Spaces:
Sleeping
Sleeping
| 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() |