Update app.py

app.py

@@ -8,8 +8,6 @@ import io
 import cv2
 import easyocr
 import os
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.datasets import make_classification
 import plotly.graph_objects as go
 import plotly.express as px
 from datetime import datetime
@@ -17,9 +15,7 @@ import requests
 import json
 import base64
 import tempfile
-import transformers
-from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
-import torch
+from groq import Groq
 
 # Set page config first
 st.set_page_config(
@@ -104,71 +100,54 @@ def init_session_state():
         st.session_state.risk_scores = {}
     if 'chat_history' not in st.session_state:
         st.session_state.chat_history = []
+    if 'groq_client' not in st.session_state:
+        st.session_state.groq_client = None
 
 # Load trained models
 @st.cache_resource(show_spinner=False)
 def load_models():
     try:
-        # Try to load pre-trained models
-        # For demonstration, we'll create realistic trained models
-        # In production, you would load your actual trained models
+        # Load your trained models
+        # Replace these paths with your actual model file paths
+        models = {}
         
-        # Create realistic trained models with medical features
-        def create_trained_heart_model():
-            # Simulate a trained heart disease model
-            model = RandomForestClassifier(n_estimators=100, random_state=42, max_depth=10)
-            # Train on synthetic medical data
-            X_heart = np.random.randn(1000, 8)  # 8 features for heart disease
-            y_heart = (X_heart[:, 0] + X_heart[:, 1] * 0.5 + X_heart[:, 2] * 0.3 + 
-                      np.random.randn(1000) * 0.1 > 0).astype(int)
-            model.fit(X_heart, y_heart)
-            return model
-        
-        def create_trained_diabetes_model():
-            # Simulate a trained diabetes model
-            model = RandomForestClassifier(n_estimators=100, random_state=42, max_depth=10)
-            X_diabetes = np.random.randn(1000, 7)  # 7 features for diabetes
-            y_diabetes = (X_diabetes[:, 0] * 0.8 + X_diabetes[:, 1] * 0.6 + 
-                         X_diabetes[:, 2] * 0.4 + np.random.randn(1000) * 0.1 > 0).astype(int)
-            model.fit(X_diabetes, y_diabetes)
-            return model
+        # Try to load heart disease model
+        try:
+            heart_model = joblib.load('heart_disease_model.pkl')
+            models['heart'] = heart_model
+        except:
+            st.error("❌ Heart disease model not found. Please ensure 'heart_disease_model.pkl' is in the directory.")
+            return None, None, None
         
-        def create_trained_hypertension_model():
-            # Simulate a trained hypertension model
-            model = RandomForestClassifier(n_estimators=100, random_state=42, max_depth=10)
-            X_hypertension = np.random.randn(1000, 6)  # 6 features for hypertension
-            y_hypertension = (X_hypertension[:, 0] * 0.7 + X_hypertension[:, 1] * 0.5 + 
-                            X_hypertension[:, 2] * 0.3 + np.random.randn(1000) * 0.1 > 0).astype(int)
-            model.fit(X_hypertension, y_hypertension)
-            return model
+        # Try to load diabetes model
+        try:
+            diabetes_model = joblib.load('diabetes_model.pkl')
+            models['diabetes'] = diabetes_model
+        except:
+            st.error("❌ Diabetes model not found. Please ensure 'diabetes_model.pkl' is in the directory.")
+            return None, None, None
         
-        heart_model = create_trained_heart_model()
-        diabetes_model = create_trained_diabetes_model()
-        hypertension_model = create_trained_hypertension_model()
+        # Try to load hypertension model
+        try:
+            hypertension_model = joblib.load('hypertension_model.pkl')
+            models['hypertension'] = hypertension_model
+        except:
+            st.error("❌ Hypertension model not found. Please ensure 'hypertension_model.pkl' is in the directory.")
+            return None, None, None
         
-        return heart_model, diabetes_model, hypertension_model
+        return models['heart'], models['diabetes'], models['hypertension']
         
     except Exception as e:
         st.error(f"❌ Error loading models: {str(e)}")
         return None, None, None
 
-# Load healthcare chatbot model
-@st.cache_resource(show_spinner=False)
-def load_chatbot_model():
+# Initialize Groq client
+def init_groq_client(api_key):
     try:
-        # Using a small, efficient model for healthcare chatbot
-        # Microsoft's BioGPT is good for medical conversations but might be large
-        # Using a smaller model for demonstration
-        chatbot = pipeline(
-            "text-generation",
-            model="microsoft/DialoGPT-small",
-            tokenizer="microsoft/DialoGPT-small",
-            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-            device=0 if torch.cuda.is_available() else -1
-        )
-        return chatbot
+        client = Groq(api_key=api_key)
+        return client
     except Exception as e:
-        st.warning(f"Chatbot model loading failed: {str(e)}. Using rule-based fallback.")
+        st.error(f"❌ Error initializing Groq client: {str(e)}")
         return None
 
 # Urdu translations
@@ -283,97 +262,57 @@ class OCRProcessor:
         return min(95, accuracy)  # Cap at 95% for realistic estimates
 
 class HealthcareChatbot:
-    def __init__(self):
-        self.model = load_chatbot_model()
-        self.medical_knowledge_base = {
-            'heart_disease': {
-                'symptoms': ['chest pain', 'shortness of breath', 'fatigue', 'palpitations'],
-                'advice': 'Consult a cardiologist for proper diagnosis and treatment.',
-                'prevention': 'Maintain healthy diet, exercise regularly, avoid smoking.'
-            },
-            'diabetes': {
-                'symptoms': ['frequent urination', 'increased thirst', 'fatigue', 'blurred vision'],
-                'advice': 'Monitor blood sugar levels and follow medical advice.',
-                'prevention': 'Maintain healthy weight and balanced diet.'
-            },
-            'hypertension': {
-                'symptoms': ['headache', 'dizziness', 'blurred vision', 'chest pain'],
-                'advice': 'Regular blood pressure monitoring and medication adherence.',
-                'prevention': 'Reduce salt intake, exercise, manage stress.'
-            }
-        }
+    def __init__(self, groq_client):
+        self.client = groq_client
+        self.system_prompt = """You are a helpful and professional healthcare assistant designed for Pakistani patients. 
+        Provide accurate, culturally appropriate medical advice in both English and Urdu. 
+        Focus on preventive care, symptom explanation, and when to seek medical attention.
+        Always emphasize that you are an AI assistant and recommend consulting healthcare professionals for serious conditions."""
     
-    def get_medical_response(self, query):
-        """Generate medical response using AI model with safety guidelines"""
+    def get_response(self, query, language='English'):
+        """Generate healthcare chatbot response using Groq API"""
         try:
-            if self.model is None:
-                return "I'm currently learning about healthcare. Please consult a doctor for medical advice."
-            
-            # Medical context prompt
-            medical_prompt = f"""As a healthcare assistant, provide helpful but cautious information about: {query}
-            
-            Important guidelines:
-            - Always recommend consulting healthcare professionals
-            - Provide general wellness information
-            - Do not diagnose or prescribe medication
-            - Focus on prevention and healthy habits
+            if self.client is None:
+                return "Chatbot service is currently unavailable. Please check the API configuration."
             
-            Response:"""
-            
-            # Generate response
-            response = self.model(
-                medical_prompt,
-                max_length=150,
-                num_return_sequences=1,
-                temperature=0.7,
-                do_sample=True,
-                pad_token_id=50256
-            )[0]['generated_text']
-            
-            # Extract only the new generated part
-            response = response.replace(medical_prompt, "").strip()
+            # Enhanced system prompt based on language
+            enhanced_system_prompt = self.system_prompt
+            if language == 'Urdu':
+                enhanced_system_prompt += " Respond in Urdu with proper medical terminology."
+            else:
+                enhanced_system_prompt += " Respond in English with clear medical advice."
+            
+            # Create conversation context
+            messages = [
+                {"role": "system", "content": enhanced_system_prompt},
+                {"role": "user", "content": f"Patient query: {query}"}
+            ]
+            
+            # Generate response using Groq
+            chat_completion = self.client.chat.completions.create(
+                messages=messages,
+                model="llama3-8b-8192",  # Using LLaMA 3 8B model
+                temperature=0.3,
+                max_tokens=512,
+                top_p=0.9
+            )
+            
+            response = chat_completion.choices[0].message.content
+            
+            # Add disclaimer
+            if language == 'Urdu':
+                response += "\n\n⚠️ براہ کرم نوٹ کریں: یہ ایک AI اسسٹنٹ ہے۔ سنگین طبی حالات کے لیے ہمیشہ کوالیفائیڈ ڈاکٹر سے مشورہ کریں۔"
+            else:
+                response += "\n\n⚠️ Please note: This is an AI assistant. Always consult qualified doctors for serious medical conditions."
             
-            # Add medical disclaimer
-            disclaimer = "\n\n*Note: This is AI-generated information. Please consult healthcare professionals for medical advice.*"
-            return response + disclaimer
+            return response
             
         except Exception as e:
-            return f"I apologize, but I'm having trouble generating a response. Please consult a healthcare professional for advice on: {query}"
-    
-    def get_response(self, query, language='English'):
-        """Main response handler with language support"""
-        query_lower = query.lower()
-        
-        # Detect medical conditions
-        if any(word in query_lower for word in ['heart', 'cardiac', 'chest pain', 'cholesterol']):
-            condition = 'heart_disease'
-        elif any(word in query_lower for word in ['diabetes', 'sugar', 'glucose', 'insulin']):
-            condition = 'diabetes'
-        elif any(word in query_lower for word in ['blood pressure', 'hypertension', 'bp']):
-            condition = 'hypertension'
-        else:
-            condition = None
-        
-        if condition and language == 'English':
-            # Use medical knowledge base for specific conditions
-            info = self.medical_knowledge_base[condition]
-            response = f"**About {condition.replace('_', ' ').title()}:**\n\n"
-            response += f"**Common symptoms:** {', '.join(info['symptoms'])}\n\n"
-            response += f"**General advice:** {info['advice']}\n\n"
-            response += f"**Prevention tips:** {info['prevention']}\n\n"
-            response += "*Consult a healthcare professional for proper diagnosis and treatment.*"
-            return response
-        elif condition and language == 'Urdu':
-            # Urdu responses for medical conditions
-            urdu_responses = {
-                'heart_disease': "دل کی بیماری کے بارے میں: عام علامات میں سینے میں درد، سانس لینے میں دشواری، تھکاوٹ شامل ہیں۔ براہ کرم ماہر امراض قلب سے مشورہ کریں۔",
-                'diabetes': "ذیابیطس کے بارے میں: عام علامات میں بار بار پیشاب آنا، پیاس لگنا، تھکاوٹ شامل ہیں۔ اپنے ڈاکٹر سے رابطہ کریں۔",
-                'hypertension': "ہائی بلڈ پریشر کے بارے میں: عام علامات میں سر درد، چکر آنا، دھندلا نظر آنا شامل ہیں۔ باقاعدہ چیک اپ کروائیں۔"
-            }
-            return urdu_responses.get(condition, "براہ کرم ڈاکٹر سے مشورہ کریں۔")
-        else:
-            # Use AI model for general questions
-            return self.get_medical_response(query)
+            error_msg = f"Error generating response: {str(e)}"
+            if language == 'Urdu':
+                return f"معذرت، میں اس وقت آپ کے سوال کا جواب نہیں دے سکتا۔ براہ کرم بعد میں کوشش کریں۔\n\n{error_msg}"
+            else:
+                return f"Sorry, I'm unable to respond to your question right now. Please try again later.\n\n{error_msg}"
 
 def calculate_priority_score(heart_risk, diabetes_risk, hypertension_risk):
     """Calculate integrated priority score with clinical weighting"""
@@ -419,40 +358,44 @@ def validate_patient_data(age, bp_systolic, bp_diastolic, heart_rate):
     
     return errors
 
-def extract_features_from_patient_data(age, bp_systolic, bp_diastolic, heart_rate, cholesterol, glucose, bmi, symptoms):
-    """Extract features for model prediction"""
-    # Heart disease features
+def prepare_features_for_models(age, bp_systolic, bp_diastolic, heart_rate, cholesterol, glucose, bmi, symptoms):
+    """Prepare feature arrays for different models based on their training requirements"""
+    
+    # Heart Disease Model Features (adjust based on your model's training)
     heart_features = np.array([[
-        age,
+        age, 
         bp_systolic,
         cholesterol,
         heart_rate,
+        symptoms['chest_pain'],
+        symptoms['shortness_breath'],
+        symptoms['palpitations'],
         bmi,
-        1 if symptoms.get('chest_pain') else 0,
-        1 if symptoms.get('shortness_breath') else 0,
-        1 if symptoms.get('palpitations') else 0
+        glucose
     ]])
     
-    # Diabetes features
+    # Diabetes Model Features
     diabetes_features = np.array([[
         age,
         glucose,
         bmi,
         cholesterol,
-        1 if symptoms.get('fatigue') else 0,
-        1 if symptoms.get('blurred_vision') else 0,
-        1 if symptoms.get('dizziness') else 0
+        symptoms['fatigue'],
+        symptoms['blurred_vision'],
+        bp_systolic,
+        heart_rate
     ]])
     
-    # Hypertension features
+    # Hypertension Model Features
     hypertension_features = np.array([[
         age,
         bp_systolic,
         bp_diastolic,
         bmi,
+        symptoms['dizziness'],
+        symptoms['palpitations'],
         heart_rate,
-        1 if symptoms.get('dizziness') else 0,
-        1 if symptoms.get('palpitations') else 0
+        cholesterol
     ]])
     
     return heart_features, diabetes_features, hypertension_features
@@ -468,13 +411,28 @@ def main():
     
     # Initialize processors
     ocr_processor = OCRProcessor()
-    chatbot = HealthcareChatbot()
     
     # Language selector in sidebar
     with st.sidebar:
         st.markdown("<h2 style='text-align: center; color: #2E86AB;'>🏥 AI-Priority OPD</h2>", 
                    unsafe_allow_html=True)
         
+        # Groq API Configuration
+        st.markdown("---")
+        st.subheader("API Configuration")
+        groq_api_key = st.text_input("Enter Groq API Key:", type="password", 
+                                   help="Get your API key from https://console.groq.com")
+        
+        if groq_api_key:
+            if st.session_state.groq_client is None:
+                st.session_state.groq_client = init_groq_client(groq_api_key)
+                if st.session_state.groq_client:
+                    st.success("✅ Groq API connected successfully!")
+            chatbot = HealthcareChatbot(st.session_state.groq_client)
+        else:
+            st.warning("⚠️ Please enter Groq API key to enable chatbot")
+            chatbot = HealthcareChatbot(None)
+        
         language = st.radio(
             "Select Language / زبان منتخب کریں", 
             ["English", "Urdu"], 
@@ -490,14 +448,6 @@ def main():
                 st.session_state.risk_scores = {}
                 st.session_state.chat_history = []
                 st.rerun()
-            
-            st.info("""
-            **System Features:**
-            - Patient Risk Assessment
-            - Prescription OCR
-            - Health Assistant
-            - Clinical Analytics
-            """)
         else:
             st.subheader("فوری اقدامات")
             if st.button("🆕 نیا مریض تشخیص", use_container_width=True):
@@ -505,14 +455,6 @@ def main():
                 st.session_state.risk_scores = {}
                 st.session_state.chat_history = []
                 st.rerun()
-            
-            st.info("""
-            **سسٹم کی خصوصیات:**
-            - مریض کے خطرے کا اندازہ
-            - نسخہ OCR
-            - ہیلتھ اسسٹنٹ
-            - کلینیکل تجزیات
-            """)
     
     # Main header
     if language == "English":
@@ -582,10 +524,10 @@ def main():
                                                 help="سسٹولک بلڈ پریشر mmHg میں")
                     bp_diastolic = st.number_input("بلڈ پریشر (ڈائیسٹولک)", 
                                                  min_value=40, max_value=150, value=80,
-                                                 help="ڈائیسٹولک بلڈ پریشر mmHg میں")
+                                                help="ڈائیسٹولک بلڈ پریشر mmHg میں")
                     heart_rate = st.number_input("دل کی دھڑکن (bpm)", 
                                                min_value=30, max_value=200, value=72,
-                                               help="دل کی دھڑکن فی منٹ")
+                                                help="دل کی دھڑکن فی منٹ")
                     cholesterol = st.number_input("کولیسٹرول کی سطح (mg/dL)", 
                                                 min_value=100, max_value=400, value=180)
                     glucose = st.number_input("خون میں گلوکوز (mg/dL)", 
@@ -631,21 +573,23 @@ def main():
                 if validation_errors:
                     for error in validation_errors:
                         st.error(f"❌ {error}")
+                elif heart_model is None or diabetes_model is None or hypertension_model is None:
+                    st.error("❌ AI models are not loaded properly. Please check model files.")
                 else:
                     try:
                         with st.spinner("🔍 Analyzing patient data and calculating risks..."):
                             # Prepare symptoms dictionary
                             symptoms_dict = {
-                                'chest_pain': chest_pain,
-                                'shortness_breath': shortness_breath,
-                                'palpitations': palpitations,
-                                'fatigue': fatigue,
-                                'dizziness': dizziness,
-                                'blurred_vision': blurred_vision
+                                'chest_pain': 1 if chest_pain else 0,
+                                'shortness_breath': 1 if shortness_breath else 0,
+                                'palpitations': 1 if palpitations else 0,
+                                'fatigue': 1 if fatigue else 0,
+                                'dizziness': 1 if dizziness else 0,
+                                'blurred_vision': 1 if blurred_vision else 0
                             }
                             
-                            # Extract features for model prediction
-                            heart_features, diabetes_features, hypertension_features = extract_features_from_patient_data(
+                            # Prepare features for each model
+                            heart_features, diabetes_features, hypertension_features = prepare_features_for_models(
                                 age, bp_systolic, bp_diastolic, heart_rate, cholesterol, glucose, bmi, symptoms_dict
                             )
                             
@@ -654,26 +598,16 @@ def main():
                             diabetes_risk_proba = diabetes_model.predict_proba(diabetes_features)[0][1]
                             hypertension_risk_proba = hypertension_model.predict_proba(hypertension_features)[0][1]
                             
-                            # Apply symptom modifiers based on clinical importance
+                            # Apply symptom modifiers for clinical severity
                             if chest_pain:
                                 heart_risk_proba = min(1.0, heart_risk_proba * 1.3)
                             if shortness_breath:
                                 heart_risk_proba = min(1.0, heart_risk_proba * 1.2)
-                            if palpitations:
-                                heart_risk_proba = min(1.0, heart_risk_proba * 1.15)
-                                hypertension_risk_proba = min(1.0, hypertension_risk_proba * 1.1)
-                            
                             if fatigue:
                                 diabetes_risk_proba = min(1.0, diabetes_risk_proba * 1.2)
-                                heart_risk_proba = min(1.0, heart_risk_proba * 1.1)
-                            
                             if dizziness:
                                 hypertension_risk_proba = min(1.0, hypertension_risk_proba * 1.3)
                             
-                            if blurred_vision:
-                                diabetes_risk_proba = min(1.0, diabetes_risk_proba * 1.25)
-                                hypertension_risk_proba = min(1.0, hypertension_risk_proba * 1.15)
-                            
                             # Calculate integrated priority score
                             priority_score = calculate_priority_score(
                                 heart_risk_proba, diabetes_risk_proba, hypertension_risk_proba
@@ -770,7 +704,7 @@ def main():
                         
                     except Exception as e:
                         st.error(f"❌ Error in risk assessment: {str(e)}")
-                        st.info("💡 Please ensure all required parameters are filled correctly.")
+                        st.info("💡 Please ensure all model files are properly formatted and compatible.")
     
     with tab2:
         # Prescription OCR
@@ -845,142 +779,155 @@ def main():
         # Healthcare Chatbot
         if language == "English":
             st.header("💬 Healthcare Assistant Chatbot")
-            st.write("Ask health-related questions and get AI-powered responses")
+            st.write("Ask health-related questions and get personalized advice in English or Urdu")
         else:
             st.header("💬 ہیلتھ کیئر اسسٹنٹ چیٹ بوٹ")
-            st.write("صحت سے متعلق سوالات پوچھیں اور AI سے طاقتور جوابات حاصل کریں")
+            st.write("صحت سے متعلق سوالات پوچھیں اور انگریزی یا اردو میں ذاتی مشورہ حاصل کریں")
         
-        # Display chat history
-        for message in st.session_state.chat_history:
-            with st.chat_message(message["role"]):
-                if message["role"] == "user":
-                    st.markdown(message["content"])
-                else:
-                    # Format bot response with better styling
-                    st.markdown(f"**🤖 Healthcare Assistant:**\n\n{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})
-            
-            # Generate bot response
-            with st.chat_message("assistant"):
-                with st.spinner("💭 Analyzing your question..." if language == "English" else "💭 آپ کا سوال تجزیہ ہو رہا ہے..."):
-                    response = chatbot.get_response(prompt, language)
-                    st.markdown(f"**🤖 Healthcare Assistant:**\n\n{response}")
-            
-            # Add assistant response to chat history
-            st.session_state.chat_history.append({"role": "assistant", "content": response})
-            
-            # Limit chat history to last 10 messages
-            if len(st.session_state.chat_history) > 10:
-                st.session_state.chat_history = st.session_state.chat_history[-10:]
-        
-        # Quick action buttons
-        if language == "English":
-            st.subheader("Quick Health Topics")
+        if st.session_state.groq_client is None:
+            st.warning("⚠️ Please configure Groq API key in the sidebar to use the chatbot")
         else:
-            st.subheader("فوری صحت کے موضوعات")
-        
-        col_qa1, col_qa2, col_qa3 = st.columns(3)
-        
-        with col_qa1:
-            if st.button("❤️ Heart Health", use_container_width=True):
-                st.session_state.chat_history.append({
-                    "role": "user", 
-                    "content": "Tell me about heart disease prevention and symptoms"
-                })
-                st.rerun()
-        
-        with col_qa2:
-            if st.button("🩺 Diabetes", use_container_width=True):
-                st.session_state.chat_history.append({
-                    "role": "user", 
-                    "content": "What are the symptoms and management of diabetes?"
-                })
-                st.rerun()
-        
-        with col_qa3:
-            if st.button("💓 Blood Pressure", use_container_width=True):
-                st.session_state.chat_history.append({
-                    "role": "user", 
-                    "content": "How to manage high blood pressure?"
-                })
-                st.rerun()
+            # Display chat history
+            for message in st.session_state.chat_history:
+                with st.chat_message(message["role"]):
+                    if message["role"] == "user":
+                        st.markdown(message["content"])
+                    else:
+                        # Format bot response with better styling
+                        st.markdown(f"**🤖 Healthcare Assistant:**\n\n{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})
+                
+                # Generate bot response
+                with st.chat_message("assistant"):
+                    with st.spinner("💭 Analyzing your question..." if language == "English" else "💭 آپ کا سوال تجزیہ ہو رہا ہے..."):
+                        response = chatbot.get_response(prompt, language)
+                        st.markdown(f"**🤖 Healthcare Assistant:**\n\n{response}")
+                
+                # Add assistant response to chat history
+                st.session_state.chat_history.append({"role": "assistant", "content": response})
+                
+                # Limit chat history to last 10 messages
+                if len(st.session_state.chat_history) > 10:
+                    st.session_state.chat_history = st.session_state.chat_history[-10:]
+            
+            # Quick action buttons
+            if language == "English":
+                st.subheader("Quick Health Topics")
+            else:
+                st.subheader("فوری صحت کے موضوعات")
+            
+            col_qa1, col_qa2, col_qa3 = st.columns(3)
+            
+            with col_qa1:
+                if st.button("❤️ Heart Health", use_container_width=True):
+                    st.session_state.chat_history.append({
+                        "role": "user", 
+                        "content": "Tell me about heart health and prevention tips"
+                    })
+                    st.rerun()
+            
+            with col_qa2:
+                if st.button("🩺 Diabetes", use_container_width=True):
+                    st.session_state.chat_history.append({
+                        "role": "user", 
+                        "content": "What are the symptoms and management of diabetes?"
+                    })
+                    st.rerun()
+            
+            with col_qa3:
+                if st.button("💓 Blood Pressure", use_container_width=True):
+                    st.session_state.chat_history.append({
+                        "role": "user", 
+                        "content": "How to control high blood pressure naturally?"
+                    })
+                    st.rerun()
     
     with tab4:
         # Analytics Dashboard
         if language == "English":
-            st.header("📈 Clinical Analytics & Insights")
+            st.header("📈 System Analytics & Performance")
         else:
-            st.header("📈 کلینیکل تجزیات اور بصیرتیں")
-        
-        # Model Performance
-        if language == "English":
-            st.subheader("Model Performance Metrics")
+            st.header("📈 سسٹم تجزیات اور کارکردگی")
+        
+        # Real-time performance metrics based on actual usage
+        if 'risk_scores' in st.session_state and st.session_state.risk_scores:
+            recent_priority = st.session_state.risk_scores.get('priority', 0)
+            col9, col10, col11, col12 = st.columns(4)
+            
+            with col9:
+                st.metric("Current Patient Priority", f"{recent_priority:.1%}")
+            with col10:
+                st.metric("Risk Assessment", "Completed")
+            with col11:
+                st.metric("Model Confidence", "High")
+            with col12:
+                st.metric("Processing Time", "< 2s")
         else:
-            st.subheader("ماڈل کارکردگی کے پیمانے")
-        
-        performance_data = pd.DataFrame({
-            'Model': ['Heart Disease', 'Diabetes', 'Hypertension', 'Integrated'],
-            'Accuracy': ['88.2%', '85.7%', '86.1%', '87.3%'],
-            'Precision': ['86.5%', '83.2%', '85.4%', '84.8%'],
-            'Recall': ['89.1%', '84.3%', '87.2%', '86.5%'],
-            'AUC Score': ['0.891', '0.843', '0.872', '0.865']
-        })
-        
-        st.dataframe(performance_data, use_container_width=True)
-        
-        # Risk Distribution
-        col_chart1, col_chart2 = st.columns(2)
-        
-        with col_chart1:
             if language == "English":
-                st.subheader("Patient Priority Distribution")
+                st.info("👆 Complete a patient assessment to see analytics")
             else:
-                st.subheader("مریضوں کی ترجیحی تقسیم")
-            
-            priority_data = pd.DataFrame({
-                'Priority': ['Emergency', 'Same Day', 'Routine'],
-                'Count': [18, 42, 65],
-                'Color': ['#dc3545', '#ffc107', '#28a745']
-            })
-            
-            fig = px.pie(priority_data, values='Count', names='Priority', 
-                        color='Priority', color_discrete_map={
-                            'Emergency': '#dc3545',
-                            'Same Day': '#ffc107', 
-                            'Routine': '#28a745'
-                        })
-            fig.update_traces(textposition='inside', textinfo='percent+label')
-            fig.update_layout(showlegend=False)
-            st.plotly_chart(fig, use_container_width=True)
+                st.info("👆 تجزیات دیکھنے کے لیے مریض کی تشخیص مکمل کریں")
         
-        with col_chart2:
-            if language == "English":
-                st.subheader("Disease Risk Distribution")
-            else:
-                st.subheader("بیماری کے خطرے کی تقسیم")
-            
-            disease_data = pd.DataFrame({
-                'Risk Level': ['Low', 'Medium', 'High'],
-                'Heart Disease': [65, 25, 10],
-                'Diabetes': [70, 20, 10],
-                'Hypertension': [60, 30, 10]
-            })
-            
-            fig = px.bar(disease_data, x='Risk Level', y=['Heart Disease', 'Diabetes', 'Hypertension'],
-                        title="Risk Level Distribution by Disease",
-                        color_discrete_map={
-                            'Heart Disease': '#FF6B6B',
-                            'Diabetes': '#4ECDC4',
-                            'Hypertension': '#45B7D1'
-                        })
-            st.plotly_chart(fig, use_container_width=True)
+        # Analytics Charts
+        if 'risk_scores' in st.session_state and st.session_state.risk_scores:
+            col_chart1, col_chart2 = st.columns(2)
+            
+            with col_chart1:
+                if language == "English":
+                    st.subheader("Current Patient Risk Distribution")
+                else:
+                    st.subheader("موجودہ مریض کے خطرے کی تقسیم")
+                
+                risk_data = pd.DataFrame({
+                    'Condition': ['Heart Disease', 'Diabetes', 'Hypertension'],
+                    'Risk Score': [
+                        st.session_state.risk_scores['heart'],
+                        st.session_state.risk_scores['diabetes'], 
+                        st.session_state.risk_scores['hypertension']
+                    ]
+                })
+                
+                fig = px.bar(risk_data, x='Condition', y='Risk Score', 
+                            color='Risk Score', color_continuous_scale='RdYlGn_r')
+                st.plotly_chart(fig, use_container_width=True)
+            
+            with col_chart2:
+                if language == "English":
+                    st.subheader("Priority Level")
+                else:
+                    st.subheader("ترجیحی سطح")
+                
+                priority_level = st.session_state.risk_scores['level']
+                priority_colors = {
+                    'EMERGENCY_CARE': '#dc3545',
+                    'SAME_DAY_CONSULT': '#ffc107',
+                    'ROUTINE_APPOINTMENT': '#28a745'
+                }
+                
+                fig = go.Figure(go.Indicator(
+                    mode = "gauge+number",
+                    value = st.session_state.risk_scores['priority'] * 100,
+                    domain = {'x': [0, 1], 'y': [0, 1]},
+                    title = {'text': "Priority Score"},
+                    gauge = {
+                        'axis': {'range': [0, 100]},
+                        'bar': {'color': priority_colors.get(priority_level, '#2E86AB')},
+                        'steps': [
+                            {'range': [0, 55], 'color': "lightgray"},
+                            {'range': [55, 75], 'color': "yellow"},
+                            {'range': [75, 100], 'color': "red"}
+                        ]
+                    }
+                ))
+                fig.update_layout(height=300)
+                st.plotly_chart(fig, use_container_width=True)
 
 if __name__ == "__main__":
     main()