Update app.py

app.py

@@ -1,102 +1,10 @@
 import gradio as gr
 import time
-import requests
-import json
-
-# ==================== CONFIGURATION MODULE ====================
-# Customize this section for your specific business
-
-BUSINESS_CONFIG = {
-    "name": "TechGadget Store",
-    "industry": "technology retail",
-    "hours": "9AM-6PM Monday to Saturday",
-    "products": "smartphones, laptops, tablets, and accessories",
-    "address": "123 Main Street, Tech City",
-    "phone": "(555) 123-4567",
-    "email": "[email protected]",
-    "return_policy": "30-day money-back guarantee on all products",
-    "shipping": "Free shipping on orders over $50",
-    "values": "Quality products, excellent customer service, and competitive prices",
-    "key_people": "John Smith (CEO), Sarah Johnson (Head of Sales)"
-}
-
-PRODUCTS = {
-    "smartphone": {
-        "basic": {"name": "BasicPhone", "price": "$199", "features": "6.1\" display, 128GB storage"},
-        "pro": {"name": "ProPhone X", "price": "$899", "features": "6.7\" OLED, 512GB, 5G"}
-    },
-    "laptop": {
-        "standard": {"name": "WorkBook Lite", "price": "$699", "features": "15\" display, 8GB RAM, 256GB SSD"},
-        "premium": {"name": "WorkBook Pro", "price": "$1499", "features": "16\" display, 16GB RAM, 1TB SSD"}
-    }
-}
-
-# ==================== AI MODULE ====================
-# This module handles complex questions using business context
-
-class BusinessAIAssistant:
-    def __init__(self, business_config):
-        self.business_config = business_config
-        self.context = self._create_context_prompt()
-        
-    def _create_context_prompt(self):
-        """Create a detailed context prompt for the AI"""
-        prompt = f"""You are a customer service representative for {self.business_config['name']}, 
-        a {self.business_config['industry']} business. Your role is to assist customers with questions
-        about products, policies, and services while maintaining a friendly and professional tone.
-        
-        BUSINESS INFORMATION:
-        - Name: {self.business_config['name']}
-        - Industry: {self.business_config['industry']}
-        - Hours: {self.business_config['hours']}
-        - Address: {self.business_config['address']}
-        - Phone: {self.business_config['phone']}
-        - Email: {self.business_config['email']}
-        - Products: {self.business_config['products']}
-        - Return Policy: {self.business_config['return_policy']}
-        - Shipping: {self.business_config['shipping']}
-        - Values: {self.business_config['values']}
-        - Key People: {self.business_config['key_people']}
-        
-        GUIDELINES:
-        1. Always be helpful, friendly, and professional
-        2. Stay on topic related to the business and its products/services
-        3. If asked about unrelated topics, politely steer back to business matters
-        4. For technical questions, provide accurate information based on product specs
-        5. If unsure about something, offer to connect the customer with a human representative
-        
-        CURRENT CONVERSATION:
-        """
-        return prompt
-        
-    def generate_response(self, user_message, conversation_history=""):
-        """
-        Generate a response to user message using business context
-        In a real implementation, this would connect to an AI API
-        """
-        # Simulated AI response - in practice, you would call an AI API here
-        # For example: Hugging Face, OpenAI, or another LLM provider
-        
-        # This is a simplified simulation - real implementation would use API calls
-        if "technical" in user_message.lower() or "spec" in user_message.lower():
-            return "I'd be happy to help with technical questions! Based on our product specifications, I can provide detailed information about features and compatibility."
-        
-        elif "recommend" in user_message.lower() or "suggest" in user_message.lower():
-            return "Based on your needs, I'd recommend considering our premium lineup for the best performance, or our standard options for great value."
-        
-        elif "compare" in user_message.lower():
-            return "I can help you compare our products! Our premium options generally offer better performance, more storage, and higher quality displays than our standard models."
-        
-        elif "problem" in user_message.lower() or "issue" in user_message.lower():
-            return "I'm sorry to hear you're experiencing an issue. Let me help you troubleshoot, or I can connect you with our technical support team if needed."
-        
-        else:
-            return "Thank you for your question. As a representative of {business_name}, I'll do my best to provide accurate information about our products and services. How else can I assist you today?".format(business_name=self.business_config['name'])
+from business_ai import BusinessAIAssistant
+from business_config import BUSINESS_CONFIG, PRODUCTS, SERVICES
 
 # Initialize the AI assistant
-ai_assistant = BusinessAIAssistant(BUSINESS_CONFIG)
-
-# ==================== CHATBOT LOGIC ====================
+ai_assistant = BusinessAIAssistant(BUSINESS_CONFIG, PRODUCTS, SERVICES)
 
 # Predefined responses for common queries
 def get_business_response(intent):
@@ -105,32 +13,36 @@ def get_business_response(intent):
         "location": f"We're located at {BUSINESS_CONFIG['address']}. Come visit us!",
         "contact": f"You can reach us at {BUSINESS_CONFIG['phone']} or email {BUSINESS_CONFIG['email']}.",
         "products": f"We offer a wide range of {BUSINESS_CONFIG['products']}. What specifically are you interested in?",
+        "services": f"We provide these services: {BUSINESS_CONFIG['services']}.",
         "returns": f"Our return policy: {BUSINESS_CONFIG['return_policy']}.",
         "shipping": f"Yes! We offer {BUSINESS_CONFIG['shipping'].lower()}",
-        "greeting": "Hello! Welcome to {name}. How can I help you today?".format(name=BUSINESS_CONFIG['name']),
+        "greeting": f"Hello! Welcome to {BUSINESS_CONFIG['name']}. How can I help you today?",
         "thanks": "You're welcome! Is there anything else I can help you with?",
-        "goodbye": "Thank you for contacting {name}. Have a great day!".format(name=BUSINESS_CONFIG['name']),
+        "goodbye": f"Thank you for contacting {BUSINESS_CONFIG['name']}. Have a great day!",
         "help": "I can help you with: \n• Store hours and location \n• Product information and pricing \n• Return and shipping policies \n• Technical comparisons between products \n• And much more! What do you need help with today?",
-        "capabilities": "I'm your virtual assistant for {name}! I can: \n• Answer questions about our products \n• Provide store information and hours \n• Explain our shipping and return policies \n• Help you compare different tech products \n• Assist with basic technical advice \n\nWhat would you like to know?".format(name=BUSINESS_CONFIG['name']),
+        "capabilities": f"I'm your virtual assistant for {BUSINESS_CONFIG['name']}! I can answer questions about our products, services, policies, and more. What would you like to know?",
         "values": f"Our company values: {BUSINESS_CONFIG['values']}",
-        "people": f"Key people at our company: {BUSINESS_CONFIG['key_people']}"
+        "people": f"Key people at our company: {BUSINESS_CONFIG['key_people']}",
+        "payment": f"We accept these payment methods: {BUSINESS_CONFIG['payment_methods']}."
     }
-    return responses.get(intent, "I'm not sure how to help with that. Can you please rephrase?")
+    return responses.get(intent, "")
 
 # Function to handle product inquiries
 def handle_product_inquiry(product_type, model=None):
     if product_type not in PRODUCTS:
-        return f"We don't carry that product. We mainly offer {', '.join(PRODUCTS.keys())}."
+        available_products = ", ".join(PRODUCTS.keys())
+        return f"We don't carry that product. We mainly offer {available_products}."
     
     if model:
         if model in PRODUCTS[product_type]:
             product = PRODUCTS[product_type][model]
             return f"Our {model} {product_type} is the {product['name']}, priced at {product['price']}. Features: {product['features']}"
         else:
-            return f"We don't have a {model} model for {product_type}. We have {', '.join(PRODUCTS[product_type].keys())}."
+            available_models = ", ".join(PRODUCTS[product_type].keys())
+            return f"We don't have a {model} model for {product_type}. We have {available_models}."
     else:
         models = PRODUCTS[product_type]
-        response = f"For {product_type}, we have: "
+        response = f"For {product_type}, we have these models:"
         for model_name, details in models.items():
             response += f"\n- {model_name}: {details['name']} ({details['price']})"
         return response
@@ -157,29 +69,37 @@ def chat_with_bot(message, chat_history):
         bot_response = get_business_response("returns")
     
     # Check for products
-    elif any(word in user_message for word in ["product", "sell", "offer", "item"]):
+    elif any(word in user_message for word in ["product", "sell", "offer", "item", "inventory", "have", "stock"]):
         bot_response = get_business_response("products")
     
+    # Check for services
+    elif any(word in user_message for word in ["service", "support", "repair", "maintenance", "warranty"]):
+        bot_response = get_business_response("services")
+    
     # Check for contact info
-    elif any(word in user_message for word in ["contact", "phone", "call", "email"]):
+    elif any(word in user_message for word in ["contact", "phone", "call", "email", "reach"]):
         bot_response = get_business_response("contact")
     
     # Check for location
-    elif any(word in user_message for word in ["where", "location", "address", "find"]):
+    elif any(word in user_message for word in ["where", "location", "address", "find", "directions"]):
         bot_response = get_business_response("location")
     
     # Check for business hours
-    elif any(word in user_message for word in ["hour", "time", "open", "close"]):
+    elif any(word in user_message for word in ["hour", "time", "open", "close", "when"]):
         bot_response = get_business_response("hours")
     
     # Check for company values
-    elif any(word in user_message for word in ["value", "mission", "believe"]):
+    elif any(word in user_message for word in ["value", "mission", "believe", "principle"]):
         bot_response = get_business_response("values")
     
     # Check for people
-    elif any(word in user_message for word in ["who", "people", "team", "owner", "ceo"]):
+    elif any(word in user_message for word in ["who", "people", "team", "owner", "ceo", "manager"]):
         bot_response = get_business_response("people")
     
+    # Check for payment methods
+    elif any(word in user_message for word in ["pay", "payment", "credit", "debit", "cash", "finance"]):
+        bot_response = get_business_response("payment")
+    
     # Check for help or capabilities
     elif any(word in user_message for word in ["help", "what can you do", "how can you help", "capabilities", "what do you know"]):
         bot_response = get_business_response("capabilities")
@@ -189,33 +109,45 @@ def chat_with_bot(message, chat_history):
         bot_response = get_business_response("thanks")
     
     # Check for goodbye
-    elif any(word in user_message for word in ["bye", "goodbye", "see you"]):
+    elif any(word in user_message for word in ["bye", "goodbye", "see you", "farewell"]):
         bot_response = get_business_response("goodbye")
     
     # Check for greetings (moved to end to avoid conflicts)
-    elif any(word in user_message for word in ["hi", "hello", "hey", "greetings"]):
+    elif any(word in user_message for word in ["hi", "hello", "hey", "greetings", "howdy"]):
         bot_response = get_business_response("greeting")
     
     # Handle product inquiries
-    elif any(word in user_message for word in ["smartphone", "phone"]):
-        if "pro" in user_message or "premium" in user_message:
+    elif any(word in user_message for word in ["smartphone", "phone", "iphone", "android"]):
+        if "pro" in user_message or "premium" in user_message or "high-end" in user_message:
             bot_response = handle_product_inquiry("smartphone", "pro")
-        elif "basic" in user_message or "cheap" in user_message:
+        elif "basic" in user_message or "cheap" in user_message or "budget" in user_message:
             bot_response = handle_product_inquiry("smartphone", "basic")
         else:
             bot_response = handle_product_inquiry("smartphone")
     
-    elif any(word in user_message for word in ["laptop", "computer"]):
-        if "pro" in user_message or "premium" in user_message:
+    elif any(word in user_message for word in ["laptop", "computer", "notebook", "macbook", "pc"]):
+        if "pro" in user_message or "premium" in user_message or "high-end" in user_message:
             bot_response = handle_product_inquiry("laptop", "premium")
-        elif "standard" in user_message or "basic" in user_message:
+        elif "standard" in user_message or "basic" in user_message or "budget" in user_message:
             bot_response = handle_product_inquiry("laptop", "standard")
         else:
             bot_response = handle_product_inquiry("laptop")
     
+    elif any(word in user_message for word in ["tablet", "ipad", "android tablet"]):
+        bot_response = "We offer a variety of tablets from leading brands. Would you like information about Apple iPads or Android tablets?"
+    
+    elif any(word in user_message for word in ["accessory", "accessories", "case", "charger", "headphone"]):
+        bot_response = "We carry a wide range of accessories including cases, chargers, headphones, and more. Is there a specific accessory you're interested in?"
+    
     # For all other questions, use the AI module
     else:
         bot_response = ai_assistant.generate_response(user_message, conversation_history)
+        # If the AI module returns a generic response, try to provide something more specific
+        if "thank you for your question" in bot_response.lower():
+            if "?" in user_message or "what" in user_message or "how" in user_message:
+                bot_response = f"I'd be happy to help! At {BUSINESS_CONFIG['name']}, we offer {BUSINESS_CONFIG['products']}. Is there a specific product or service you're interested in?"
+            else:
+                bot_response = "How can I assist you today? I can help with product information, store details, or technical support."
 
     # Add a small delay to make it feel more natural
     time.sleep(0.3)
@@ -226,8 +158,7 @@ def chat_with_bot(message, chat_history):
     
     return "", chat_history
 
-# ==================== GRADIO INTERFACE ====================
-
+# Custom CSS for styling
 custom_css = """
 #chatbot {
     font-family: 'Arial', sans-serif;
@@ -264,7 +195,7 @@ h1 {
 }
 """
 
-with gr.Blocks(css=custom_css, title="TechGadget Store Support") as demo:
+with gr.Blocks(css=custom_css, title=f"{BUSINESS_CONFIG['name']} Support") as demo:
     with gr.Column(elem_id="container"):
         gr.Markdown(f"# 🤖 {BUSINESS_CONFIG['name']} Support Chat")
         gr.Markdown("### How can I help you with our products and services today?")
@@ -280,7 +211,7 @@ with gr.Blocks(css=custom_css, title="TechGadget Store Support") as demo:
                 with gr.Row():
                     msg = gr.Textbox(
                         label="Type your message here...",
-                        placeholder="Ask me about products, shipping, or technical details...",
+                        placeholder="Ask me about products, services, or anything else...",
                         lines=1,
                         scale=4
                     )
@@ -292,13 +223,13 @@ with gr.Blocks(css=custom_css, title="TechGadget Store Support") as demo:
                 
                 examples = gr.Examples(
                     examples=[
+                        ["What products do you have?"],
                         ["Do you offer free shipping?"],
                         ["What are your store hours?"],
                         ["What's your return policy?"],
                         ["Can you compare your laptop models?"],
-                        ["What technical specifications do your phones have?"],
-                        ["Who are the key people at your company?"],
-                        ["What values does your company uphold?"]
+                        ["What services do you provide?"],
+                        ["What payment methods do you accept?"]
                     ],
                     inputs=msg,
                     label="Click any question to try it!"
@@ -318,26 +249,5 @@ with gr.Blocks(css=custom_css, title="TechGadget Store Support") as demo:
         send_btn.click(chat_with_bot, [msg, chatbot], [msg, chatbot])
         clear.click(lambda: None, None, chatbot, queue=False)
 
-# ==================== INSTRUCTIONS ====================
-
-gr.Markdown("""
-### Implementation Notes:
-
-1. **AI Integration**: The `BusinessAIAssistant` class is designed to connect to any AI API. Currently, it uses simulated responses, but you can easily extend it to use:
-   - Hugging Face Inference API
-   - OpenAI GPT models
-   - Anthropic Claude
-   - Or any other LLM provider
-
-2. **Customization**: Modify the `BUSINESS_CONFIG` dictionary at the top to match any business. The AI module will automatically adapt to the new context.
-
-3. **Extending Functionality**: To add more capabilities:
-   - Add new patterns to the response logic in `chat_with_bot()`
-   - Extend the `BusinessAIAssistant` class with specialized methods
-   - Add more products to the `PRODUCTS` dictionary
-
-4. **API Integration**: To connect to a real AI API, replace the `generate_response` method with actual API calls to your preferred provider.
-""")
-
 if __name__ == "__main__":
     demo.launch(share=True)