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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}]
# Create a Pydantic model for the Evaluation
class Evaluation(BaseModel):
is_acceptable: bool
feedback: str
class Me:
def __init__(self):
# when saving secret in HF space, don't use "" :-)
# Initialize Open Router client using OpenAI format
# open_router_api_key = os.getenv('OPEN_ROUTER_API_KEY')
# if open_router_api_key:
# print(f"Checking Keys: Open router API Key exists and begins {open_router_api_key[:8]}")
# else:
# print("Checking Keys: Open router API Key not set - please head to the troubleshooting guide in the setup folder")
self.openrouter = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key= os.getenv('OPEN_ROUTER_API_KEY') ) # open_router_api_key
# Initialize Gemini client using OpenAI format
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
# Generate initial response with tool handling
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
# Evaluate the response
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)
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