Update app.py

app.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 """
-Apex Biotical Veterinary WhatsApp Bot - Premium Edition
-The most effective and accurate veterinary chatbot in the market
+Apex Biotical Veterinary WhatsApp Assistant - Premium Edition
+The most effective and accurate veterinary Assistant in the market
 """
 
 import os
@@ -60,7 +60,7 @@ logger = logging.getLogger(__name__)
 load_dotenv()
 
 # Initialize FastAPI app
-app = FastAPI(title="Apex Biotical Veterinary Bot", version="2.0.0")
+app = FastAPI(title="Apex Biotical Veterinary Assistant", version="2.0.0")
 
 # Ensure static and uploads directories exist before mounting
 os.makedirs('static', exist_ok=True)
@@ -280,7 +280,7 @@ async def transcribe_voice_with_openai(file_path: str) -> str:
                 model="whisper-1",
                 file=audio_file,
                 language="en",  # Force English first
-                prompt="This is a voice message for a veterinary products bot. Language: English or Urdu only. Common greetings: hi, hello, hey, salam, assalamualaikum. Numbers: one, two, three, 1, 2, 3, aik, do, teen. Menu options: search, browse, download, catalog, products, categories, contact, availability. Products: hydropex, heposel, bromacid, tribiotic, symodex, adek gold. Please transcribe clearly and accurately."
+                prompt="This is a voice message for a veterinary products Assistant. Language: English or Urdu only. Common greetings: hi, hello, hey, salam, assalamualaikum. Numbers: one, two, three, 1, 2, 3, aik, do, teen. Menu options: search, browse, download, catalog, product, category, contact, availability, hydropex, heposel, respira aid plus, etc."
             )
             
         transcribed_text = transcript.text.strip()
@@ -295,7 +295,7 @@ async def transcribe_voice_with_openai(file_path: str) -> str:
                     model="whisper-1",
                     file=audio_file,
                     language="ur",  # Force Urdu
-                    prompt="This is a voice message in Urdu for a veterinary products bot. Common Urdu greetings: سلام, ہیلو, ہائے, السلام علیکم, وعلیکم السلام. Numbers: ایک, دو, تین, چار, پانچ, 1, 2, 3, 4, 5. Menu options: تلاش, براؤز, ڈاؤن لوڈ, کیٹلاگ, پروڈکٹ, کیٹیگری, رابطہ, دستیابی. Products: ہائیڈروپیکس, ہیپوسیل, بروماسڈ, ٹرائیبیوٹک. Please transcribe clearly in Urdu or English."
+                    prompt="This is a voice message in Urdu for a veterinary products Assistant. Common Urdu greetings: سلام, ہیلو, ہائے, السلام علیکم, وعلیکم السلام. Numbers: ایک, دو, تین, چار, پانچ, 1, 2, 3, 4, 5. Menu options: تلاش, براؤز, ڈاؤن لوڈ, کیٹلاگ, پروڈکٹ, کیٹیگری, رابطہ, دستیابی, ہائیڈروپیکس, ہیپوسیل, ریسپیرا ایڈ پلس, وغیرہ."
                 )
                 
             transcribed_text = transcript.text.strip()
@@ -309,7 +309,7 @@ async def transcribe_voice_with_openai(file_path: str) -> str:
                     transcript = openai.Audio.transcribe(
                         model="whisper-1",
                         file=audio_file,
-                        prompt="This is a voice message for a veterinary products bot. Language: English or Urdu only. Common words: hi, hello, salam, one, two, three, aik, do, teen, search, browse, download, catalog, products, categories, contact, availability, hydropex, heposel, bromacid, tribiotic, symodex, adek gold. Please transcribe any speech you can hear, even if unclear. Numbers and menu selections are important."
+                        prompt="This is a voice message for a veterinary products Assistant. Language: English or Urdu only. Common words: hi, hello, salam, one, two, three, aik, do, teen, search, browse, download, catalog, products, categories, contact, availability, hydropex, heposel, respira aid plus, etc."
                     )
                     
                 transcribed_text = transcript.text.strip()
@@ -1044,6 +1044,9 @@ async def get_catalog():
 @app.get("/", response_class=HTMLResponse)
 async def root():
     return """
+    <h2>Apex Biotical Veterinary WhatsApp Assistant</h2>
+    <p>The Assistant is running! Use the API endpoints for WhatsApp integration.</p>
+    <ul>
     <h2>Apex Biotical Veterinary WhatsApp Bot</h2>
     <p>The bot is running! Use the API endpoints for WhatsApp integration.</p>
     <ul>
@@ -1539,808 +1542,39 @@ async def process_incoming_message(from_number: str, msg: dict):
         # This ensures users can say product names like "hydropex", "respira aid plus", etc. from any menu
         logger.info(f"[Process] Checking for product name in message: '{message_body}' from state: {current_state}")
         products = get_veterinary_product_matches(message_body)
-        
-        if products:
-            logger.info(f"[Process] Product name detected: '{message_body}' -> Found {len(products)} products")
-            
-            # If single product found, show it directly
-            if len(products) == 1:
-                selected_product = products[0]
-                product_name = selected_product.get('Product Name', 'Unknown')
-                logger.info(f"[Process] Single product found: {product_name}")
-                
-                # Set current product and show details
-                context_manager.update_context(
-                    from_number,
-                    current_product=selected_product,
-                    current_state='product_inquiry',
-                    current_menu='product_inquiry',
-                    current_menu_options=list(MENU_CONFIG['product_inquiry']['option_descriptions'].values())
-                )
-                
-                # Generate and send product response
-                response = generate_veterinary_product_response(selected_product, user_context)
-                send_whatsjet_message(from_number, response)
-                return
-            
-            # If multiple products found, show list for selection
-            elif len(products) > 1:
-                logger.info(f"[Process] Multiple products found: {len(products)} products")
-                
-                # Use OpenAI to generate a professional summary and list all products
-                if OPENAI_API_KEY:
-                    try:
-                        # Create a comprehensive prompt for multiple products
-                        products_info = []
-                        for i, product in enumerate(products, 1):
-                            product_name = product.get('Product Name', 'N/A')
-                            category = product.get('Category', 'N/A')
-                            target_species = product.get('Target Species', 'N/A')
-                            products_info.append(f"{i}. {product_name} - {category} ({target_species})")
-                        
-                        products_text = "\n".join(products_info)
-                        
-                        prompt = f"""
-You are a professional veterinary product assistant for Apex Biotical. The user asked about "{message_body}" and we found {len(products)} relevant products.
-
-Available Products:
-{products_text}
-
-Please provide:
-1. A professional, welcoming response acknowledging their query
-2. A brief summary of what these products are for (if it's a category like "poultry products", explain the category)
-3. List all products with their numbers and brief descriptions
-4. Clear instructions on how to proceed
-
-Format your response professionally with emojis and clear structure. Keep it concise but informative.
-"""
-                        
-                        response = openai.ChatCompletion.create(
-                            model="gpt-4o",
-                            messages=[{"role": "user", "content": prompt}],
-                            temperature=0.7,
-                            max_tokens=400
-                        )
-                        
-                        ai_response = response.choices[0].message.content.strip()
-                        
-                        # Add instructions for selection
-                        selection_instructions = (
-                            f"\n\n💬 *To view detailed information about any product, reply with its number (1-{len(products)})*\n"
-                            "💬 *Type 'main' to return to the main menu*"
-                        )
-                        
-                        full_response = ai_response + selection_instructions
-                        
-                        # Translate response if needed
-                        if reply_language == 'ur':
-                            try:
-                                translated_response = GoogleTranslator(source='auto', target='ur').translate(full_response)
-                                send_whatsjet_message(from_number, translated_response)
-                            except Exception as e:
-                                logger.error(f"[AI] Translation error: {e}")
-                                send_whatsjet_message(from_number, full_response)
-                        else:
-                            send_whatsjet_message(from_number, full_response)
-                        
-                        # Store the product list in context for selection handling
-                        context_manager.update_context(
-                            from_number, 
-                            current_state='intelligent_products_menu',
-                            current_menu='intelligent_products_menu',
-                            current_menu_options=[f"Product {i+1}" for i in range(len(products))],
-                            available_products=products,
-                            last_query=message_body
-                        )
-                        
-                        # Add to conversation history
-                        context_manager.add_to_history(from_number, message_body, full_response)
-                        return
-                        
-                    except Exception as e:
-                        logger.error(f"[AI] Error generating product summary: {e}")
-                        # Fall back to simple listing if AI fails
-                        pass
-                
-                # Fallback: Simple listing without AI
-                message = f"🔍 *Found {len(products)} products matching '{message_body}':*\n\n"
-                
-                for i, product in enumerate(products, 1):
-                    product_name = product.get('Product Name', 'N/A')
-                    category = product.get('Category', 'N/A')
-                    target_species = product.get('Target Species', 'N/A')
-                    message += f"{format_number_with_emoji(i)} {product_name}\n"
-                    message += f"   📦 {category} ({target_species})\n\n"
-                
-                message += (
-                    f"💬 *To view detailed information about any product, reply with its number (1-{len(products)})*\n"
-                    "💬 *Type 'main' to return to the main menu*"
-                )
-                
-                # Translate response if needed
-                if reply_language == 'ur':
-                    try:
-                        translated_message = GoogleTranslator(source='auto', target='ur').translate(message)
-                        send_whatsjet_message(from_number, translated_message)
-                    except Exception as e:
-                        logger.error(f"[AI] Translation error: {e}")
-                        send_whatsjet_message(from_number, message)
-                else:
-                    send_whatsjet_message(from_number, message)
-                
-                # Store the product list in context for selection handling
-                context_manager.update_context(
-                    from_number, 
-                    current_state='intelligent_products_menu',
-                    current_menu='intelligent_products_menu',
-                    current_menu_options=[f"Product {i+1}" for i in range(len(products))],
-                    available_products=products,
-                    last_query=message_body
-                )
-                
-                # Add to conversation history
-                context_manager.add_to_history(from_number, message_body, message)
-                return
-        
-        # If no product found, continue with normal menu processing
-        logger.info(f"[Process] No product name detected, continuing with menu processing")
-        
-        # Handle state-specific menu selections with intelligent voice command processing
-        # Handle product follow-up menu selections (must be first)
-        if current_state == 'product_inquiry':
-            # Use intelligent voice command processor for better understanding
-            mapped_selection = process_intelligent_voice_command(message_body, current_state, user_context)
-            logger.info(f"[Process] Product inquiry selection mapped: '{message_body}' -> '{mapped_selection}'")
-            
-            # Check for main navigation first
-            if mapped_selection == 'main':
-                logger.info(f"[Process] Main navigation from product_inquiry: '{message_body}' -> 'main'")
-                welcome_msg = generate_veterinary_welcome_message()
-                send_whatsjet_message(from_number, welcome_msg)
-                context_manager.update_context(
-                    from_number, 
-                    current_state='main_menu', 
-                    current_menu='main_menu', 
-                    current_menu_options=list(MENU_CONFIG['main_menu']['option_descriptions'].values())
-                )
-                return
-            
-            # Validate menu selection using the mapped selection
-            if is_valid_menu_selection(mapped_selection, current_state, user_context):
-                await handle_veterinary_product_followup(mapped_selection, from_number)
-                return
-            else:
-                # If not a valid menu selection, treat as contact inquiry response
-                logger.info(f"[Process] Invalid menu selection in product_inquiry, treating as contact inquiry: '{message_body}'")
-                await handle_contact_request_response(from_number, message_body)
-                return
-        
-        # Handle contact request responses
-        if current_state == 'contact_request':
-            await handle_contact_request_response(from_number, message_body)
-            return
-        
-        # Handle availability inquiry responses
-        if current_state == 'availability_request':
-            await handle_availability_request_response(from_number, message_body)
-            return
-        
-        # Handle category product selections
-        if current_state == 'category_products_menu':
-            # Use intelligent voice command processor
-            mapped_selection = process_intelligent_voice_command(message_body, current_state, user_context)
-            logger.info(f"[Process] Category product selection mapped: '{message_body}' -> '{mapped_selection}'")
-            
-            # Check for main navigation first
-            if mapped_selection == 'main':
-                logger.info(f"[Process] Main navigation from category_products_menu: '{message_body}' -> 'main'")
-                welcome_msg = generate_veterinary_welcome_message()
-                send_whatsjet_message(from_number, welcome_msg)
-                context_manager.update_context(
-                    from_number, 
-                    current_state='main_menu', 
-                    current_menu='main_menu', 
-                    current_menu_options=list(MENU_CONFIG['main_menu']['option_descriptions'].values())
-                )
-                return
-            
-            # Validate menu selection using the mapped selection
-            if is_valid_menu_selection(mapped_selection, current_state, user_context):
-                await handle_category_product_selection(from_number, mapped_selection, user_context)
-                return
-            else:
-                # Invalid menu selection - send specific error message
-                error_msg = get_menu_validation_message(current_state, user_context)
-                send_whatsjet_message(from_number, error_msg)
-                return
-        
-        # Handle all products menu selections FIRST (before main menu)
-        if current_state == 'all_products_menu':
-            logger.info(f"[Process] Handling all_products_menu selection: '{message_body}'")
-            logger.info(f"[Process] User context state: {user_context.get('current_state')}")
-            logger.info(f"[Process] Message body type: {type(message_body)}, value: '{message_body}'")
-            
-            # Use intelligent voice command processor
-            mapped_selection = process_intelligent_voice_command(message_body, current_state, user_context)
-            logger.info(f"[Process] Mapped selection: '{message_body}' -> '{mapped_selection}'")
-            
-            # Check for main navigation first
-            if mapped_selection == 'main':
-                logger.info(f"[Process] Main navigation from all_products_menu: '{message_body}' -> 'main'")
-                welcome_msg = generate_veterinary_welcome_message()
-                send_whatsjet_message(from_number, welcome_msg)
-                context_manager.update_context(
-                    from_number, 
-                    current_state='main_menu', 
-                    current_menu='main_menu', 
-                    current_menu_options=list(MENU_CONFIG['main_menu']['option_descriptions'].values())
-                )
-                return
-            
-            # Validate menu selection using the mapped selection
-            if is_valid_menu_selection(mapped_selection, current_state, user_context):
-                logger.info(f"[Process] Valid selection: {mapped_selection}, proceeding to handle_all_products_selection")
-                await handle_all_products_selection(from_number, mapped_selection, user_context)
-                logger.info(f"[Process] Completed all_products_menu handling")
-                return
-            else:
-                # Invalid menu selection - send specific error message
-                error_msg = get_menu_validation_message(current_state, user_context)
-                send_whatsjet_message(from_number, error_msg)
-                return
-        
-        # Handle category selection menu
-        if current_state == 'category_selection_menu':
-            # Use intelligent voice command processor
-            mapped_selection = process_intelligent_voice_command(message_body, current_state, user_context)
-            logger.info(f"[Process] Category selection mapped: '{message_body}' -> '{mapped_selection}'")
-            
-            # Check for main navigation first
-            if mapped_selection == 'main':
-                logger.info(f"[Process] Main navigation from category_selection_menu: '{message_body}' -> 'main'")
-                welcome_msg = generate_veterinary_welcome_message()
-                send_whatsjet_message(from_number, welcome_msg)
-                context_manager.update_context(
-                    from_number, 
-                    current_state='main_menu', 
-                    current_menu='main_menu', 
-                    current_menu_options=list(MENU_CONFIG['main_menu']['option_descriptions'].values())
-                )
-                return
-            
-            # Validate menu selection using the mapped selection
-            if is_valid_menu_selection(mapped_selection, current_state, user_context):
-                await handle_category_selection(mapped_selection, from_number)
-                return
-            else:
-                # Invalid menu selection - send specific error message
-                error_msg = get_menu_validation_message(current_state, user_context)
-                send_whatsjet_message(from_number, error_msg)
-                return
-        
-        # Handle general menu selections (ONLY for main_menu state) with intelligent processing
-        if current_state == 'main_menu':
-            # Use intelligent voice command processor for better understanding
-            mapped_selection = process_intelligent_voice_command(message_body, current_state, user_context)
-            logger.info(f"[Process] Main menu selection mapped: '{message_body}' -> '{mapped_selection}'")
-            
-            if mapped_selection in ['1', '2', '3', '4']:
-                await handle_veterinary_menu_selection_complete(mapped_selection, from_number)
-                return
-            else:
-                # Invalid menu selection - send specific error message
-                error_msg = get_menu_validation_message(current_state, user_context)
-                send_whatsjet_message(from_number, error_msg)
-                return
-        
-        # Handle AI Chat Mode - completely separate from menu system
-        if current_state == 'ai_chat_mode':
-            logger.info(f"[AI Chat] Processing query in AI chat mode: '{message_body}' for {from_number}")
-            await handle_ai_chat_mode(from_number, message_body, reply_language)
-            return
-        
-        # If we reach here, we're not in a menu state - allow AI processing for general inquiries
-        # Expanded keyword list for product/general queries (English + Urdu)
-        product_keywords = [
-            'product', 'information', 'details', 'about', 'poultry', 'veterinary', 'medicine', 'treatment',
-            'products', 'catalog', 'category', 'categories', 'list', 'all',
-            'پروڈکٹ', 'معلومات', 'تفصیل', 'ادویات', 'علاج', 'جانور', 'دوائی', 'کیٹلاگ', 'فہرست', 'تمام', 'کیٹیگری', 'کیٹیگریز'
-        ]
-        # Lowercase and normalize message for keyword matching
-        msg_lower = message_body.lower().strip()
-        if any(keyword in msg_lower for keyword in product_keywords):
-            logger.info(f"[Process] Detected product/general inquiry (OpenAI) in message: '{message_body}'")
-            # User-facing message for voice/general queries
-            if reply_language == 'ur':
-                send_whatsjet_message(from_number, "🤖 آپ کے request کو ہمارے Veterinary AI assistant کے ساتھ process کر رہا ہوں...")
-            else:
-                send_whatsjet_message(from_number, "🤖 Processing your request with our Veterinary AI assistant...")
-            # Get OpenAI response with reply_language parameter
-            await handle_intelligent_product_inquiry(from_number, message_body, user_context, reply_language)
-            return
-        else:
-            # General free-form query (OpenAI)
-            logger.info(f"[Process] Detected general free-form inquiry (OpenAI) in message: '{message_body}'")
-            if reply_language == 'ur':
-                send_whatsjet_message(from_number, "🤖 آپ کے request کو ہمارے Veterinary AI assistant کے ساتھ process کر رہا ہوں...")
-            else:
-                send_whatsjet_message(from_number, "🤖 Processing your request with our Veterinary AI assistant...")
-            await handle_general_query_with_ai(from_number, message_body, user_context, reply_language)
-            return
-        
-    except Exception as e:
-        logger.error(f"[Process] Error processing message: {e}")
-        send_whatsjet_message(from_number, 
-            "❌ Sorry, I encountered an error. Please try again or type 'main' to return to the main menu.")
 
-async def handle_voice_message_complete(from_number: str, msg: dict):
-    """Complete voice message processing with OpenAI transcription - treats voice exactly like text"""
-    try:
-        logger.info(f"[Voice] Processing voice message from {from_number}")
-        logger.info(f"[Voice] Message structure: {msg}")
-        
-        # Check if OpenAI is available
-        if not OPENAI_API_KEY:
-            send_whatsjet_message(from_number, 
-                "🎤 Voice messages require OpenAI API. Please send a text message or type 'main' to see the menu.")
-            return
-        
-        # Extract media URL from different possible locations
-        media_url = None
-        logger.info(f"[Voice] Checking media URL locations...")
-        
-        if msg.get('media', {}).get('link'):
-            media_url = msg.get('media', {}).get('link')
-            logger.info(f"[Voice] Found media URL in media.link: {media_url}")
-        elif msg.get('media', {}).get('url'):
-            media_url = msg.get('media', {}).get('url')
-            logger.info(f"[Voice] Found media URL in media.url: {media_url}")
-        elif msg.get('url'):
-            media_url = msg.get('url')
-            logger.info(f"[Voice] Found media URL in url: {media_url}")
-        elif msg.get('audio', {}).get('url'):
-            media_url = msg.get('audio', {}).get('url')
-            logger.info(f"[Voice] Found media URL in audio.url: {media_url}")
-        else:
-            logger.error(f"[Voice] No media URL found in message structure")
-            logger.error(f"[Voice] Available fields: {list(msg.keys())}")
-            if 'media' in msg:
-                logger.error(f"[Voice] Media fields: {list(msg['media'].keys())}")
-        
-        logger.info(f"[Voice] Final extracted media URL: {media_url}")
-        
-        if not media_url:
-            send_whatsjet_message(from_number, "❌ Could not process voice message. Please try again.")
-            return
-        
-        # Generate unique filename
-        filename = f"voice_{from_number}_{int(time.time())}.ogg"
-        
-        # Download voice file
-        file_path = await download_voice_file(media_url, filename)
-        if not file_path:
-            send_whatsjet_message(from_number, "❌ Failed to download voice message. Please try again.")
-            return
-        
-        # Transcribe with OpenAI
-        transcribed_text = await transcribe_voice_with_openai(file_path)
-        
-        # Clean up voice file immediately
-        try:
-            os.remove(file_path)
-        except:
-            pass
-        
-        # Handle empty or failed transcription
-        if not transcribed_text or transcribed_text.strip() == "":
-            logger.warning(f"[Voice] Empty transcription for {from_number}")
-            send_whatsjet_message(from_number, 
-                "🎤 *Voice Message Issue*\n\n"
-                "I couldn't hear anything in your voice message. This can happen due to:\n"
-                "• Very short voice note\n"
-                "• Background noise\n"
-                "• Microphone too far away\n"
-                "• Audio quality issues\n\n"
-                "💡 *Tips for better voice notes:*\n"
-                "• Speak clearly and slowly\n"
-                "• Keep phone close to mouth\n"
-                "• Record in quiet environment\n"
-                "• Make voice note at least 2-3 seconds\n\n"
-                "💬 *You can also:*\n"
-                "• Send a text message\n"
-                "• Type 'main' to see menu options\n"
-                "• Try voice note again")
-            return
-        
-        # Process transcribed text with full intelligence
-        logger.info(f"[Voice] Transcribed: {transcribed_text}")
-        
-        # Apply transcription error corrections
-        corrected_text = process_voice_input(transcribed_text)
-        if corrected_text != transcribed_text:
-            logger.info(f"[Voice] Applied corrections: '{transcribed_text}' -> '{corrected_text}'")
-            transcribed_text = corrected_text
-        
-        # Detect language of transcribed text
-        detected_lang = 'en'  # Default to English
-        try:
-            detected_lang = detect(transcribed_text)
-            logger.info(f"[Voice] Detected language: {detected_lang}")
-            
-            # Map language codes to supported languages
-            lang_mapping = {
-                'ur': 'ur',  # Urdu
-                'ar': 'ur',  # Arabic (treat as Urdu for Islamic greetings)
-                'en': 'en',  # English
-                'hi': 'ur',  # Hindi (treat as Urdu)
-                'bn': 'ur',  # Bengali (treat as Urdu)
-                'pa': 'ur',  # Punjabi (treat as Urdu)
-                'id': 'ur',  # Indonesian (often misdetected for Urdu/Arabic)
-                'ms': 'ur',  # Malay (often misdetected for Urdu/Arabic)
-                'tr': 'ur',  # Turkish (often misdetected for Urdu/Arabic)
-            }
-            
-            # Check if text contains Urdu/Arabic characters or Islamic greetings
-            urdu_arabic_pattern = re.compile(r'[\u0600-\u06FF\u0750-\u077F\u08A0-\u08FF\uFB50-\uFDFF\uFE70-\uFEFF]')
-            islamic_greetings = ['assalamu', 'assalam', 'salam', 'salaam', 'adaab', 'namaste', 'khuda', 'allah']
-            
-            has_urdu_chars = bool(urdu_arabic_pattern.search(transcribed_text))
-            has_islamic_greeting = any(greeting in transcribed_text.lower() for greeting in islamic_greetings)
-            
-            if has_urdu_chars or has_islamic_greeting:
-                detected_lang = 'ur'
-                logger.info(f"[Voice] Overriding language detection to Urdu due to Arabic/Urdu characters or Islamic greeting")
-            
-            reply_language = lang_mapping.get(detected_lang, 'en')
-            logger.info(f"[Voice] Language '{detected_lang}' mapped to: {reply_language}")
-            
-        except Exception as e:
-            logger.warning(f"[Voice] Language detection failed: {e}")
-            reply_language = 'en'
-        
-        if reply_language not in ['en', 'ur']:
-            logger.info(f"[Voice] Language '{reply_language}' not supported, defaulting to English")
-            reply_language = 'en'
-        
-        # For Urdu voice notes, translate to English for processing
-        processing_text = transcribed_text
-        if reply_language == 'ur' and detected_lang == 'ur':
-            try:
-                logger.info(f"[Voice] Translating Urdu voice note to English for processing")
-                translated_text = GoogleTranslator(source='ur', target='en').translate(transcribed_text)
-                processing_text = translated_text
-                logger.info(f"[Voice] Translated to English: {translated_text}")
-            except Exception as e:
-                logger.error(f"[Voice] Translation failed: {e}")
-                # If translation fails, use original text
-                processing_text = transcribed_text
-        
-        # Determine reply language - always respond in English or Urdu
-        if detected_lang == 'ur':
-            reply_language = 'ur'  # Urdu voice notes get Urdu replies
-        else:
-            reply_language = 'en'  # All other languages get English replies
-        
-        logger.info(f"[Voice] Processing text: {processing_text}")
-        logger.info(f"[Voice] Reply language set to: {reply_language}")
-        
-        # Check if this is a greeting in voice note (check both original and translated)
-        if is_greeting(transcribed_text) or is_greeting(processing_text):
-            logger.info(f"[Voice] Greeting detected in voice note: {transcribed_text}")
-            
-            # Check if user is currently in AI chat mode - if so, don't trigger menu mode
-            user_context = context_manager.get_context(from_number)
-            current_state = user_context.get('current_state', 'main_menu')
-            
-            if current_state == 'ai_chat_mode':
-                logger.info(f"[Voice] User is in AI chat mode, treating greeting as AI query instead of menu trigger")
-                # Treat greeting as a general query in AI chat mode
-                await handle_general_query_with_ai(from_number, processing_text, user_context, reply_language)
-                return
-            else:
-                # Only trigger menu mode if not in AI chat mode
-                welcome_msg = generate_veterinary_welcome_message()
-                send_whatsjet_message(from_number, welcome_msg)
-                context_manager.update_context(from_number, current_state='main_menu', current_menu='main_menu', current_menu_options=list(MENU_CONFIG['main_menu']['option_descriptions'].values()))
-                return
-        
-        # Process the translated text using the same strict state-based logic as text messages
-        # This ensures voice messages follow the same menu and state rules as text messages
-        await process_incoming_message(from_number, {
-            'body': processing_text,  # Use translated text for processing
-            'type': 'text',
-            'reply_language': reply_language,
-            'original_transcription': transcribed_text  # Keep original for context
-        })
-        
-    except Exception as e:
-        logger.error(f"[Voice] Error processing voice message: {e}")
-        logger.error(f"[Voice] Full error details: {str(e)}")
-        import traceback
-        logger.error(f"[Voice] Traceback: {traceback.format_exc()}")
-        send_whatsjet_message(from_number, 
-            "❌ Error processing voice message. Please try a text message.")
-
-async def handle_veterinary_menu_selection_complete(selection: str, from_number: str):
-    """Complete menu selection handling with all possible states and menu context"""
-    try:
-        user_context = context_manager.get_context(from_number)
-        current_state = user_context.get('current_state', 'main_menu')
-        current_menu = user_context.get('current_menu', current_state)
-        current_menu_options = user_context.get('current_menu_options', [])
-        logger.info(f"[Menu] Handling selection '{selection}' in state '{current_state}' (menu: {current_menu}) for {from_number}")
-        
-        # Validate selection
-        is_valid, error_msg = validate_menu_selection(selection, current_state, user_context)
-        if not is_valid:
-            send_whatsjet_message(from_number, error_msg)
-            return
-        
-        # Main menu - check current_state, not current_menu
-        if current_state == 'main_menu':
-            logger.info(f"[Menu] Processing main_menu selection: '{selection}' for {from_number}")
-            if selection == '1':
-                await display_all_products(from_number)
-            elif selection == '2':
-                categories = get_all_categories()
-                if not categories:
-                    send_whatsjet_message(from_number, "❌ No categories available at the moment.")
-                    return
-                category_message = "📁 *Browse Categories*\n\n"
-                for i, category in enumerate(categories, 1):
-                    category_message += f"{format_number_with_emoji(i)} {category}\n"
-                category_message += "\nSelect a category number or type 'main' to return to main menu."
-                send_whatsjet_message(from_number, category_message)
-                context_manager.update_context(
-                    from_number,
-                    current_state='category_selection_menu',
-                    current_menu='category_selection_menu',
-                    current_menu_options=categories,
-                    available_categories=categories
-                )
-            elif selection == '3':
-                await send_catalog_pdf(from_number)
-                context_manager.update_context(from_number, current_state='main_menu', current_menu='main_menu', current_menu_options=list(MENU_CONFIG['main_menu']['option_descriptions'].values()))
-            elif selection == '4':
-                # Enter AI Chat Mode
-                ai_welcome_msg = (
-                    "🤖 *Veterinary AI Assistant Activated*\n\n"
-                    "I'm your intelligent veterinary assistant. I can help you with:\n"
-                    "• Product information and recommendations\n"
-                    "• Veterinary advice and guidance\n"
-                    "• Treatment suggestions\n"
-                    "• General veterinary questions\n\n"
-                    "💬 *Ask me anything related to veterinary care!*\n"
-                    "🎤 *Voice messages are supported*\n\n"
-                    "Type 'main' to return to main menu."
-                )
-                send_whatsjet_message(from_number, ai_welcome_msg)
-                context_manager.update_context(
-                    from_number,
-                    current_state='ai_chat_mode',
-                    current_menu='ai_chat_mode',
-                    current_menu_options=['main']
-                )
-            else:
-                send_whatsjet_message(from_number, "❌ Invalid selection. Please choose 1, 2, 3, or 4.")
-        
-        # Product inquiry menu
-        elif current_menu == 'product_inquiry':
-            await handle_veterinary_product_followup(selection, from_number)
-        
-        # Intelligent products menu (for multiple product results)
-        elif current_menu == 'intelligent_products_menu':
-            available_products = user_context.get('available_products', [])
-            if selection.isdigit() and 1 <= int(selection) <= len(available_products):
-                selected_product = available_products[int(selection) - 1]
-                product_name = selected_product.get('Product Name', 'Unknown')
-                context_manager.update_context(from_number, current_product=selected_product, current_state='product_inquiry', current_menu='product_inquiry', current_menu_options=list(MENU_CONFIG['product_inquiry']['option_descriptions'].values()))
-                response = generate_veterinary_product_response(selected_product, user_context)
-                send_whatsjet_message(from_number, response)
-            else:
-                send_whatsjet_message(from_number, f"❌ Invalid selection. Please choose a number between 1 and {len(available_products)}.")
-        
-        # Category selection menu
-        elif current_menu == 'category_selection_menu':
-            await handle_category_selection(selection, from_number)
-        
-        # Category products menu
-        elif current_menu == 'category_products_menu':
-            await handle_category_product_selection(from_number, selection, user_context)
-        
-        # All products menu
-        elif current_menu == 'all_products_menu':
-            await handle_all_products_selection(from_number, selection, user_context)
-        
-        else:
-            welcome_msg = generate_veterinary_welcome_message()
-            send_whatsjet_message(from_number, welcome_msg)
-            context_manager.update_context(from_number, current_state='main_menu', current_menu='main_menu', current_menu_options=list(MENU_CONFIG['main_menu']['option_descriptions'].values()))
-            
-    except Exception as e:
-        logger.error(f"[Menu] Error handling menu selection: {e}")
-        welcome_msg = generate_veterinary_welcome_message()
-        send_whatsjet_message(from_number, welcome_msg)
-        context_manager.update_context(from_number, current_state='main_menu', current_menu='main_menu', current_menu_options=list(MENU_CONFIG['main_menu']['option_descriptions'].values()))
-
-async def handle_category_product_selection(from_number: str, selection: str, user_context: dict):
-    """Handle product selection from category products menu"""
-    try:
-        available_products = user_context.get('available_products', [])
-        if selection.isdigit() and 1 <= int(selection) <= len(available_products):
-            selected_product = available_products[int(selection) - 1]
-            product_name = selected_product.get('Product Name', 'Unknown')
-            # Set current product and show details
+        # --- NEW LOGIC: Check for exact match first ---
+        normalized_input = normalize(message_body).lower().strip()
+        exact_match = None
+        for product in products:
+            product_name = product.get('Product Name', '')
+            normalized_product_name = normalize(product_name).lower().strip()
+            if normalized_product_name == normalized_input:
+                exact_match = product
+                break
+
+        if exact_match:
+            logger.info(f"[Process] Exact product match found: {exact_match.get('Product Name', 'Unknown')}")
             context_manager.update_context(
                 from_number,
-                current_product=selected_product,
+                current_product=exact_match,
                 current_state='product_inquiry',
                 current_menu='product_inquiry',
                 current_menu_options=list(MENU_CONFIG['product_inquiry']['option_descriptions'].values())
             )
-            # Generate product response
-            response = generate_veterinary_product_response(selected_product, user_context)
+            response = generate_veterinary_product_response(exact_match, user_context)
             send_whatsjet_message(from_number, response)
-        else:
-            send_whatsjet_message(from_number, "❌ Invalid selection. Please choose a valid product number.")
-    except Exception as e:
-        logger.error(f"[Category] Error handling product selection: {e}")
-        send_helpful_guidance(from_number, 'category_products_menu')
-
-async def handle_all_products_selection(from_number: str, selection: str, user_context: dict):
-    """Handle product selection from all products menu"""
-    try:
-        if products_df is None or products_df.empty:
-            send_whatsjet_message(from_number, "❌ No products available.")
             return
-        products = products_df.to_dict('records')
-        if selection.isdigit() and 1 <= int(selection) <= len(products):
-            selected_index = int(selection) - 1
-            selected_product = products[selected_index]
-            product_name = selected_product.get('Product Name', 'Unknown')
-            logger.info(f"[All Products] Selected product: {product_name} (index {selected_index})")
-            # Set current product and show details
-            context_manager.update_context(
-                from_number,
-                current_product=selected_product,
-                current_state='product_inquiry',
-                current_menu='product_inquiry',
-                current_menu_options=list(MENU_CONFIG['product_inquiry']['option_descriptions'].values())
-            )
-            response = generate_veterinary_product_response(selected_product, user_context)
-            send_whatsjet_message(from_number, response)
-        else:
-            send_whatsjet_message(from_number, "❌ Invalid selection. Please choose a valid product number.")
-    except Exception as e:
-        logger.error(f"[All Products] Error handling product selection: {e}")
-        send_helpful_guidance(from_number, 'all_products_menu')
 
-async def handle_intelligent_product_inquiry(from_number: str, query: str, user_context: dict, reply_language: str = 'en'):
-    """Handle product inquiry with OpenAI intelligence"""
-    try:
-        # First try direct product search
-        products = get_veterinary_product_matches(query)
-        
+        # --- END NEW LOGIC ---
+
         if products:
-            # Check if this is a broad/category query (multiple products found)
-            if len(products) > 1:
-                # Use OpenAI to generate a professional summary and list all products
-                if OPENAI_API_KEY:
-                    try:
-                        # Create a comprehensive prompt for multiple products
-                        products_info = []
-                        for i, product in enumerate(products, 1):
-                            product_name = product.get('Product Name', 'N/A')
-                            category = product.get('Category', 'N/A')
-                            target_species = product.get('Target Species', 'N/A')
-                            products_info.append(f"{i}. {product_name} - {category} ({target_species})")
-                        
-                        products_text = "\n".join(products_info)
-                        
-                        prompt = f"""
-You are a professional veterinary product assistant for Apex Biotical. The user asked about "{query}" and we found {len(products)} relevant products.
-
-Available Products:
-{products_text}
-
-Please provide:
-1. A professional, welcoming response acknowledging their query
-2. A brief summary of what these products are for (if it's a category like "poultry products", explain the category)
-3. List all products with their numbers and brief descriptions
-4. Clear instructions on how to proceed
-
-Format your response professionally with emojis and clear structure. Keep it concise but informative.
-"""
-                        
-                        response = openai.ChatCompletion.create(
-                            model="gpt-4o",
-                            messages=[{"role": "user", "content": prompt}],
-                            temperature=0.7,
-                            max_tokens=400
-                        )
-                        
-                        ai_response = response.choices[0].message.content.strip()
-                        
-                        # Add instructions for selection
-                        selection_instructions = (
-                            f"\n\n💬 *To view detailed information about any product, reply with its number (1-{len(products)})*\n"
-                            "💬 *Type 'main' to return to the main menu*"
-                        )
-                        
-                        full_response = ai_response + selection_instructions
-                        
-                        # Translate response if needed
-                        if reply_language == 'ur':
-                            try:
-                                translated_response = GoogleTranslator(source='auto', target='ur').translate(full_response)
-                                send_whatsjet_message(from_number, translated_response)
-                            except Exception as e:
-                                logger.error(f"[AI] Translation error: {e}")
-                                send_whatsjet_message(from_number, full_response)
-                        else:
-                            send_whatsjet_message(from_number, full_response)
-                        
-                        # Store the product list in context for selection handling
-                        context_manager.update_context(
-                            from_number, 
-                            current_state='intelligent_products_menu',
-                            current_menu='intelligent_products_menu',
-                            current_menu_options=[f"Product {i+1}" for i in range(len(products))],
-                            available_products=products,
-                            last_query=query
-                        )
-                        
-                        # Add to conversation history
-                        context_manager.add_to_history(from_number, query, full_response)
-                        return
-                        
-                    except Exception as e:
-                        logger.error(f"[AI] Error generating product summary: {e}")
-                        # Fall back to simple listing if AI fails
-                        pass
-                
-                # Fallback: Simple listing without AI
-                message = f"🔍 *Found {len(products)} products matching '{query}':*\n\n"
-                
-                for i, product in enumerate(products, 1):
-                    product_name = product.get('Product Name', 'N/A')
-                    category = product.get('Category', 'N/A')
-                    target_species = product.get('Target Species', 'N/A')
-                    message += f"{format_number_with_emoji(i)} {product_name}\n"
-                    message += f"   📦 {category} ({target_species})\n\n"
-                
-                message += (
-                    f"💬 *To view detailed information about any product, reply with its number (1-{len(products)})*\n"
-                    "💬 *Type 'main' to return to the main menu*"
-                )
-                
-                # Translate response if needed
-                if reply_language == 'ur':
-                    try:
-                        translated_message = GoogleTranslator(source='auto', target='ur').translate(message)
-                        send_whatsjet_message(from_number, translated_message)
-                    except Exception as e:
-                        logger.error(f"[AI] Translation error: {e}")
-                        send_whatsjet_message(from_number, message)
-                else:
-                    send_whatsjet_message(from_number, message)
-                
-                # Store the product list in context for selection handling
-                context_manager.update_context(
-                    from_number, 
-                    current_state='intelligent_products_menu',
-                    current_menu='intelligent_products_menu',
-                    current_menu_options=[f"Product {i+1}" for i in range(len(products))],
-                    available_products=products,
-                    last_query=query
-                )
-                
-                # Add to conversation history
-                context_manager.add_to_history(from_number, query, message)
-                
-            else:
-                # Single product found - show detailed information as before
+            logger.info(f"[Process] Product name detected: '{message_body}' -> Found {len(products)} products")
+            # If single product found, show it directly
+            if len(products) == 1:
                 selected_product = products[0]
+                product_name = selected_product.get('Product Name', 'Unknown')
+                logger.info(f"[Process] Single product found: {product_name}")
                 context_manager.update_context(
                     from_number, 
                     current_product=selected_product, 
@@ -3685,7 +2919,7 @@ def is_valid_menu_selection(selection: str, current_state: str, user_context: di
 def generate_veterinary_welcome_message(phone_number=None, user_context=None):
     """Generate veterinary welcome message"""
     return (
-        "🏥 *Welcome to Apex Biotical Veterinary Bot*\n\n"
+        "🏥 *Welcome to Apex Biotical Veterinary Assistant*\n\n"
         "I'm your intelligent veterinary assistant. How can I help you today?\n\n"
         "📋 *Main Menu:*\n"
         "1️⃣ Search Veterinary Products\n"