| from openai import OpenAI |
| import json |
| import os |
| import requests |
| from PyPDF2 import PdfReader |
| import gradio as gr |
| from pydantic import BaseModel |
|
|
|
|
| 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"} |
|
|
| 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 |
| } |
| } |
|
|
| tools = [{"type": "function", "function": record_user_details_json}, |
| {"type": "function", "function": record_unknown_question_json}] |
|
|
| |
| class Evaluation(BaseModel): |
| is_acceptable: bool |
| feedback: str |
| |
| class Me: |
|
|
| def __init__(self): |
| |
| |
| |
| |
| |
| |
| |
| self.openrouter = OpenAI( |
| base_url="https://openrouter.ai/api/v1", |
| api_key= os.getenv('OPEN_ROUTER_API_KEY') ) |
| |
| self.gemini = OpenAI( |
| api_key=os.getenv("GOOGLE_API_KEY"), |
| base_url="https://generativelanguage.googleapis.com/v1beta/openai/" |
| ) |
| self.name = "Chaoran Zhou" |
| 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 evaluator_system_prompt(self): |
| evaluator_system_prompt = f"You are an evaluator that decides whether a response to a question is acceptable. \ |
| You are provided with a conversation between a User and an Agent. Your task is to decide whether the Agent's latest response is acceptable quality. \ |
| The Agent is playing the role of {self.name} and is representing {self.name} on their website. \ |
| The Agent has been instructed to be professional and engaging, as if talking to a potential client or future employer who came across the website. \ |
| The Agent has been provided with context on {self.name} in the form of their summary and LinkedIn details. Here's the information:" |
|
|
| evaluator_system_prompt += f"\n\n## Summary:\n{self.summary}\n\n## LinkedIn Profile:\n{self.linkedin}\n\n" |
| evaluator_system_prompt += f"With this context, please evaluate the latest response, replying with whether the response is acceptable and your feedback." |
| return evaluator_system_prompt |
|
|
| def evaluator_user_prompt(self, reply, message, history): |
| user_prompt = f"Here's the conversation between the User and the Agent: \n\n{history}\n\n" |
| user_prompt += f"Here's the latest message from the User: \n\n{message}\n\n" |
| user_prompt += f"Here's the latest response from the Agent: \n\n{reply}\n\n" |
| user_prompt += f"Please evaluate the response, replying with whether it is acceptable and your feedback." |
| return user_prompt |
|
|
| def evaluate(self, reply, message, history) -> Evaluation: |
| messages = [ |
| {"role": "system", "content": self.evaluator_system_prompt()}, |
| {"role": "user", "content": self.evaluator_user_prompt(reply, message, history)} |
| ] |
| response = self.gemini.beta.chat.completions.parse( |
| model="gemini-2.5-flash-preview-05-20", |
| messages=messages, |
| response_format=Evaluation |
| ) |
| return response.choices[0].message.parsed |
|
|
| def rerun(self, reply, message, history, feedback): |
| updated_system_prompt = self.system_prompt() + f"\n\n## Previous answer rejected\nYou just tried to reply, but the quality control rejected your reply\n" |
| updated_system_prompt += f"## Your attempted answer:\n{reply}\n\n" |
| updated_system_prompt += f"## Reason for rejection:\n{feedback}\n\n" |
| messages = [{"role": "system", "content": updated_system_prompt}] + history + [{"role": "user", "content": message}] |
| |
| done = False |
| while not done: |
| response = self.gemini.chat.completions.create( |
| model="gemini-2.5-flash-preview-05-20", |
| messages=messages, |
| tools=tools |
| ) |
| |
| if response.choices[0].finish_reason == "tool_calls": |
| message_obj = response.choices[0].message |
| tool_calls = message_obj.tool_calls |
| results = self.handle_tool_call(tool_calls) |
| messages.append(message_obj) |
| messages.extend(results) |
| else: |
| done = True |
| return response.choices[0].message.content |
| |
| def chat(self, message, history): |
| messages = [{"role": "system", "content": self.system_prompt()}] + history + [{"role": "user", "content": message}] |
| done = False |
| |
| |
| while not done: |
| response = self.openrouter.chat.completions.create(model="meta-llama/llama-3.3-8b-instruct:free", 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 |
| |
| reply = response.choices[0].message.content |
|
|
| |
| try: |
| evaluation = self.evaluate(reply, message, history) |
| |
| if evaluation.is_acceptable: |
| print("Passed evaluation - returning reply") |
| else: |
| print("Failed evaluation - retrying") |
| print(f"Feedback: {evaluation.feedback}") |
| reply = self.rerun(reply, message, history, evaluation.feedback) |
| except Exception as e: |
| print(f"Evaluation failed with error: {e}") |
| print("Proceeding with original reply") |
| |
| return reply |
| |
| |
|
|
| if __name__ == "__main__": |
| me = Me() |
| gr.ChatInterface(me.chat, type="messages").launch(debug=True, share=False) |
| |
