Create chat_agent.py

chat_agent.py

@@ -0,0 +1,105 @@
+import gradio as gr
+from huggingface_hub import InferenceClient
+import json
+import time
+import random
+
+class ChatAgent:
+    def __init__(self):
+        self.client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+        self.load_content()
+        self.engagement_prompts = [
+            "👋 Hi there! Looking for an AI solution? We have options from $100 for students to enterprise-grade systems.",
+            "🎓 Are you a student? Check out our Student Study Assistant - lifetime access for just $100!",
+            "🚀 Ready to transform your workflow with AI? Our RAG Assistant Pro might be perfect for you.",
+            "🏢 Need an enterprise AI solution? Let's discuss your custom requirements.",
+        ]
+        
+    def load_content(self):
+        with open("data/site_content.json", "r") as f:
+            self.content = json.load(f)
+            
+    def get_product_info(self, product_name=None):
+        products = self.content.get('products', [])
+        if product_name:
+            for product in products:
+                if product['name'].lower() == product_name.lower():
+                    return product
+        return products[0]  # Return first product if none specified
+        
+    def generate_initial_greeting(self):
+        return random.choice(self.engagement_prompts)
+        
+    def get_response(self, message, history):
+        # Get relevant product based on message content
+        context = ""
+        if "student" in message.lower():
+            product = self.get_product_info("Student Study Assistant")
+            context = f"Focusing on Student Study Assistant: {product['description']} Price: {product['price']}"
+        elif "rag" in message.lower() or "professional" in message.lower():
+            product = self.get_product_info("Personalized RAG Assistant Pro")
+            context = f"Focusing on RAG Assistant Pro: {product['description']} Price: {product['price']}"
+        elif "enterprise" in message.lower():
+            product = self.get_product_info("Enterprise AI Suite")
+            context = f"Focusing on Enterprise AI Suite: {product['description']}"
+        elif "custom" in message.lower() or "llm" in message.lower():
+            product = self.get_product_info("Custom LLM Platform")
+            context = f"Focusing on Custom LLM Platform: {product['description']}"
+        
+        system_message = f"""You are a helpful sales assistant for Sletcher Systems. 
+Current product information: {context}
+Style: Be friendly, professional, and helpful. Focus on understanding the customer's needs.
+Goals: Help customers find the right AI solution and encourage them to schedule a consultation.
+"""
+
+        messages = [{"role": "system", "content": system_message}]
+        for msg in history:
+            messages.extend([
+                {"role": "user", "content": msg[0]},
+                {"role": "assistant", "content": msg[1]}
+            ])
+        messages.append({"role": "user", "content": message})
+
+        response = ""
+        for msg in self.client.chat_completion(
+            messages,
+            max_tokens=512,
+            stream=True,
+            temperature=0.7,
+        ):
+            token = msg.choices[0].delta.content
+            response += token
+            yield response
+
+def create_chat_interface():
+    agent = ChatAgent()
+    
+    with gr.Blocks(theme=gr.themes.Soft()) as demo:
+        chatbot = gr.Chatbot(
+            label="SletcherSystems Sales Assistant",
+            height=400
+        )
+        msg = gr.Textbox(label="Type your message here...")
+        clear = gr.Button("Clear")
+        
+        # Add initial greeting
+        def show_greeting():
+            return [[None, agent.generate_initial_greeting()]]
+        
+        def respond(message, chat_history):
+            bot_message = ""
+            for chunk in agent.get_response(message, chat_history):
+                bot_message = chunk
+                yield chat_history + [[message, bot_message]]
+                
+        msg.submit(respond, [msg, chatbot], [chatbot])
+        clear.click(lambda: None, None, chatbot, queue=False)
+        
+        # Show initial greeting
+        demo.load(show_greeting, None, chatbot)
+    
+    return demo
+
+if __name__ == "__main__":
+    demo = create_chat_interface()
+    demo.launch()