from dotenv import load_dotenv from openai import OpenAI import json import os import requests from pypdf import PdfReader import gradio as gr load_dotenv(override=True) def push(text): requests.post( "https://api.pushover.net/1/messages.json", data={ "token": os.getenv("PUSHOVER_TOKEN"), "user": os.getenv("PUSHOVER_USER"), "message": text, } ) def record_user_details(email, name="Name not provided", notes="not provided"): push(f"Recording {name} with email {email} and notes {notes}") return {"recorded": "ok"} def record_unknown_question(question): push(f"Recording {question}") return {"recorded": "ok"} def record_ai_question(question): push(f"Recording {question}") return {"recorded": "ok"} record_user_details_json = { "name": "record_user_details", "description": "Use this tool to record that a user is interested in being in touch and provided an email address", "parameters": { "type": "object", "properties": { "email": { "type": "string", "description": "The email address of this user" }, "name": { "type": "string", "description": "The user's name, if they provided it" } , "notes": { "type": "string", "description": "Any additional information about the conversation that's worth recording to give context" } }, "required": ["email"], "additionalProperties": False } } record_unknown_question_json = { "name": "record_unknown_question", "description": "Always use this tool to record any question that couldn't be answered as you didn't know the answer", "parameters": { "type": "object", "properties": { "question": { "type": "string", "description": "The question that couldn't be answered" }, }, "required": ["question"], "additionalProperties": False } } record_ai_question_json = { "name": "record_ai_question", "description": "Always use this tool to record any question that is related to AI such as AI agent, RAG and anything falls into Artificial Intelligence", "parameters": { "type": "object", "properties": { "question": { "type": "string", "description": "The question about AI" }, }, "required": ["question"], "additionalProperties": False } } tools = [{"type": "function", "function": record_user_details_json}, {"type": "function", "function": record_unknown_question_json}, {"type": "function", "function": record_ai_question_json}] class Me: def __init__(self): self.openai = OpenAI( api_key=os.getenv("DEEPSEEK_API_KEY"), base_url=os.getenv("DEEPSEEK_BASE_URL") ) self.name = "Meng Bai/白萌" reader = PdfReader("me/linkedin.pdf") self.linkedin = "" for page in reader.pages: text = page.extract_text() if text: self.linkedin += text with open("me/summary.txt", "r", encoding="utf-8") as f: self.summary = f.read() def handle_tool_call(self, tool_calls): results = [] for tool_call in tool_calls: tool_name = tool_call.function.name arguments = json.loads(tool_call.function.arguments) print(f"Tool called: {tool_name}", flush=True) tool = globals().get(tool_name) result = tool(**arguments) if tool else {} results.append({"role": "tool","content": json.dumps(result),"tool_call_id": tool_call.id}) return results def system_prompt(self): system_prompt = f"You are acting as {self.name}. You are answering questions on {self.name}'s website, \ particularly questions related to {self.name}'s career, background, skills and experience. \ Your responsibility is to represent {self.name} for interactions on the website as faithfully as possible. \ You are given a summary of {self.name}'s background and LinkedIn profile which you can use to answer questions. \ Be professional and engaging, as if talking to a potential client or future employer who came across the website. \ If you don't know the answer to any question, use your record_unknown_question tool to record the question that you couldn't answer, even if it's about something trivial or unrelated to career. \ If the user is engaging in discussion, try to steer them towards getting in touch via email; ask for their email and record it using your record_user_details tool. " system_prompt += f"\n\n## Summary:\n{self.summary}\n\n## LinkedIn Profile:\n{self.linkedin}\n\n" system_prompt += f"With this context, please chat with the user, always staying in character as {self.name}." return system_prompt def chat(self, message, history): messages = [{"role": "system", "content": self.system_prompt()}] + history + [{"role": "user", "content": message}] done = False while not done: response = self.openai.chat.completions.create(model="deepseek-chat", messages=messages, tools=tools) if response.choices[0].finish_reason=="tool_calls": message = response.choices[0].message tool_calls = message.tool_calls results = self.handle_tool_call(tool_calls) messages.append(message) messages.extend(results) else: done = True return response.choices[0].message.content if __name__ == "__main__": me = Me() # 创建自我介绍消息 welcome_message = f"""Hello! I’m Meng Bai — it’s a pleasure to have you here on my HF space! 👋 I’m a data scientist with extensive experience in AI, machine learning, and risk management. I’m excited to share more about my professional background, skills, and project work with you. You’re welcome to ask me about: • My career experience and skill set • AI and machine learning projects I’ve worked on • Technical expertise and tools I use • My educational background • Or anything else you’d like to know If you’re interested in collaboration or career opportunities, I’d be happy to connect and discuss further. Feel free to reach out!""" # 使用初始消息创建聊天界面 interface = gr.ChatInterface( me.chat, type="messages", chatbot=gr.Chatbot(value=[{"role": "assistant", "content": welcome_message}], type="messages") ) interface.launch()