Update app.py

app.py

@@ -2318,26 +2318,24 @@ async def process_incoming_message(from_number: str, msg: dict):
                         send_whatsjet_message(from_number, get_menu_validation_message(current_state, user_context))
                 elif current_state == 'all_products_menu':
                     # Handle product selection from all products
-                    if products_df is not None and not products_df.empty:
-                        all_products = products_df.to_dict('records')
-                        if message_body.isdigit() and 1 <= int(message_body) <= len(all_products):
-                            selected_product = all_products[int(message_body) - 1]
-                            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())
-                            )
-                            await send_product_image_with_caption(from_number, selected_product, user_context)
-                        else:
-                            send_whatsjet_message(from_number, get_menu_validation_message(current_state, user_context))
+                    available_products = user_context.get('available_products', [])
+                    if message_body.isdigit() and 1 <= int(message_body) <= len(available_products):
+                        selected_product = available_products[int(message_body) - 1]
+                        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())
+                        )
+                        await send_product_image_with_caption(from_number, selected_product, user_context)
+                        return
                     else:
-                        send_whatsjet_message(from_number, "❌ No products available. Type 'main' to return to main menu.")
+                        send_whatsjet_message(from_number, get_menu_validation_message(current_state, user_context))
+                        return
                 elif current_state == 'product_inquiry':
                     await handle_veterinary_product_followup(message_body, from_number)
                 elif current_state == 'intelligent_products_menu':
-                    # Handle product selection from intelligent products menu
                     available_products = user_context.get('available_products', [])
                     if message_body.isdigit() and 1 <= int(message_body) <= len(available_products):
                         selected_product = available_products[int(message_body) - 1]
@@ -4445,523 +4443,53 @@ async def test_send_product_image(phone: str, product_name: str = "Bromacid"):
         return {"error": str(e)}
 
 async def handle_intelligent_product_inquiry(from_number: str, query: str, user_context: dict, reply_language: str = 'en'):
-    """Handle product inquiry with OpenAI intelligence and media support"""
+    """Handle product inquiry with OpenAI intelligence and media support, matching WhatsApp screenshot logic"""
     try:
-        # Check for intelligent product count queries
-        count_keywords = ['how many', 'count', 'number of', 'total', 'quantity', 'amount']
-        is_count_query = any(keyword in query.lower() for keyword in count_keywords)
-        
-        if is_count_query and products_df is not None and not products_df.empty:
-            # Intelligent count analysis
-            query_lower = query.lower()
-            
-            # Check if it's asking for total products
-            total_indicators = ['total products', 'all products', 'products you have', 'products in total', 'total medicines', 'all medicines']
-            is_total_query = any(indicator in query_lower for indicator in total_indicators)
-            
-            if is_total_query:
-                # Total products count
-                total_products = len(products_df)
-                categories = products_df['Category'].dropna().unique()
-                total_categories = len(categories)
-                
-                if reply_language == 'ur':
-                    response = (
-                        f"📊 *کل مصنوعات کی تعداد*\n\n"
-                        f"ہمارے پاس کل **{total_products}** ویٹرنری مصنوعات ہیں۔\n\n"
-                        f"📂 *زمرے:* {total_categories}\n"
-                        f"📦 *مصنوعات:* {total_products}\n\n"
-                        "💡 *مثال کے طور پر:*\n"
-                        "• 'سانس کی مصنوعات دکھائیں'\n"
-                        "• 'hydropex کے بارے میں بتائیں'\n"
-                        "• 'main' لکھ کر مین مینو پر جائیں"
-                    )
-                else:
-                    response = (
-                        f"📊 *Total Product Count*\n\n"
-                        f"We have a total of **{total_products}** veterinary products.\n\n"
-                        f"📂 *Categories:* {total_categories}\n"
-                        f"📦 *Products:* {total_products}\n\n"
-                        "💡 *Examples:*\n"
-                        "• 'Show me respiratory products'\n"
-                        "• 'Tell me about hydropex'\n"
-                        "• Type 'main' to return to main menu"
-                    )
-                
-                send_whatsjet_message(from_number, response)
-                return
-            else:
-                # Specific category/product type count
-                # Extract search terms from the query
-                search_terms = []
-                
-                # Common veterinary categories and terms
-                category_mappings = {
-                    'respiratory': ['respiratory', 'breathing', 'lung', 'bronchi', 'respira'],
-                    'liver': ['liver', 'hepatic', 'hepat', 'hepo'],
-                    'antibiotic': ['antibiotic', 'anti-biotic', 'biotic', 'tribiotic'],
-                    'vitamin': ['vitamin', 'vit', 'multivitamin', 'symodex', 'adek'],
-                    'electrolyte': ['electrolyte', 'hydropex', 'rehydration'],
-                    'poultry': ['poultry', 'chicken', 'bird', 'avian'],
-                    'livestock': ['livestock', 'cattle', 'cow', 'buffalo', 'animal'],
-                    'immune': ['immune', 'immunity', 'immuno', 'ec-immune'],
-                    'parasite': ['parasite', 'parasitic', 'para'],
-                    'antifungal': ['antifungal', 'fungal', 'myco'],
-                    'pain': ['pain', 'analgesic', 'painkiller'],
-                    'fever': ['fever', 'pyrexia', 'temperature']
-                }
-                
-                # Find matching categories
-                matched_categories = []
-                for category, terms in category_mappings.items():
-                    if any(term in query_lower for term in terms):
-                        matched_categories.append(category)
-                
-                # Search in CSV for matching products
-                matching_products = []
-                if matched_categories:
-                    for category in matched_categories:
-                        # Search by category name
-                        category_products = products_df[
-                            products_df['Category'].str.lower().str.contains(category, na=False) |
-                            products_df['Product Name'].str.lower().str.contains(category, na=False) |
-                            products_df['Target Species'].str.lower().str.contains(category, na=False) |
-                            products_df['Mode of Action'].str.lower().str.contains(category, na=False)
-                        ]
-                        matching_products.extend(category_products.to_dict('records'))
-                    
-                    # Remove duplicates
-                    seen_names = set()
-                    unique_products = []
-                    for product in matching_products:
-                        name = product.get('Product Name', '')
-                        if name not in seen_names:
-                            seen_names.add(name)
-                            unique_products.append(product)
-                    
-                    matching_products = unique_products
-                else:
-                    # If no specific category found, search for any terms in the query
-                    query_words = query_lower.split()
-                    for word in query_words:
-                        if len(word) > 3:  # Only search for meaningful words
-                            word_products = products_df[
-                                products_df['Category'].str.lower().str.contains(word, na=False) |
-                                products_df['Product Name'].str.lower().str.contains(word, na=False) |
-                                products_df['Target Species'].str.lower().str.contains(word, na=False) |
-                                products_df['Mode of Action'].str.lower().str.contains(word, na=False)
-                            ]
-                            matching_products.extend(word_products.to_dict('records'))
-                    
-                    # Remove duplicates
-                    seen_names = set()
-                    unique_products = []
-                    for product in matching_products:
-                        name = product.get('Product Name', '')
-                        if name not in seen_names:
-                            seen_names.add(name)
-                            unique_products.append(product)
-                    
-                    matching_products = unique_products
-                
-                # Generate response based on results
-                if matching_products:
-                    count = len(matching_products)
-                    
-                    # Get category breakdown
-                    categories_found = {}
-                    for product in matching_products:
-                        category = product.get('Category', 'Unknown')
-                        categories_found[category] = categories_found.get(category, 0) + 1
-                    
-                    if reply_language == 'ur':
-                        response = (
-                            f"📊 *{query}*\n\n"
-                            f"ہمارے پاس **{count}** مصنوعات ملی ہیں۔\n\n"
-                        )
-                        
-                        if len(categories_found) > 1:
-                            response += "📂 *زمرے کے مطابق:*\n"
-                            for category, cat_count in categories_found.items():
-                                response += f"• {category}: {cat_count}\n"
-                            response += "\n"
-                        
-                        response += (
-                            "💡 *مثال کے طور پر:*\n"
-                            "• 'سانس کی مصنوعات دکھائیں'\n"
-                            "• 'hydropex کے بارے میں بتائیں'\n"
-                            "• 'main' لکھ کر مین مینو پر جائیں"
-                        )
-                    else:
-                        response = (
-                            f"📊 *{query}*\n\n"
-                            f"We found **{count}** products.\n\n"
-                        )
-                        
-                        if len(categories_found) > 1:
-                            response += "📂 *By Category:*\n"
-                            for category, cat_count in categories_found.items():
-                                response += f"• {category}: {cat_count}\n"
-                            response += "\n"
-                        
-                        response += (
-                            "💡 *Examples:*\n"
-                            "• 'Show me respiratory products'\n"
-                            "• 'Tell me about hydropex'\n"
-                            "• Type 'main' to return to main menu"
-                        )
-                    
-                    send_whatsjet_message(from_number, response)
-                    return
-                else:
-                    # No matching products found
-                    if reply_language == 'ur':
-                        response = (
-                            f"❌ *{query}*\n\n"
-                            "ہمیں کوئی مصنوعات نہیں ملی۔\n\n"
-                            "💡 *مثال کے طور پر:*\n"
-                            "• 'سانس کی مصنوعات'\n"
-                            "• 'جگر کی مصنوعات'\n"
-                            "• 'main' لکھ کر مین مینو پر جائیں"
-                        )
-                    else:
-                        response = (
-                            f"❌ *{query}*\n\n"
-                            "No products found matching your query.\n\n"
-                            "💡 *Try:*\n"
-                            "• 'respiratory products'\n"
-                            "• 'liver products'\n"
-                            "• Type 'main' to return to main menu"
-                        )
-                    
-                    send_whatsjet_message(from_number, response)
-                    return
-        
-        # Clean and normalize the query
-        clean_query = query.strip().lower()
-        
-        # Check for common misspellings and variations
-        misspelling_corrections = {
-            'aapex': 'apex',
-            'apex': 'apex biotical',
-            'ec immune': 'ec-immune',
-            'ecimmune': 'ec-immune',
-            'hydro pex': 'hydropex',
-            'hydropex': 'hydropex',
-            'respira aid': 'respira aid plus',
-            'respiraaid': 'respira aid plus',
-            'hepo sel': 'heposel',
-            'heposel': 'heposel',
-            'brom acid': 'bromacid',
-            'bromacid': 'bromacid',
-            'hexa tox': 'hexatox',
-            'hexatox': 'hexatox',
-            'apma fort': 'apma fort',
-            'apmafort': 'apma fort',
-            'para c': 'para c.e',
-            'para ce': 'para c.e',
-            'parace': 'para c.e',
-            'tribiotic': 'tribiotic',
-            'phyto sal': 'phyto-sal',
-            'phytosal': 'phyto-sal',
-            'mycopex': 'mycopex super',
-            'mycopexsuper': 'mycopex super',
-            'eflin': 'eflin kt-20',
-            'eflinkt20': 'eflin kt-20',
-            'salcozine': 'salcozine st-30',
-            'salcozinest30': 'salcozine st-30',
-            'oftilex': 'oftilex ua-10',
-            'oftilexua10': 'oftilex ua-10',
-            'biscomin': 'biscomin 10',
-            'biscomin10': 'biscomin 10',
-            'apvita': 'apvita plus',
-            'apvitaplus': 'apvita plus',
-            'bg aspro': 'b-g aspro-c',
-            'bgaspro': 'b-g aspro-c',
-            'liverpex': 'liverpex',
-            'symodex': 'symodex',
-            'adek': 'adek gold',
-            'adekgold': 'adek gold',
-            'immuno': 'immuno dx',
-            'immunodx': 'immuno dx',
-            'mood of action': 'mode of action',
-            'mode of action': 'mode of action',
-            'mechanism of action': 'mode of action',
-            'how does it work': 'mode of action',
-            'what does it do': 'mode of action',
-            'how it works': 'mode of action'
-        }
-        
-        # Apply misspelling corrections
-        corrected_query = clean_query
-        for misspelling, correction in misspelling_corrections.items():
-            if misspelling in clean_query:
-                corrected_query = clean_query.replace(misspelling, correction)
-                logger.info(f"[Query] Applied correction: '{misspelling}' -> '{correction}'")
-                break
-        
-        # Check if query is asking about mode of action or mechanism
-        mode_of_action_keywords = ['mode of action', 'mechanism', 'how does it work', 'what does it do', 'how it works']
-        is_mode_of_action_query = any(keyword in clean_query for keyword in mode_of_action_keywords)
-        
-        # First try direct product search with original query
         products = get_veterinary_product_matches(query)
-        
-        # If no products found, try with corrected query
-        if not products and corrected_query != clean_query:
-            products = get_veterinary_product_matches(corrected_query)
-            if products:
-                logger.info(f"[Query] Found products using corrected query: '{corrected_query}'")
-        
-        # If still no products, try fuzzy matching with product names
-        if not products:
-            if products_df is not None and not products_df.empty:
-                all_product_names = [str(name).lower() for name in products_df['Product Name'].dropna()]
-                
-                # Use fuzzy matching to find similar product names
-                for product_name in all_product_names:
-                    similarity = fuzz.ratio(clean_query, product_name)
-                    if similarity >= 80:  # 80% similarity threshold
-                        logger.info(f"[Query] Fuzzy match found: '{clean_query}' -> '{product_name}' (similarity: {similarity}%)")
-                        products = get_veterinary_product_matches(product_name)
-                        if products:
-                            break
-        
         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 a CONCISE response:
-1. Brief acknowledgment (1 line max)
-2. Simple numbered list of products with category only
-3. Clear instructions on how to proceed
-
-Keep it SHORT and PRECISE. No marketing language, no detailed explanations, no repetition.
-Format: Brief intro + numbered list + instructions only.
-"""
-                        
-                        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 (ENGLISH/URDU ONLY)
-                        if reply_language == 'ur':
-                            try:
-                                # Only translate from English to Urdu - no other languages
-                                translated_response = GoogleTranslator(source='en', 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"
-                
+                # Numbered list with short descriptions
+                message = f"Certainly, here are the relevant products for your 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 (ENGLISH/URDU ONLY)
-                if reply_language == 'ur':
-                    try:
-                        # Only translate from English to Urdu - no other languages
-                        translated_message = GoogleTranslator(source='en', 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
+                    product_name = product.get('Product Name', 'Unknown')
+                    category = product.get('Category', '')
+                    short_desc = product.get('Type', '') or product.get('Indications', '')
+                    message += f"{i}. {product_name}"
+                    if category:
+                        message += f" - {category}"
+                    if short_desc:
+                        message += f" / {short_desc}"
+                    message += "\n"
+                message += (f"\nTo view detailed information about any product, reply with its number (1-{len(products)})\n"
+                            "Type 'main' to return to the main menu")
+                send_whatsjet_message(from_number, message)
+                # Store product list in context for selection
                 context_manager.update_context(
-                    from_number, 
+                    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
+                    current_menu_options=[str(i) for i in range(1, len(products)+1)],
+                    available_products=products
                 )
-                
-                # Add to conversation history
-                context_manager.add_to_history(from_number, query, message)
-                
+                return
             else:
-                # Single product found - show detailed information with media support
+                # Single product found - show details
                 selected_product = products[0]
                 context_manager.update_context(
-                    from_number, 
-                    current_product=selected_product, 
+                    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())
                 )
-                
-                # If it's a mode of action query, provide detailed mechanism information
-                if is_mode_of_action_query:
-                    product_name = clean_special_characters(selected_product.get('Product Name', 'Unknown'))
-                    composition = clean_special_characters(selected_product.get('Composition', 'Not specified'))
-                    indications = clean_special_characters(selected_product.get('Indications', 'Not specified'))
-                    
-                    mode_of_action_response = (
-                        f"🧪 *{product_name} - Mode of Action*\n\n"
-                        f"📋 *Composition:* {composition}\n\n"
-                        f"💊 *Mechanism:* {indications}\n\n"
-                        f"💬 *For complete product details, select an option below:*\n"
-                        f"1️⃣ Talk to Veterinary Consultant\n"
-                        f"2️⃣ Inquire About Availability\n"
-                        f"3️⃣ Back to Main Menu"
-                    )
-                    
-                    send_whatsjet_message(from_number, mode_of_action_response)
-                else:
-                    # Send product image with caption using the new function
-                    await send_product_image_with_caption(from_number, selected_product, user_context)
-                
-                # Add to conversation history
-                context_manager.add_to_history(from_number, query, f"Product inquiry for {selected_product.get('Product Name', 'Unknown')}")
-            
+                await send_product_image_with_caption(from_number, selected_product, user_context)
+                return
         else:
-            # No products found - check if it's an ambiguous query that needs clarification
-            if any(keyword in clean_query for keyword in ['what is', 'apex', 'immune', 'hydro', 'hepo', 'brom', 'trib', 'symo']):
-                # Check if it might be about a product but needs clarification
-                if 'apex' in clean_query:
-                    # Direct response about Apex Biotical
-                    if reply_language == 'ur':
-                        message = (
-                            "🏥 *Apex Biotical Solutions*\n\n"
-                            "ہم ایک پیشہ ور ویٹرنری فارماسیوٹیکل کمپنی ہیں جو مندرجہ ذیل میں مہارت رکھتے ہیں:\n\n"
-                            "📦 *ہماری مصنوعات:*\n"
-                            "• سانس کی مدد (Respira Aid Plus, Bromacid)\n"
-                            "• جگر کی صحت (Heposel, Liverpex)\n"
-                            "• مدافعتی نظام (EC-Immune)\n"
-                            "• اینٹی بائیوٹکس (Tribiotic, Para C.E)\n"
-                            "• وٹامنز اور سپلیمنٹس (Symodex, Adek Gold)\n\n"
-                            "💬 *مصنوعات دیکھنے کے لیے:*\n"
-                            "• 'main' لکھ کر مین مینو پر جائیں"
-                        )
-                    else:
-                        message = (
-                            "🏥 *Apex Biotical Solutions*\n\n"
-                            "We are a leading veterinary pharmaceutical company specializing in:\n\n"
-                            "📦 *Our Products:*\n"
-                            "• Respiratory support (Respira Aid Plus, Bromacid)\n"
-                            "• Liver health (Heposel, Liverpex)\n"
-                            "• Immune system (EC-Immune)\n"
-                            "• Antibiotics (Tribiotic, Para C.E)\n"
-                            "• Vitamins & supplements (Symodex, Adek Gold)\n\n"
-                            "🌍 *Our Focus:*\n"
-                            "• Professional veterinary solutions\n"
-                            "• Quality pharmaceutical products\n"
-                            "• Comprehensive animal healthcare\n\n"
-                            "💬 *To explore our products:*\n"
-                            "• Type 'main' to see the main menu\n"
-                            "• Type a product name (e.g., 'hydropex', 'respira aid plus')"
-                        )
-                    send_whatsjet_message(from_number, message)
-                    context_manager.add_to_history(from_number, query, message)
-                    return
-                elif any(product_hint in clean_query for product_hint in ['immune', 'hydro', 'hepo', 'brom', 'trib', 'symo']):
-                    # Use OpenAI for intelligent response about veterinary topics
-                    if OPENAI_API_KEY:
-                        await handle_ai_chat_mode(from_number, query, reply_language)
-                    else:
-                        # Fallback to generic response
-                        if reply_language == 'ur':
-                            message = "❌ *سوال درست نہیں ہے*\n\n💬 *براہ کرم اپنا سوال درست کریں یا 'main' لکھ کر مین مینو پر جائیں*"
-                        else:
-                            message = "❌ *Please correct your question*\n\n💬 *Type 'main' to go to main menu*"
-                        send_whatsjet_message(from_number, message)
-                else:
-                    # Use OpenAI for general veterinary questions
-                    if OPENAI_API_KEY:
-                        await handle_ai_chat_mode(from_number, query, reply_language)
-                    else:
-                        # Fallback to generic response
-                        if reply_language == 'ur':
-                            message = "❌ *سوال درست نہیں ہے*\n\n💬 *براہ کرم اپنا سوال درست کریں یا 'main' لکھ کر مین مینو پر جائیں*"
-                        else:
-                            message = "❌ *Please correct your question*\n\n💬 *Type 'main' to go to main menu*"
-                        send_whatsjet_message(from_number, message)
-            else:
-                # Generic, professional "not found" response
-                if reply_language == 'ur':
-                    message = "❌ *سوال درست نہیں ہے*\n\n💬 *براہ کرم اپنا سوال درست کریں یا 'main' لکھ کر مین مینو پر جائیں*"
-                else:
-                    message = "❌ *Please correct your question*\n\n💬 *Type 'main' to go to main menu*"
-                
-                send_whatsjet_message(from_number, message)
-            
+            # No products found
+            send_whatsjet_message(from_number, "❌ No products found matching your query.\nType 'main' to return to the main menu.")
     except Exception as e:
-        logger.error(f"Error in product inquiry: {e}")
-        # Professional error response
-        if reply_language == 'ur':
-            error_msg = "❌ *خطا آ گئی ہے*\n\n💬 *براہ کرم 'main' لکھ کر مین مینو پر جائیں*"
-        else:
-            error_msg = "❌ *An error occurred*\n\n💬 *Please type 'main' to go to main menu*"
-        
-        send_whatsjet_message(from_number, error_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()))
+        logger.error(f"Error in handle_intelligent_product_inquiry: {e}")
+        send_whatsjet_message(from_number, "❌ Error processing your request. Type 'main' to return to the main menu.")
 
 async def handle_contact_request(from_number: str):
     """Handle contact request"""