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from openai import OpenAI |
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import json |
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import os |
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import requests |
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from PyPDF2 import PdfReader |
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import gradio as gr |
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from pydantic import BaseModel |
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def push(text): |
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requests.post( |
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"https://api.pushover.net/1/messages.json", |
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data={ |
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"token": os.getenv("PUSHOVER_TOKEN"), |
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"user": os.getenv("PUSHOVER_USER"), |
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"message": text, |
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} |
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) |
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def record_user_details(email, name="Name not provided", notes="not provided"): |
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push(f"Recording {name} with email {email} and notes {notes}") |
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return {"recorded": "ok"} |
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def record_unknown_question(question): |
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push(f"Recording {question}") |
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return {"recorded": "ok"} |
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record_user_details_json = { |
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"name": "record_user_details", |
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"description": "Use this tool to record that a user is interested in being in touch and provided an email address", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"email": { |
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"type": "string", |
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"description": "The email address of this user" |
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}, |
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"name": { |
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"type": "string", |
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"description": "The user's name, if they provided it" |
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} |
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, |
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"notes": { |
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"type": "string", |
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"description": "Any additional information about the conversation that's worth recording to give context" |
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} |
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}, |
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"required": ["email"], |
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"additionalProperties": False |
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} |
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} |
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record_unknown_question_json = { |
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"name": "record_unknown_question", |
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"description": "Always use this tool to record any question that couldn't be answered as you didn't know the answer", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"question": { |
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"type": "string", |
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"description": "The question that couldn't be answered" |
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}, |
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}, |
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"required": ["question"], |
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"additionalProperties": False |
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} |
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} |
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tools = [{"type": "function", "function": record_user_details_json}, |
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{"type": "function", "function": record_unknown_question_json}] |
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class Evaluation(BaseModel): |
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is_acceptable: bool |
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feedback: str |
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class Me: |
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def __init__(self): |
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self.openrouter = OpenAI( |
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base_url="https://openrouter.ai/api/v1", |
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api_key= os.getenv('OPEN_ROUTER_API_KEY') ) |
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self.gemini = OpenAI( |
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api_key=os.getenv("GOOGLE_API_KEY"), |
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base_url="https://generativelanguage.googleapis.com/v1beta/openai/" |
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) |
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self.name = "Chaoran Zhou" |
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reader = PdfReader("me/linkedin.pdf") |
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self.linkedin = "" |
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for page in reader.pages: |
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text = page.extract_text() |
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if text: |
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self.linkedin += text |
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with open("me/summary.txt", "r", encoding="utf-8") as f: |
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self.summary = f.read() |
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def handle_tool_call(self, tool_calls): |
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results = [] |
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for tool_call in tool_calls: |
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tool_name = tool_call.function.name |
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arguments = json.loads(tool_call.function.arguments) |
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print(f"Tool called: {tool_name}", flush=True) |
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tool = globals().get(tool_name) |
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result = tool(**arguments) if tool else {} |
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results.append({"role": "tool","content": json.dumps(result),"tool_call_id": tool_call.id}) |
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return results |
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def system_prompt(self): |
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system_prompt = f"You are acting as {self.name}. You are answering questions on {self.name}'s website, \ |
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particularly questions related to {self.name}'s career, background, skills and experience. \ |
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Your responsibility is to represent {self.name} for interactions on the website as faithfully as possible. \ |
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You are given a summary of {self.name}'s background and LinkedIn profile which you can use to answer questions. \ |
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Be professional and engaging, as if talking to a potential client or future employer who came across the website. \ |
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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. \ |
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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. " |
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system_prompt += f"\n\n## Summary:\n{self.summary}\n\n## LinkedIn Profile:\n{self.linkedin}\n\n" |
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system_prompt += f"With this context, please chat with the user, always staying in character as {self.name}." |
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return system_prompt |
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def evaluator_system_prompt(self): |
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evaluator_system_prompt = f"You are an evaluator that decides whether a response to a question is acceptable. \ |
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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. \ |
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The Agent is playing the role of {self.name} and is representing {self.name} on their website. \ |
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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. \ |
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The Agent has been provided with context on {self.name} in the form of their summary and LinkedIn details. Here's the information:" |
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evaluator_system_prompt += f"\n\n## Summary:\n{self.summary}\n\n## LinkedIn Profile:\n{self.linkedin}\n\n" |
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evaluator_system_prompt += f"With this context, please evaluate the latest response, replying with whether the response is acceptable and your feedback." |
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return evaluator_system_prompt |
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def evaluator_user_prompt(self, reply, message, history): |
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user_prompt = f"Here's the conversation between the User and the Agent: \n\n{history}\n\n" |
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user_prompt += f"Here's the latest message from the User: \n\n{message}\n\n" |
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user_prompt += f"Here's the latest response from the Agent: \n\n{reply}\n\n" |
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user_prompt += f"Please evaluate the response, replying with whether it is acceptable and your feedback." |
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return user_prompt |
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def evaluate(self, reply, message, history) -> Evaluation: |
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messages = [ |
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{"role": "system", "content": self.evaluator_system_prompt()}, |
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{"role": "user", "content": self.evaluator_user_prompt(reply, message, history)} |
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] |
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response = self.gemini.beta.chat.completions.parse( |
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model="gemini-2.5-flash-preview-05-20", |
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messages=messages, |
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response_format=Evaluation |
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) |
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return response.choices[0].message.parsed |
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def rerun(self, reply, message, history, feedback): |
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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" |
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updated_system_prompt += f"## Your attempted answer:\n{reply}\n\n" |
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updated_system_prompt += f"## Reason for rejection:\n{feedback}\n\n" |
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messages = [{"role": "system", "content": updated_system_prompt}] + history + [{"role": "user", "content": message}] |
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done = False |
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while not done: |
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response = self.gemini.chat.completions.create( |
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model="gemini-2.5-flash-preview-05-20", |
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messages=messages, |
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tools=tools |
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) |
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if response.choices[0].finish_reason == "tool_calls": |
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message_obj = response.choices[0].message |
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tool_calls = message_obj.tool_calls |
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results = self.handle_tool_call(tool_calls) |
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messages.append(message_obj) |
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messages.extend(results) |
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else: |
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done = True |
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return response.choices[0].message.content |
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def chat(self, message, history): |
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messages = [{"role": "system", "content": self.system_prompt()}] + history + [{"role": "user", "content": message}] |
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done = False |
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while not done: |
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response = self.openrouter.chat.completions.create(model="meta-llama/llama-3.3-8b-instruct:free", messages=messages, tools=tools) |
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if response.choices[0].finish_reason=="tool_calls": |
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message = response.choices[0].message |
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tool_calls = message.tool_calls |
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results = self.handle_tool_call(tool_calls) |
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messages.append(message) |
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messages.extend(results) |
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else: |
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done = True |
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reply = response.choices[0].message.content |
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try: |
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evaluation = self.evaluate(reply, message, history) |
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if evaluation.is_acceptable: |
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print("Passed evaluation - returning reply") |
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else: |
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print("Failed evaluation - retrying") |
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print(f"Feedback: {evaluation.feedback}") |
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reply = self.rerun(reply, message, history, evaluation.feedback) |
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except Exception as e: |
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print(f"Evaluation failed with error: {e}") |
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print("Proceeding with original reply") |
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return reply |
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if __name__ == "__main__": |
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me = Me() |
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gr.ChatInterface(me.chat, type="messages").launch(debug=True, share=False) |
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