Update app.py

app.py

@@ -1,518 +1,514 @@
-from flask import Flask, request, render_template, session, url_for, redirect, jsonify
-from flask_session import Session
-from langchain_core.messages import HumanMessage, AIMessage
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import os
-import logging
-import re
-import traceback
-import base64
-import shutil
-import zipfile
-from dotenv import load_dotenv
-from huggingface_hub import hf_hub_download
-from PIL import Image
-
-# --- Core Application Imports ---
-# Make sure you have an empty __init__.py file in your 'src' folder
-from api import api_bp
-from src.medical_swarm import run_medical_swarm
-from src.utils import load_rag_system, standardize_query, get_standalone_question, parse_agent_response, markdown_bold_to_html
-from langchain_google_genai import ChatGoogleGenerativeAI
-
-# Setup logging
-logging.basicConfig(level=logging.DEBUG)
-logger = logging.getLogger(__name__)
-
-# Load environment variables
-load_dotenv()
-
-# --- 1. DATABASE SETUP FUNCTION (For Deployment) ---
-def setup_database():
-    """Downloads and unzips the ChromaDB folder from Hugging Face Datasets."""
-    
-    # --- !!! IMPORTANT !!! ---
-    # YOU MUST CHANGE THIS to your Hugging Face Dataset repo ID
-    # For example: "your_username/your_database_repo_name"
-    DATASET_REPO_ID = "WanIrfan/atlast-db" 
-    # -------------------------
-
-    ZIP_FILENAME = "chroma_db.zip"
-    DB_DIR = "chroma_db"
-
-    if os.path.exists(DB_DIR) and os.listdir(DB_DIR):
-        logger.info("✅ Database directory already exists. Skipping download.")
-        return
-
-    logger.info(f"📥 Downloading database from HF Hub: {DATASET_REPO_ID}")
-    try:
-        zip_path = hf_hub_download(
-            repo_id=DATASET_REPO_ID,
-            filename=ZIP_FILENAME,
-            repo_type="dataset",
-            # You might need to add your HF token to secrets if the dataset is private
-            # token=os.getenv("HF_TOKEN") 
-        )
-        
-        logger.info(f"📦 Unzipping database from {zip_path}...")
-        with zipfile.ZipFile(zip_path, 'r') as zip_ref:
-            zip_ref.extractall(".") # Extracts to the root, creating ./chroma_db
-            
-        logger.info("✅ Database setup complete!")
-        
-        # Clean up the downloaded zip file to save space
-        if os.path.exists(zip_path):
-            os.remove(zip_path)
-            
-    except Exception as e:
-        logger.error(f"❌ CRITICAL ERROR setting up database: {e}", exc_info=True)
-        # This will likely cause the RAG system to fail loading, which is expected
-        # if the database isn't available.
-
-# --- RUN DATABASE SETUP *BEFORE* INITIALIZING THE APP ---
-setup_database()
-
-
-# --- STANDARD FLASK APP INITIALIZATION ---
-app = Flask(__name__)
-app.secret_key = os.urandom(24)  # Set a secret key for session signing
-
-# --- CONFIGURE SERVER-SIDE SESSIONS ---
-app.config["SESSION_PERMANENT"] = False
-app.config["SESSION_TYPE"] = "filesystem"
-Session(app)
-
-google_api_key = os.getenv("GOOGLE_API_KEY")
-if not google_api_key:
-    logger.warning("⚠️ GOOGLE_API_KEY not found in environment variables. LLM calls will fail.")
-else:
-    logger.info("GOOGLE_API_KEY loaded successfully.")
-
-# Initialize LLM
-llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", temperature=0.05, google_api_key=google_api_key)
-
-# --- LOAD RAG SYSTEMS (AFTER DB SETUP) ---
-logger.info("🌟 Starting Multi-Domain AI Assistant...")
-try:
-    rag_systems = {
-        'medical': load_rag_system(collection_name="medical_csv_Agentic_retrieval", domain="medical"),
-        'islamic': load_rag_system(collection_name="islamic_texts_Agentic_retrieval", domain="islamic"),
-        'insurance': load_rag_system(collection_name="etiqa_Agentic_retrieval", domain="insurance")
-    }
-except Exception as e:
-    logger.error(f"❌ FAILED to load RAG systems. Check database path and permissions. Error: {e}", exc_info=True)
-    rag_systems = {'medical': None, 'islamic': None, 'insurance': None}
-
-# Store systems and LLM on the app for blueprints
-app.rag_systems = rag_systems
-app.llm = llm
-
-# Register the API blueprint
-app.register_blueprint(api_bp)
-logger.info(f"✅ API Blueprint registered. API endpoints are now available under /api")
-
-# Check initialization status
-logger.info("\n📊 SYSTEM STATUS:")
-for domain, system in rag_systems.items():
-    status = "✅ Ready" if system else "❌ Failed (DB missing?)" 
-    logger.info(f"   {domain}: {status}")
-
-
-# --- FLASK ROUTES ---
-
-@app.route("/")
-def homePage():
-    # Clear all session history when visiting the home page
-    session.pop('medical_history', None)
-    session.pop('islamic_history', None)
-    session.pop('insurance_history', None)
-    session.pop('current_medical_document', None)
-    return render_template("homePage.html")
-
-
-@app.route("/medical", methods=["GET", "POST"])
-def medical_page():
-    # Use session for history and document context
-    if request.method == "GET":
-        # Load all latest data from session (or default to empty if not found)
-        latest_response = session.pop('latest_medical_response', {}) # POP to clear it after one display
-        
-        answer = latest_response.get('answer', "")
-        thoughts = latest_response.get('thoughts', "")
-        validation = latest_response.get('validation', "")
-        source = latest_response.get('source', "")
-
-        # Clear history only when a user first navigates (not on redirect)
-        if not latest_response and 'medical_history' not in session:
-            session.pop('current_medical_document', None)
-        
-        return render_template("medical_page.html", 
-                               history=session.get('medical_history', []),
-                               answer=answer,
-                               thoughts=thoughts,
-                               validation=validation,
-                               source=source)
-    
-    # POST Request Logic
-    answer, thoughts, validation, source = "", "", "", ""
-    history = session.get('medical_history', [])
-    current_medical_document = session.get('current_medical_document', "")
-    
-    
-    try:
-        query=standardize_query(request.form.get("query", ""))
-        has_image = 'image' in request.files and request.files['image'].filename
-        has_document = 'document' in request.files and request.files['document'].filename
-        has_query = request.form.get("query") or request.form.get("question", "")
-            
-        logger.info(f"POST request received: has_image={has_image}, has_document={has_document}, has_query={has_query}")
-            
-        if has_document:
-            # Scenario 3: Query + Document
-            logger.info("Processing Scenario 3: Query + Document with Medical Swarm")
-            file = request.files['document']
-            try:
-                # Store the new document text in the session
-                document_text = file.read().decode("utf-8")
-                session['current_medical_document'] = document_text
-                current_medical_document = document_text # Use the new document for this turn
-            except UnicodeDecodeError:
-                answer = "Error: Could not decode the uploaded document. Please ensure it is a valid text or PDF file."
-                logger.error("Scenario 3: Document decode error")
-                thoughts = traceback.format_exc()
-                  
-            swarm_answer = run_medical_swarm(current_medical_document, query)
-            answer = markdown_bold_to_html(swarm_answer)
-                
-            history.append(HumanMessage(content=f"[Document Uploaded] Query: '{query}'"))
-            history.append(AIMessage(content=swarm_answer))
-            thoughts = "Swarm analysis complete. The process is orchestrated and does not use the ReAct thought process. You can now ask follow-up questions."
-            source= "Medical Swarm"
-            validation = (True, "Swarm output generated.") # Swarm has its own validation logic
-            
-        elif has_image :
-            #Scenario 1 
-            logger.info("Processing Multimodal RAG: Query + Image")
-            # --- Step 1 & 2: Image Setup & Vision Analysis ---
-            file = request.files['image']
-            upload_dir = "Uploads"
-            os.makedirs(upload_dir, exist_ok=True)
-            image_path = os.path.join(upload_dir, file.filename)
-            
-            try:
-                file.save(image_path)
-                file.close()
-            
-                with open(image_path, "rb") as img_file:
-                    img_data = base64.b64encode(img_file.read()).decode("utf-8")
-                
-            
-                vision_prompt = f"Analyze this image and identify the main subject in a single, concise sentence. The user's query is: '{query}'"
-                message = HumanMessage(content=[
-                    {"type": "text", "text": vision_prompt},
-                    {"type": "image_url", "image_url": f"data:image/jpeg;base64,{img_data}"}
-                ])
-                vision_response = llm.invoke([message])
-                visual_prediction = vision_response.content
-                logger.info(f"Vision Prediction: {visual_prediction}")
-
-                # --- Create an Enhanced Query ---
-                enhanced_query = (
-                    f'User Query: "{query}" '
-                    f'Context from an image provided by the LLM: "{visual_prediction}" '
-                    'Based on the user\'s query and the context from LLM, provide a comprehensive answer.'
-                )
-                logger.info(f"Enhanced query : {enhanced_query}")
-            
-                agent = rag_systems['medical']
-                if not agent: raise Exception("Medical RAG system is not loaded.")
-                response_dict = agent.answer(enhanced_query, chat_history=history)
-                answer, thoughts, validation, source = parse_agent_response(response_dict)
-                history.append(HumanMessage(content=query))
-                history.append(AIMessage(content=answer))
-            
-            finally:
-                if os.path.exists(image_path):
-                    try:
-                        os.remove(image_path)
-                        logger.info(f"Successfully deleted temporary image file: {image_path}")
-                    except PermissionError as e:
-                        logger.warning(f"Could not remove {image_path} after processing. "
-                                       f"File may be locked by another process. Error: {e}")
-            
-        elif query:
-            # --- SCENARIO 2: TEXT-ONLY QUERY OR SWARM FOLLOW-UP ---
-            history_for_agent = history
-            if current_medical_document:
-                logger.info("Processing Follow-up Query for Document")
-                history_for_agent = [HumanMessage(content=f"We are discussing this document:\n{current_medical_document}")] + history
-            else:
-                logger.info("Processing Text RAG query for Medical domain")
-            
-            logger.info(f"Original Query: '{query}'")
-            print(f"📚 Using chat history with {len(history)} previous messages to create standalone query")
-            standalone_query = get_standalone_question(query, history_for_agent,llm)
-            logger.info(f"Standalone Query: '{standalone_query}'")
-            
-            agent = rag_systems['medical']
-            if not agent: raise Exception("Medical RAG system is not loaded.")
-            response_dict = agent.answer(standalone_query, chat_history=history_for_agent)
-            answer, thoughts, validation, source = parse_agent_response(response_dict)
-
-            history.append(HumanMessage(content=query))
-            history.append(AIMessage(content=answer))
-
-        else:
-            raise ValueError("No query or file provided.")
-    except Exception as e:
-        logger.error(f"Error on /medical page: {e}", exc_info=True)
-        answer = f"An error occurred: {e}"
-        thoughts = traceback.format_exc()
-    
-    # Save updated history and LATEST RESPONSE DATA back to the session
-    session['medical_history'] = history
-    session['latest_medical_response'] = {
-        'answer': answer, 
-        'thoughts': thoughts, 
-        'validation': validation, 
-        'source': source
-    }
-    session.modified = True
-                             
-    logger.debug(f"Redirecting after saving latest response.")
-    return redirect(url_for('medical_page'))
-
-@app.route("/medical/clear")
-def clear_medical_chat():
-    session.pop('medical_history', None)
-    session.pop('current_medical_document', None)
-    logger.info("Medical chat history cleared.")
-    return redirect(url_for('medical_page'))
-
-@app.route("/islamic", methods=["GET", "POST"])
-def islamic_page():
-    #Use session
-    
-    if request.method == "GET":
-        # Load all latest data from session (or default to empty if not found)
-        latest_response = session.pop('latest_islamic_response', {}) # POP to clear it after one display
-        
-        answer = latest_response.get('answer', "")
-        thoughts = latest_response.get('thoughts', "")
-        validation = latest_response.get('validation', "")
-        source = latest_response.get('source', "")
-        
-        # Clear history only when a user first navigates (no latest_response and no current history)
-        if not latest_response and 'islamic_history' not in session:
-            session.pop('islamic_history', None)
-        
-        return render_template("islamic_page.html", 
-                                history=session.get('islamic_history', []),
-                                answer=answer,
-                                thoughts=thoughts,
-                                validation=validation,
-                                source=source)
-    
-    # POST Request Logic
-    answer, thoughts, validation, source = "", "", "", ""
-    history = session.get('islamic_history', [])
-    
-    # This try/except block wraps the ENTIRE POST logic
-    try:
-        query = standardize_query(request.form.get("query", ""))
-        has_image = 'image' in request.files and request.files['image'].filename
-        
-        final_query = query # Default to the original query
-        
-        if has_image:
-            logger.info("Processing Multimodal RAG query for Islamic domain")
-            
-            file = request.files['image']
-            
-            upload_dir = "Uploads"
-            os.makedirs(upload_dir, exist_ok=True)
-            image_path = os.path.join(upload_dir, file.filename)
-            
-            try:
-                file.save(image_path)
-                file.close() 
-                
-                with open(image_path, "rb") as img_file:
-                    img_base64 = base64.b64encode(img_file.read()).decode("utf-8")
-                
-                vision_prompt = f"Analyze this image's main subject. User's query is: '{query}'"
-                message = HumanMessage(content=[{"type": "text", "text": vision_prompt}, {"type": "image_url", "image_url": f"data:image/jpeg;base64,{img_base64}"}])
-                visual_prediction = llm.invoke([message]).content
-
-                enhanced_query = (
-                    f'User Query: "{query}" '
-                    f'Context from an image provided by the LLM: "{visual_prediction}" '
-                    'Based on the user\'s query and the context from LLM, provide a comprehensive answer.'
-                )
-                logger.info(f"Create enchanced query : {enhanced_query}")
-                
-                final_query = enhanced_query 
-            
-            finally:
-                if os.path.exists(image_path):
-                    try:
-                        os.remove(image_path)
-                        logger.info(f"Successfully cleaned up {image_path}")
-                    except PermissionError as e:
-                        logger.warning(f"Could not remove {image_path} after processing. "
-                                       f"File may be locked. Error: {e}")
-            
-        elif query: # Only run text logic if there's a query and no image
-            logger.info("Processing Text RAG query for Islamic domain")
-            standalone_query = get_standalone_question(query, history,llm)
-            logger.info(f"Original Query: '{query}'")
-            print(f"📚 Using chat history with {len(history)} previous messages to create standalone query")
-            logger.info(f"Standalone Query: '{standalone_query}'")
-            final_query = standalone_query
-            
-        if not final_query: 
-            raise ValueError("No query or file provided.")
-        
-        agent = rag_systems['islamic']
-        if not agent: raise Exception("Islamic RAG system is not loaded.")
-        response_dict = agent.answer(final_query, chat_history=history)
-        answer, thoughts , validation, source = parse_agent_response(response_dict)
-        history.append(HumanMessage(content=query))
-        history.append(AIMessage(content=answer))
-
-    except Exception as e:
-        logger.error(f"Error on /islamic page: {e}", exc_info=True)
-        answer = f"An error occurred: {e}"
-        thoughts = traceback.format_exc()
-            
-    # Save updated history and LATEST RESPONSE DATA back to the session
-    session['islamic_history'] = history
-    session['latest_islamic_response'] = {
-        'answer': answer, 
-        'thoughts': thoughts, 
-        'validation': validation, 
-        'source': source
-    }
-    session.modified = True
-                        
-    logger.debug(f"Redirecting after saving latest response.")
-    return redirect(url_for('islamic_page'))
-
-@app.route("/islamic/clear")
-def clear_islamic_chat():
-    session.pop('islamic_history', None)
-    logger.info("Islamic chat history cleared.")
-    return redirect(url_for('islamic_page'))
-
-@app.route("/insurance", methods=["GET", "POST"])
-def insurance_page():
-    if request.method == "GET" :
-        latest_response = session.pop('latest_insurance_response',{})
-        
-        answer = latest_response.get('answer', "")
-        thoughts = latest_response.get('thoughts', "")
-        validation = latest_response.get('validation', "")
-        source = latest_response.get('source', "")
-        
-        if not latest_response and 'insurance_history' not in session:
-            session.pop('insurance_history', None)
-        
-        return render_template("insurance_page.html", # You will need to create this HTML file
-                                history=session.get('insurance_history', []),
-                                answer=answer,
-                                thoughts=thoughts,
-                                validation=validation,
-                                source=source)
-    
-    # POST Request Logic
-    answer, thoughts, validation, source = "", "", "", ""
-    history = session.get('insurance_history', [])
-    
-    try:
-        query = standardize_query(request.form.get("query", ""))
-        
-        if query:
-            logger.info("Processing Text RAG query for Insurance domain")
-            standalone_query = get_standalone_question(query, history, llm)
-            logger.info(f"Original Query: '{query}'")
-            logger.info(f"Standalone Query: '{standalone_query}'")
-            
-            agent = rag_systems['insurance']
-            if not agent: raise Exception("Insurance RAG system is not loaded.")
-            response_dict = agent.answer(standalone_query, chat_history=history)
-            answer, thoughts, validation, source = parse_agent_response(response_dict)
-            
-            history.append(HumanMessage(content=query))
-            history.append(AIMessage(content=answer))
-        else:
-            raise ValueError("No query provided.")
-
-    except Exception as e:
-        logger.error(f"Error on /insurance page: {e}", exc_info=True)
-        answer = f"An error occurred: {e}"
-        thoughts = traceback.format_exc()
-            
-    session['insurance_history'] = history
-    session['latest_insurance_response'] = {
-        'answer': answer, 
-        'thoughts': thoughts, 
-        'validation': validation, 
-        'source': source
-    }
-    session.modified = True
-                        
-    logger.debug(f"Redirecting after saving latest response.")
-    return redirect(url_for('insurance_page'))
-
-@app.route("/insurance/clear")
-def clear_insurance_chat():
-    session.pop('insurance_history', None)
-    logger.info("Insurance chat history cleared.")
-    return redirect(url_for('insurance_page'))
-
-@app.route("/about", methods=["GET"])
-def about():
-    return render_template("about.html")
-
-@app.route('/metrics/<domain>')
-def get_metrics(domain):
-    """API endpoint to get metrics for a specific domain."""
-    try:
-        if domain == "medical" and rag_systems['medical']:
-            stats = rag_systems['medical'].metrics_tracker.get_stats()
-        elif domain == "islamic" and rag_systems['islamic']:
-            stats = rag_systems['islamic'].metrics_tracker.get_stats()
-        elif domain == "insurance" and rag_systems['insurance']:
-            stats = rag_systems['insurance'].metrics_tracker.get_stats()
-        elif not rag_systems.get(domain):
-            return jsonify({"error": f"{domain} RAG system not loaded"}), 500
-        else:
-            return jsonify({"error": "Invalid domain"}), 400
-        
-        return jsonify(stats)
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
-
-@app.route('/metrics/reset/<domain>', methods=['POST'])
-def reset_metrics(domain):
-    """Reset metrics for a domain (useful for testing)."""
-    try:
-        if domain == "medical" and rag_systems['medical']:
-            rag_systems['medical'].metrics_tracker.reset_metrics()
-        elif domain == "islamic" and rag_systems['islamic']:
-            rag_systems['islamic'].metrics_tracker.reset_metrics()
-        elif domain == "insurance" and rag_systems['insurance']:
-            rag_systems['insurance'].metrics_tracker.reset_metrics()
-        elif not rag_systems.get(domain):
-            return jsonify({"error": f"{domain} RAG system not loaded"}), 500
-        else:
-            return jsonify({"error": "Invalid domain"}), 400
-        
-        return jsonify({"success": True, "message": f"Metrics reset for {domain}"})
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
-
-if __name__ == "__main__":
-    logger.info("Starting Flask app for deployment testing...")
-    # This port 7860 is what Hugging Face Spaces expects by default
+from flask import Flask, request, render_template, session, url_for, redirect, jsonify
+from flask_session import Session
+from langchain_core.messages import HumanMessage, AIMessage
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+import os
+import logging
+import re
+import traceback
+import base64
+import shutil
+import zipfile
+from dotenv import load_dotenv
+from huggingface_hub import hf_hub_download
+
+# --- Core Application Imports ---
+# Make sure you have an empty __init__.py file in your 'src' folder
+
+from src.medical_swarm import run_medical_swarm
+from src.utils import load_rag_system, standardize_query, get_standalone_question, parse_agent_response, markdown_bold_to_html
+from langchain_google_genai import ChatGoogleGenerativeAI
+
+# Setup logging
+logging.basicConfig(level=logging.DEBUG)
+logger = logging.getLogger(__name__)
+
+# Load environment variables
+load_dotenv()
+
+# --- 1. DATABASE SETUP FUNCTION (For Deployment) ---
+def setup_database():
+    """Downloads and unzips the ChromaDB folder from Hugging Face Datasets."""
+    
+    # --- !!! IMPORTANT !!! ---
+    # YOU MUST CHANGE THIS to your Hugging Face Dataset repo ID
+    # For example: "your_username/your_database_repo_name"
+    DATASET_REPO_ID = "WanIrfan/atlast-db" 
+    # -------------------------
+
+    ZIP_FILENAME = "chroma_db.zip"
+    DB_DIR = "chroma_db"
+
+    if os.path.exists(DB_DIR) and os.listdir(DB_DIR):
+        logger.info("✅ Database directory already exists. Skipping download.")
+        return
+
+    logger.info(f"📥 Downloading database from HF Hub: {DATASET_REPO_ID}")
+    try:
+        zip_path = hf_hub_download(
+            repo_id=DATASET_REPO_ID,
+            filename=ZIP_FILENAME,
+            repo_type="dataset",
+            # You might need to add your HF token to secrets if the dataset is private
+            # token=os.getenv("HF_TOKEN") 
+        )
+        
+        logger.info(f"📦 Unzipping database from {zip_path}...")
+        with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+            zip_ref.extractall(".") # Extracts to the root, creating ./chroma_db
+            
+        logger.info("✅ Database setup complete!")
+        
+        # Clean up the downloaded zip file to save space
+        if os.path.exists(zip_path):
+            os.remove(zip_path)
+            
+    except Exception as e:
+        logger.error(f"❌ CRITICAL ERROR setting up database: {e}", exc_info=True)
+        # This will likely cause the RAG system to fail loading, which is expected
+        # if the database isn't available.
+
+# --- RUN DATABASE SETUP *BEFORE* INITIALIZING THE APP ---
+setup_database()
+
+
+# --- STANDARD FLASK APP INITIALIZATION ---
+app = Flask(__name__)
+app.secret_key = os.urandom(24)  # Set a secret key for session signing
+
+# --- CONFIGURE SERVER-SIDE SESSIONS ---
+app.config["SESSION_PERMANENT"] = False
+app.config["SESSION_TYPE"] = "filesystem"
+Session(app)
+
+google_api_key = os.getenv("GOOGLE_API_KEY")
+if not google_api_key:
+    logger.warning("⚠️ GOOGLE_API_KEY not found in environment variables. LLM calls will fail.")
+else:
+    logger.info("GOOGLE_API_KEY loaded successfully.")
+
+# Initialize LLM
+llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", temperature=0.05, google_api_key=google_api_key)
+
+# --- LOAD RAG SYSTEMS (AFTER DB SETUP) ---
+logger.info("🌟 Starting Multi-Domain AI Assistant...")
+try:
+    rag_systems = {
+        'medical': load_rag_system(collection_name="medical_csv_Agentic_retrieval", domain="medical"),
+        'islamic': load_rag_system(collection_name="islamic_texts_Agentic_retrieval", domain="islamic"),
+        'insurance': load_rag_system(collection_name="etiqa_Agentic_retrieval", domain="insurance")
+    }
+except Exception as e:
+    logger.error(f"❌ FAILED to load RAG systems. Check database path and permissions. Error: {e}", exc_info=True)
+    rag_systems = {'medical': None, 'islamic': None, 'insurance': None}
+
+# Store systems and LLM on the app for blueprints
+app.rag_systems = rag_systems
+app.llm = llm
+
+
+# Check initialization status
+logger.info("\n📊 SYSTEM STATUS:")
+for domain, system in rag_systems.items():
+    status = "✅ Ready" if system else "❌ Failed (DB missing?)" 
+    logger.info(f"   {domain}: {status}")
+
+
+# --- FLASK ROUTES ---
+
+@app.route("/")
+def homePage():
+    # Clear all session history when visiting the home page
+    session.pop('medical_history', None)
+    session.pop('islamic_history', None)
+    session.pop('insurance_history', None)
+    session.pop('current_medical_document', None)
+    return render_template("homePage.html")
+
+
+@app.route("/medical", methods=["GET", "POST"])
+def medical_page():
+    # Use session for history and document context
+    if request.method == "GET":
+        # Load all latest data from session (or default to empty if not found)
+        latest_response = session.pop('latest_medical_response', {}) # POP to clear it after one display
+        
+        answer = latest_response.get('answer', "")
+        thoughts = latest_response.get('thoughts', "")
+        validation = latest_response.get('validation', "")
+        source = latest_response.get('source', "")
+
+        # Clear history only when a user first navigates (not on redirect)
+        if not latest_response and 'medical_history' not in session:
+            session.pop('current_medical_document', None)
+        
+        return render_template("medical_page.html", 
+                               history=session.get('medical_history', []),
+                               answer=answer,
+                               thoughts=thoughts,
+                               validation=validation,
+                               source=source)
+    
+    # POST Request Logic
+    answer, thoughts, validation, source = "", "", "", ""
+    history = session.get('medical_history', [])
+    current_medical_document = session.get('current_medical_document', "")
+    
+    
+    try:
+        query=standardize_query(request.form.get("query", ""))
+        has_image = 'image' in request.files and request.files['image'].filename
+        has_document = 'document' in request.files and request.files['document'].filename
+        has_query = request.form.get("query") or request.form.get("question", "")
+            
+        logger.info(f"POST request received: has_image={has_image}, has_document={has_document}, has_query={has_query}")
+            
+        if has_document:
+            # Scenario 3: Query + Document
+            logger.info("Processing Scenario 3: Query + Document with Medical Swarm")
+            file = request.files['document']
+            try:
+                # Store the new document text in the session
+                document_text = file.read().decode("utf-8")
+                session['current_medical_document'] = document_text
+                current_medical_document = document_text # Use the new document for this turn
+            except UnicodeDecodeError:
+                answer = "Error: Could not decode the uploaded document. Please ensure it is a valid text or PDF file."
+                logger.error("Scenario 3: Document decode error")
+                thoughts = traceback.format_exc()
+                  
+            swarm_answer = run_medical_swarm(current_medical_document, query)
+            answer = markdown_bold_to_html(swarm_answer)
+                
+            history.append(HumanMessage(content=f"[Document Uploaded] Query: '{query}'"))
+            history.append(AIMessage(content=swarm_answer))
+            thoughts = "Swarm analysis complete. The process is orchestrated and does not use the ReAct thought process. You can now ask follow-up questions."
+            source= "Medical Swarm"
+            validation = (True, "Swarm output generated.") # Swarm has its own validation logic
+            
+        elif has_image :
+            #Scenario 1 
+            logger.info("Processing Multimodal RAG: Query + Image")
+            # --- Step 1 & 2: Image Setup & Vision Analysis ---
+            file = request.files['image']
+            upload_dir = "Uploads"
+            os.makedirs(upload_dir, exist_ok=True)
+            image_path = os.path.join(upload_dir, file.filename)
+            
+            try:
+                file.save(image_path)
+                file.close()
+            
+                with open(image_path, "rb") as img_file:
+                    img_data = base64.b64encode(img_file.read()).decode("utf-8")
+                
+            
+                vision_prompt = f"Analyze this image and identify the main subject in a single, concise sentence. The user's query is: '{query}'"
+                message = HumanMessage(content=[
+                    {"type": "text", "text": vision_prompt},
+                    {"type": "image_url", "image_url": f"data:image/jpeg;base64,{img_data}"}
+                ])
+                vision_response = llm.invoke([message])
+                visual_prediction = vision_response.content
+                logger.info(f"Vision Prediction: {visual_prediction}")
+
+                # --- Create an Enhanced Query ---
+                enhanced_query = (
+                    f'User Query: "{query}" '
+                    f'Context from an image provided by the LLM: "{visual_prediction}" '
+                    'Based on the user\'s query and the context from LLM, provide a comprehensive answer.'
+                )
+                logger.info(f"Enhanced query : {enhanced_query}")
+            
+                agent = rag_systems['medical']
+                if not agent: raise Exception("Medical RAG system is not loaded.")
+                response_dict = agent.answer(enhanced_query, chat_history=history)
+                answer, thoughts, validation, source = parse_agent_response(response_dict)
+                history.append(HumanMessage(content=query))
+                history.append(AIMessage(content=answer))
+            
+            finally:
+                if os.path.exists(image_path):
+                    try:
+                        os.remove(image_path)
+                        logger.info(f"Successfully deleted temporary image file: {image_path}")
+                    except PermissionError as e:
+                        logger.warning(f"Could not remove {image_path} after processing. "
+                                       f"File may be locked by another process. Error: {e}")
+            
+        elif query:
+            # --- SCENARIO 2: TEXT-ONLY QUERY OR SWARM FOLLOW-UP ---
+            history_for_agent = history
+            if current_medical_document:
+                logger.info("Processing Follow-up Query for Document")
+                history_for_agent = [HumanMessage(content=f"We are discussing this document:\n{current_medical_document}")] + history
+            else:
+                logger.info("Processing Text RAG query for Medical domain")
+            
+            logger.info(f"Original Query: '{query}'")
+            print(f"📚 Using chat history with {len(history)} previous messages to create standalone query")
+            standalone_query = get_standalone_question(query, history_for_agent,llm)
+            logger.info(f"Standalone Query: '{standalone_query}'")
+            
+            agent = rag_systems['medical']
+            if not agent: raise Exception("Medical RAG system is not loaded.")
+            response_dict = agent.answer(standalone_query, chat_history=history_for_agent)
+            answer, thoughts, validation, source = parse_agent_response(response_dict)
+
+            history.append(HumanMessage(content=query))
+            history.append(AIMessage(content=answer))
+
+        else:
+            raise ValueError("No query or file provided.")
+    except Exception as e:
+        logger.error(f"Error on /medical page: {e}", exc_info=True)
+        answer = f"An error occurred: {e}"
+        thoughts = traceback.format_exc()
+    
+    # Save updated history and LATEST RESPONSE DATA back to the session
+    session['medical_history'] = history
+    session['latest_medical_response'] = {
+        'answer': answer, 
+        'thoughts': thoughts, 
+        'validation': validation, 
+        'source': source
+    }
+    session.modified = True
+                             
+    logger.debug(f"Redirecting after saving latest response.")
+    return redirect(url_for('medical_page'))
+
+@app.route("/medical/clear")
+def clear_medical_chat():
+    session.pop('medical_history', None)
+    session.pop('current_medical_document', None)
+    logger.info("Medical chat history cleared.")
+    return redirect(url_for('medical_page'))
+
+@app.route("/islamic", methods=["GET", "POST"])
+def islamic_page():
+    #Use session
+    
+    if request.method == "GET":
+        # Load all latest data from session (or default to empty if not found)
+        latest_response = session.pop('latest_islamic_response', {}) # POP to clear it after one display
+        
+        answer = latest_response.get('answer', "")
+        thoughts = latest_response.get('thoughts', "")
+        validation = latest_response.get('validation', "")
+        source = latest_response.get('source', "")
+        
+        # Clear history only when a user first navigates (no latest_response and no current history)
+        if not latest_response and 'islamic_history' not in session:
+            session.pop('islamic_history', None)
+        
+        return render_template("islamic_page.html", 
+                                history=session.get('islamic_history', []),
+                                answer=answer,
+                                thoughts=thoughts,
+                                validation=validation,
+                                source=source)
+    
+    # POST Request Logic
+    answer, thoughts, validation, source = "", "", "", ""
+    history = session.get('islamic_history', [])
+    
+    # This try/except block wraps the ENTIRE POST logic
+    try:
+        query = standardize_query(request.form.get("query", ""))
+        has_image = 'image' in request.files and request.files['image'].filename
+        
+        final_query = query # Default to the original query
+        
+        if has_image:
+            logger.info("Processing Multimodal RAG query for Islamic domain")
+            
+            file = request.files['image']
+            
+            upload_dir = "Uploads"
+            os.makedirs(upload_dir, exist_ok=True)
+            image_path = os.path.join(upload_dir, file.filename)
+            
+            try:
+                file.save(image_path)
+                file.close() 
+                
+                with open(image_path, "rb") as img_file:
+                    img_base64 = base64.b64encode(img_file.read()).decode("utf-8")
+                
+                vision_prompt = f"Analyze this image's main subject. User's query is: '{query}'"
+                message = HumanMessage(content=[{"type": "text", "text": vision_prompt}, {"type": "image_url", "image_url": f"data:image/jpeg;base64,{img_base64}"}])
+                visual_prediction = llm.invoke([message]).content
+
+                enhanced_query = (
+                    f'User Query: "{query}" '
+                    f'Context from an image provided by the LLM: "{visual_prediction}" '
+                    'Based on the user\'s query and the context from LLM, provide a comprehensive answer.'
+                )
+                logger.info(f"Create enchanced query : {enhanced_query}")
+                
+                final_query = enhanced_query 
+            
+            finally:
+                if os.path.exists(image_path):
+                    try:
+                        os.remove(image_path)
+                        logger.info(f"Successfully cleaned up {image_path}")
+                    except PermissionError as e:
+                        logger.warning(f"Could not remove {image_path} after processing. "
+                                       f"File may be locked. Error: {e}")
+            
+        elif query: # Only run text logic if there's a query and no image
+            logger.info("Processing Text RAG query for Islamic domain")
+            standalone_query = get_standalone_question(query, history,llm)
+            logger.info(f"Original Query: '{query}'")
+            print(f"📚 Using chat history with {len(history)} previous messages to create standalone query")
+            logger.info(f"Standalone Query: '{standalone_query}'")
+            final_query = standalone_query
+            
+        if not final_query: 
+            raise ValueError("No query or file provided.")
+        
+        agent = rag_systems['islamic']
+        if not agent: raise Exception("Islamic RAG system is not loaded.")
+        response_dict = agent.answer(final_query, chat_history=history)
+        answer, thoughts , validation, source = parse_agent_response(response_dict)
+        history.append(HumanMessage(content=query))
+        history.append(AIMessage(content=answer))
+
+    except Exception as e:
+        logger.error(f"Error on /islamic page: {e}", exc_info=True)
+        answer = f"An error occurred: {e}"
+        thoughts = traceback.format_exc()
+            
+    # Save updated history and LATEST RESPONSE DATA back to the session
+    session['islamic_history'] = history
+    session['latest_islamic_response'] = {
+        'answer': answer, 
+        'thoughts': thoughts, 
+        'validation': validation, 
+        'source': source
+    }
+    session.modified = True
+                        
+    logger.debug(f"Redirecting after saving latest response.")
+    return redirect(url_for('islamic_page'))
+
+@app.route("/islamic/clear")
+def clear_islamic_chat():
+    session.pop('islamic_history', None)
+    logger.info("Islamic chat history cleared.")
+    return redirect(url_for('islamic_page'))
+
+@app.route("/insurance", methods=["GET", "POST"])
+def insurance_page():
+    if request.method == "GET" :
+        latest_response = session.pop('latest_insurance_response',{})
+        
+        answer = latest_response.get('answer', "")
+        thoughts = latest_response.get('thoughts', "")
+        validation = latest_response.get('validation', "")
+        source = latest_response.get('source', "")
+        
+        if not latest_response and 'insurance_history' not in session:
+            session.pop('insurance_history', None)
+        
+        return render_template("insurance_page.html", # You will need to create this HTML file
+                                history=session.get('insurance_history', []),
+                                answer=answer,
+                                thoughts=thoughts,
+                                validation=validation,
+                                source=source)
+    
+    # POST Request Logic
+    answer, thoughts, validation, source = "", "", "", ""
+    history = session.get('insurance_history', [])
+    
+    try:
+        query = standardize_query(request.form.get("query", ""))
+        
+        if query:
+            logger.info("Processing Text RAG query for Insurance domain")
+            standalone_query = get_standalone_question(query, history, llm)
+            logger.info(f"Original Query: '{query}'")
+            logger.info(f"Standalone Query: '{standalone_query}'")
+            
+            agent = rag_systems['insurance']
+            if not agent: raise Exception("Insurance RAG system is not loaded.")
+            response_dict = agent.answer(standalone_query, chat_history=history)
+            answer, thoughts, validation, source = parse_agent_response(response_dict)
+            
+            history.append(HumanMessage(content=query))
+            history.append(AIMessage(content=answer))
+        else:
+            raise ValueError("No query provided.")
+
+    except Exception as e:
+        logger.error(f"Error on /insurance page: {e}", exc_info=True)
+        answer = f"An error occurred: {e}"
+        thoughts = traceback.format_exc()
+            
+    session['insurance_history'] = history
+    session['latest_insurance_response'] = {
+        'answer': answer, 
+        'thoughts': thoughts, 
+        'validation': validation, 
+        'source': source
+    }
+    session.modified = True
+                        
+    logger.debug(f"Redirecting after saving latest response.")
+    return redirect(url_for('insurance_page'))
+
+@app.route("/insurance/clear")
+def clear_insurance_chat():
+    session.pop('insurance_history', None)
+    logger.info("Insurance chat history cleared.")
+    return redirect(url_for('insurance_page'))
+
+@app.route("/about", methods=["GET"])
+def about():
+    return render_template("about.html")
+
+@app.route('/metrics/<domain>')
+def get_metrics(domain):
+    """API endpoint to get metrics for a specific domain."""
+    try:
+        if domain == "medical" and rag_systems['medical']:
+            stats = rag_systems['medical'].metrics_tracker.get_stats()
+        elif domain == "islamic" and rag_systems['islamic']:
+            stats = rag_systems['islamic'].metrics_tracker.get_stats()
+        elif domain == "insurance" and rag_systems['insurance']:
+            stats = rag_systems['insurance'].metrics_tracker.get_stats()
+        elif not rag_systems.get(domain):
+            return jsonify({"error": f"{domain} RAG system not loaded"}), 500
+        else:
+            return jsonify({"error": "Invalid domain"}), 400
+        
+        return jsonify(stats)
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+@app.route('/metrics/reset/<domain>', methods=['POST'])
+def reset_metrics(domain):
+    """Reset metrics for a domain (useful for testing)."""
+    try:
+        if domain == "medical" and rag_systems['medical']:
+            rag_systems['medical'].metrics_tracker.reset_metrics()
+        elif domain == "islamic" and rag_systems['islamic']:
+            rag_systems['islamic'].metrics_tracker.reset_metrics()
+        elif domain == "insurance" and rag_systems['insurance']:
+            rag_systems['insurance'].metrics_tracker.reset_metrics()
+        elif not rag_systems.get(domain):
+            return jsonify({"error": f"{domain} RAG system not loaded"}), 500
+        else:
+            return jsonify({"error": "Invalid domain"}), 400
+        
+        return jsonify({"success": True, "message": f"Metrics reset for {domain}"})
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == "__main__":
+    logger.info("Starting Flask app for deployment testing...")
+    # This port 7860 is what Hugging Face Spaces expects by default
     app.run(host="0.0.0.0", port=7860, debug=False)