diastolic, bmi]])
+                
+                # Get predictions
+                heart_risk = heart_model.predict_proba(heart_features)[0][1]
+                diabetes_risk = diabetes_model.predict_proba(diabetes_features)[0][1]
+                hypertension_risk = hypertension_model.predict_proba(hypertension_features)[0][1]
+                
+                # Calculate priority score
+                priority_score = calculate_priority_score(heart_risk, diabetes_risk, hypertension_risk)
+                priority_level, recommendation, risk_class = get_priority_recommendation(priority_score, language)
+                
+                # Store results
+                st.session_state.risk_scores = {
+                    'heart': heart_risk,
+                    'diabetes': diabetes_risk,
+                    'hypertension': hypertension_risk,
+                    'priority': priority_score,
+                    'recommendation': recommendation,
+                    'level': priority_level
+                }
+                
+                # Display results
+                st.markdown("---")
+                
+                # Risk Scores Visualization
+                col5, col6, col7, col8 = st.columns(4)
+                
+                with col5:
+                    fig = go.Figure(go.Indicator(
+                        mode = "gauge+number+delta",
+                        value = heart_risk,
+                        domain = {'x': [0, 1], 'y': [0, 1]},
+                        title = {'text': "Heart Disease Risk"},
+                        gauge = {'axis': {'range': [0, 1]},
+                                'bar': {'color': "red"},
+                                'steps': [{'range': [0, 0.3], 'color': "lightgreen"},
+                                         {'range': [0.3, 0.7], 'color': "yellow"},
+                                         {'range': [0.7, 1], 'color': "red"}]}))
+                    st.plotly_chart(fig, use_container_width=True)
+                
+                with col6:
+                    fig = go.Figure(go.Indicator(
+                        mode = "gauge+number+delta",
+                        value = diabetes_risk,
+                        domain = {'x': [0, 1], 'y': [0, 1]},
+                        title = {'text': "Diabetes Risk"},
+                        gauge = {'axis': {'range': [0, 1]},
+                                'bar': {'color': "orange"},
+                                'steps': [{'range': [0, 0.3], 'color': "lightgreen"},
+                                         {'range': [0.3, 0.7], 'color': "yellow"},
+                                         {'range': [0.7, 1], 'color': "red"}]}))
+                    st.plotly_chart(fig, use_container_width=True)
+                
+                with col7:
+                    fig = go.Figure(go.Indicator(
+                        mode = "gauge+number+delta",
+                        value = hypertension_risk,
+                        domain = {'x': [0, 1], 'y': [0, 1]},
+                        title = {'text': "Hypertension Risk"},
+                        gauge = {'axis': {'range': [0, 1]},
+                                'bar': {'color': "blue"},
+                                'steps': [{'range': [0, 0.3], 'color': "lightgreen"},
+                                         {'range': [0.3, 0.7], 'color': "yellow"},
+                                         {'range': [0.7, 1], 'color': "red"}]}))
+                    st.plotly_chart(fig, use_container_width=True)
+                
+                with col8:
+                    fig = go.Figure(go.Indicator(
+                        mode = "gauge+number+delta",
+                        value = priority_score,
+                        domain = {'x': [0, 1], 'y': [0, 1]},
+                        title = {'text': "Priority Score"},
+                        gauge = {'axis': {'range': [0, 1]},
+                                'bar': {'color': "purple"},
+                                'steps': [{'range': [0, 0.6], 'color': "lightgreen"},
+                                         {'range': [0.6, 0.8], 'color': "yellow"},
+                                         {'range': [0.8, 1], 'color': "red"}]}))
+                    st.plotly_chart(fig, use_container_width=True)
+                
+                # Priority Recommendation
+                st.markdown(f'<div class="priority-box {risk_class}">', unsafe_allow_html=True)
+                if language == "English":
+                    st.markdown(f"## 🎯 Priority Recommendation: {recommendation}")
+                    st.markdown(f"**Overall Risk Score:** {priority_score:.3f}")
+                    st.markdown(f"**Recommended Action:** {priority_level.replace('_', ' ').title()}")
+                else:
+                    st.markdown(f"## 🎯 ترجیحی سفارش: {recommendation}")
+                    st.markdown(f"**کل خطرے کا اسکور:** {priority_score:.3f}")
+                    st.markdown(f"**سفارش کردہ عمل:** {priority_level.replace('_', ' ').title()}")
+                st.markdown('</div>', unsafe_allow_html=True)
+                
+            except Exception as e:
+                st.error(f"Error in risk assessment: {str(e)}")
+    
+    with tab2:
+        # Prescription OCR
+        if language == "English":
+            st.header("Prescription OCR Analysis")
+            st.write("Upload a prescription image to extract medication information")
+        else:
+            st.header("نسخہ OCR تجزیہ")
+            st.write("دوائی کی معلومات نکالنے کے لیے نسخہ کی تصویر اپ لوڈ کریں")
+        
+        uploaded_file = st.file_uploader(
+            "Upload Prescription Image" if language == "English" else "نسخہ تصویر اپ لوڈ کریں",
+            type=['png', 'jpg', 'jpeg']
+        )
+        
+        if uploaded_file is not None:
+            image = Image.open(uploaded_file)
+            st.image(image, caption="Uploaded Prescription", use_column_width=True)
+            
+            if st.button("Extract Text" if language == "English" else "متن نکالیں"):
+                with st.spinner("Processing prescription..." if language == "English" else "نسخہ پروسیس ہو رہا ہے..."):
+                    extracted_text = ocr_processor.extract_text(image)
+                    
+                    if extracted_text:
+                        if language == "English":
+                            st.success("✅ Text extracted successfully!")
+                            st.subheader("Extracted Text:")
+                        else:
+                            st.success("✅ متن کامیابی سے نکال لیا گیا!")
+                            st.subheader("نکالا گیا متن:")
+                        
+                        st.text_area("", extracted_text, height=200)
+                        
+                        # Simple accuracy estimation (in real scenario, use validation dataset)
+                        word_count = len(extracted_text.split())
+                        non_empty_chars = len([c for c in extracted_text if c.strip()])
+                        
+                        if word_count > 5 and non_empty_chars > 20:
+                            estimated_accuracy = min(85, (word_count / max(1, word_count)) * 100)
+                            st.metric("Estimated OCR Accuracy", f"{estimated_accuracy:.1f}%")
+                        else:
+                            st.warning("Low confidence in OCR extraction")
+                    
+                    else:
+                        if language == "English":
+                            st.error("No text could be extracted from the image")
+                        else:
+                            st.error("تصویر سے کوئی متن نہیں نکالا جا سکا")
+    
+    with tab3:
+        # Healthcare Chatbot
+        if language == "English":
+            st.header("Healthcare Assistant Chatbot")
+            st.write("Ask health-related questions and get personalized advice")
+        else:
+            st.header("ہیلتھ کیئر اسسٹنٹ چیٹ بوٹ")
+            st.write("صحت سے متعلق سوالات پوچھیں اور ذاتی مشورہ حاصل کریں")
+        
+        # Initialize chat history
+        if 'chat_history' not in st.session_state:
+            st.session_state.chat_history = []
+        
+        # Display chat history
+        for message in st.session_state.chat_history:
+            with st.chat_message(message["role"]):
+                st.markdown(message["content"])
+        
+        # Chat input
+        if prompt := st.chat_input("Type your health question here..." if language == "English" else "اپنا صحت کا سوال یہاں ٹائپ کریں..."):
+            # Add user message to chat history
+            st.session_state.chat_history.append({"role": "user", "content": prompt})
+            with st.chat_message("user"):
+                st.markdown(prompt)
+            
+            # Generate bot response
+            with st.chat_message("assistant"):
+                with st.spinner("Thinking..." if language == "English" else "سوچ رہا ہوں..."):
+                    response = chatbot.get_response(prompt, language)
+                    st.markdown(response)
+            
+            # Add assistant response to chat history
+            st.session_state.chat_history.append({"role": "assistant", "content": response})
+    
+    with tab4:
+        # Analytics Dashboard
+        if language == "English":
+            st.header("System Analytics & Performance")
+        else:
+            st.header("سسٹم تجزیات اور کارکردگی")
+        
+        col9, col10, col11 = st.columns(3)
+        
+        with col9:
+            # Mock accuracy metrics (replace with actual validation)
+            st.metric("Diagnostic Accuracy", "87%", "2%")
+        with col10:
+            st.metric("OCR Accuracy", "82%", "3%")
+        with col11:
+            st.metric("Risk Scoring AUC", "0.85", "0.02")
+        
+        # Performance charts
+        if language == "English":
+            st.subheader("Priority Distribution")
+        else:
+            st.subheader("ترجیحی تقسیم")
+        
+        # Mock data for demonstration
+        priority_data = pd.DataFrame({
+            'Priority': ['Emergency', 'Same Day', 'Routine'],
+            'Count': [25, 45, 30]
+        })
+        
+        fig = px.pie(priority_data, values='Count', names='Priority', 
+                     title="Patient Priority Distribution")
+        st.plotly_chart(fig, use_container_width=True)
+        
+        # Model performance
+        if language == "English":
+            st.subheader("Model Performance Metrics")
+        else:
+            st.subheader("ماڈل کارکردگی کے پیمانے")
+        
+        performance_data = pd.DataFrame({
+            'Model': ['Heart Disease', 'Diabetes', 'Hypertension'],
+            'Accuracy': [0.88, 0.85, 0.86],
+            'AUC': [0.89, 0.84, 0.87]
+        })
+        
+        fig = px.bar(performance_data, x='Model', y=['Accuracy', 'AUC'],
+                     title="Model Performance Comparison", barmode='group')
+        st.plotly_chart(fig, use_container_width=True)
+
+if __name__ == "__main__":
     main()