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32e3e0c 6ce3660 32e3e0c 50582af 32e3e0c 6caace1 32e3e0c 6caace1 c4cf437 6caace1 41c5912 8551ca4 b0717f3 2a5ff21 32e3e0c 6caace1 32e3e0c e9ce4fc 4b52f16 40e5c03 4b52f16 8551ca4 730f05a 8551ca4 730f05a 41c5912 730f05a c4cf437 730f05a c4cf437 730f05a f2f209f c4cf437 41c5912 f2f209f 4e97f43 32e3e0c e71f49a 32e3e0c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 | from openai import OpenAI
import json
import os
import requests
from PyPDF2 import PdfReader
import gradio as gr
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 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 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)
try:
response = self.gemini.chat.completions.create(
model="gemini-2.5-flash-preview-05-20",
messages=messages,
tools=tools
)
except Exception as e:
print(f"Error during OpenAI API call: {e}")
return "Sorry, there was an error processing your request. Please check your API key and try again."
if response is None or not hasattr(response, "choices") or not response.choices:
return "Sorry, no response from the language model."
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
# canonical version
# 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 = 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
# try n catch version
# try:
# response = self.client.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
# except Exception as e:
# print(f"Error during OpenAI API call: {e}")
# return "Sorry, there was an error processing your request. Please check your API key and try again."
# return response.choices[0].message.content
if __name__ == "__main__":
me = Me()
gr.ChatInterface(me.chat, type="messages").launch(debug=True, share=False)
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