Update app.py

app.py

@@ -3,7 +3,6 @@ from huggingface_hub import InferenceClient
 import json
 from typing import Dict, List, Any
 import time
-import random
 
 # Initialize the client with retries
 MAX_RETRIES = 3
@@ -14,7 +13,7 @@ def create_client(retries=MAX_RETRIES):
     for attempt in range(retries):
         try:
             return InferenceClient(
-                "mistralai/Mistral-7B-Instruct-v0.2",  # Updated to a more capable model
+                "HuggingFaceH4/zephyr-7b-beta",
                 timeout=30
             )
         except Exception as e:
@@ -36,26 +35,6 @@ def load_site_content() -> Dict[str, Any]:
         print(f"Error loading JSON: {e}")
         return {}
 
-def get_engagement_prompt(content):
-    """Generate a random friendly greeting."""
-    prompts = [
-        "👋 Welcome to SletcherSystems! How can I assist you today?",
-        "🇿🇦 Hello from SletcherSystems in South Africa! How may I help you?",
-        "👨‍💻 Need help with AI solutions or educational technology? I'm here to assist!",
-        "🚀 Looking to transform your business with AI? Let me know what you're interested in!",
-        "🌍 Greetings! I'm your SletcherSystems virtual assistant. What can I help you with?"
-    ]
-    
-    # Add product-specific prompts if available
-    products = content.get('products', [])
-    if products and isinstance(products, list):
-        product_count = min(2, len(products))  # Get up to 2 products
-        for i in range(product_count):
-            product = products[i]
-            prompts.append(f"🔍 Interested in our {product.get('name', '')}? Feel free to ask about it!")
-    
-    return random.choice(prompts)
-
 def get_relevant_context(query: str, data: Dict[str, Any]) -> str:
     """Get relevant context based on the query keywords."""
     query = query.lower()
@@ -115,19 +94,8 @@ Company Information:
   Features: {', '.join(spec_data.get('core_features', []))}
   Technologies: {', '.join(spec_data.get('key_technologies', []))}""")
 
-    # Products information
-    if any(word in query for word in ['product', 'pricing', 'cost', 'buy', 'purchase', 'student', 'enterprise', 'personal']):
-        products = data.get('products', [])
-        context_parts.append("\nOur Products:")
-        for product in products:
-            context_parts.append(f"""
-- {product.get('name', '')}
-  Description: {product.get('description', '')}
-  Price: {product.get('price', 'Contact for pricing')}
-  Features: {', '.join(product.get('features', []))}""")
-
     # Add payment information for relevant queries
-    if any(word in query for word in ['payment', 'pay', 'cost', 'pricing', 'bank', 'bitcoin', 'eth', 'btc', 'crypto']):
+    if any(word in query for word in ['payment', 'pay', 'cost', 'pricing', 'bank', 'bitcoin', 'eth', 'btc']):
         info = data.get('company_info', {})
         context_parts.append(f"\nPayment Information: {info.get('payment', '')}")
 
@@ -155,117 +123,78 @@ def respond(
     if client is None:
         client = create_client()
         if client is None:
-            return "I apologize, but I'm having trouble connecting to the language model."
+            yield "I apologize, but I'm having trouble connecting to the language model. Please try again in a moment."
+            return
 
-    try:
-        # Load content
-        content = load_site_content()
-        if not content:
-            return "I apologize, but I'm having trouble accessing the company information."
-        
-        # Get relevant context
-        context = get_relevant_context(message, content)
-        
-        # Enhanced system message
-        enhanced_system_message = f"""You are the official AI assistant for SletcherSystems.
+    # Load content
+    content = load_site_content()
+    if not content:
+        yield "I apologize, but I'm having trouble accessing the company information. Please try again in a moment."
+        return
+    
+    # Get relevant context
+    context = get_relevant_context(message, content)
+    
+    # Enhanced system message with strict instructions
+    enhanced_system_message = f"""{system_message}
+IMPORTANT CONTEXT - USE THIS INFORMATION ONLY:
 {context}
-GUIDELINES:
-- Be friendly and professional
-- Only use information from the context above
-- Keep responses concise and helpful"""
+STRICT INSTRUCTIONS:
+1. ONLY use information from the context provided above
+2. If information isn't in the context, say "I don't have that specific information"
+3. NEVER make assumptions about location - we are a proudly South African company
+4. NEVER invent services or capabilities not listed
+5. Be accurate about our AI and educational technology focus
+6. Acknowledge our cryptocurrency acceptance when relevant
+7. Use exact statistics when they're provided in the context
+8. Always acknowledge Wayne Sletcher as CEO and Founder when relevant"""
 
+    try:
         # Format conversation history
         messages = [{"role": "system", "content": enhanced_system_message}]
         for user_msg, assistant_msg in history:
-            messages.append({"role": "user", "content": user_msg})
+            if user_msg:
+                messages.append({"role": "user", "content": user_msg})
             if assistant_msg:
                 messages.append({"role": "assistant", "content": assistant_msg})
         messages.append({"role": "user", "content": message})
 
-        # Get response without streaming
-        response = client.chat_completion(
+        # Stream the response
+        response = ""
+        for msg in client.chat_completion(
             messages,
             max_tokens=max_tokens,
+            stream=True,
             temperature=temperature,
             top_p=top_p,
-            stream=False
-        )
-        
-        return response.choices[0].message.content
-
+        ):
+            token = msg.choices[0].delta.content
+            response += token
+            yield response
     except Exception as e:
         print(f"Error in chat completion: {e}")
+        # Try to recreate client on error
         client = create_client()
-        return "I apologize, but I encountered an error. Please try again."
-
-def create_chat_interface():
-    content = load_site_content()
-    
-    with gr.Blocks(theme=gr.themes.Soft()) as demo:
-        with gr.Row():
-            with gr.Column(scale=3):
-                chatbot = gr.Chatbot(
-                    label="SletcherSystems Assistant",
-                    height=500,
-                    avatar_images=["https://api.dicebear.com/7.x/bottts/svg?seed=user", "https://api.dicebear.com/7.x/bottts/svg?seed=sletcher"]
-                )
-        
-        with gr.Row():
-            msg = gr.Textbox(
-                placeholder="Ask me about SletcherSystems...",
-                label="Your message",
-                scale=8
-            )
-            submit = gr.Button("Send", variant="primary", scale=1)
-            clear = gr.Button("Clear", variant="secondary", scale=1)
-        
-        with gr.Accordion("Advanced Settings", open=False):
-            system_message = gr.Textbox(
-                value="You are the official AI assistant for SletcherSystems, a proudly South African technology company. Provide helpful, friendly information based on the context provided.",
-                label="System message"
-            )
-            max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max tokens")
-            temperature = gr.Slider(minimum=0.1, maximum=1.0, value=0.8, step=0.1, label="Temperature")
-            top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p")
-        
-        # Add initial greeting
-        def show_greeting():
-            greeting = "👋 Welcome to SletcherSystems! How can I assist you today?"
-            try:
-                if content:
-                    greeting = get_engagement_prompt(content)
-            except Exception as e:
-                print(f"Error generating greeting: {e}")
-            return [[None, greeting]]
-        
-        def user_submit(message, chat_history):
-            return "", chat_history + [[message, None]]
-        
-        def bot_response(chat_history, system_msg, max_tok, temp, top_probability):
-            if chat_history and chat_history[-1][1] is None:
-                user_message = chat_history[-1][0]
-                response = respond(user_message, chat_history[:-1], system_msg, max_tok, temp, top_probability)
-                chat_history[-1][1] = response
-                yield chat_history
-            else:
-                yield chat_history
-        
-        # Set up event handlers
-        msg.submit(user_submit, [msg, chatbot], [msg, chatbot]).then(
-            bot_response, [chatbot, system_message, max_tokens, temperature, top_p], [chatbot]
-        )
-        
-        submit.click(user_submit, [msg, chatbot], [msg, chatbot]).then(
-            bot_response, [chatbot, system_message, max_tokens, temperature, top_p], [chatbot]
-        )
-        
-        clear.click(lambda: (None, []), None, [msg, chatbot], queue=False)
-        
-        # Show initial greeting on load
-        demo.load(show_greeting, None, chatbot)
-    
-    return demo
+        yield "I apologize, but I encountered an error. Please try your question again."
+
+# Create the Gradio interface
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Textbox(
+            value="You are the official AI assistant for SletcherSystems, a proudly South African technology company. Provide accurate, specific information based only on the provided context.",
+            label="System message"
+        ),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(
+            minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"
+        ),
+    ],
+    title="SletcherSystems AI Assistant",
+    description="Welcome! I'm here to help you learn about SletcherSystems, a proudly South African technology company.",
+    theme=gr.themes.Soft()
+)
 
 if __name__ == "__main__":
-    demo = create_chat_interface()
     demo.launch()