dctazewell/1_foundations
0
1from dotenv import load_dotenv
2from openai import OpenAI
3import json
4import os
5import requests
6from pypdf import PdfReader
7import gradio as gr
8
9
10load_dotenv(override=True)
11
12def push(text):
13 requests.post(
14 "https://api.pushover.net/1/messages.json",
15 data={
16 "token": os.getenv("PUSHOVER_TOKEN"),
17 "user": os.getenv("PUSHOVER_USER"),
18 "message": text,
19 }
20 )
21
22
23def record_user_details(email, name="Name not provided", notes="not provided"):
24 push(f"Recording {name} with email {email} and notes {notes}")
25 return {"recorded": "ok"}
26
27def record_unknown_question(question):
28 push(f"Recording {question}")
29 return {"recorded": "ok"}
30
31record_user_details_json = {
32 "name": "record_user_details",
33 "description": "Use this tool to record that a user is interested in being in touch and provided an email address",
34 "parameters": {
35 "type": "object",
36 "properties": {
37 "email": {
38 "type": "string",
39 "description": "The email address of this user"
40 },
41 "name": {
42 "type": "string",
43 "description": "The user's name, if they provided it"
44 }
45 ,
46 "notes": {
47 "type": "string",
48 "description": "Any additional information about the conversation that's worth recording to give context"
49 }
50 },
51 "required": ["email"],
52 "additionalProperties": False
53 }
54}
55
56record_unknown_question_json = {
57 "name": "record_unknown_question",
58 "description": "Always use this tool to record any question that couldn't be answered as you didn't know the answer",
59 "parameters": {
60 "type": "object",
61 "properties": {
62 "question": {
63 "type": "string",
64 "description": "The question that couldn't be answered"
65 },
66 },
67 "required": ["question"],
68 "additionalProperties": False
69 }
70}
71
72tools = [{"type": "function", "function": record_user_details_json},
73 {"type": "function", "function": record_unknown_question_json}]
74
75
76class Me:
77
78 def __init__(self):
79 self.openai = OpenAI()
80 self.name = "Darian Tazewell"
81 reader = PdfReader("me/linkedin.pdf")
82 self.linkedin = ""
83 for page in reader.pages:
84 text = page.extract_text()
85 if text:
86 self.linkedin += text
87 with open("me/summary.txt", "r", encoding="utf-8") as f:
88 self.summary = f.read()
89
90
91 def handle_tool_call(self, tool_calls):
92 results = []
93 for tool_call in tool_calls:
94 tool_name = tool_call.function.name
95 arguments = json.loads(tool_call.function.arguments)
96 print(f"Tool called: {tool_name}", flush=True)
97 tool = globals().get(tool_name)
98 result = tool(**arguments) if tool else {}
99 results.append({"role": "tool","content": json.dumps(result),"tool_call_id": tool_call.id})
100 return results
101
102 def system_prompt(self):
103 system_prompt = f"You are acting as {self.name}. You are answering questions on {self.name}'s website, \
104particularly questions related to {self.name}'s career, background, skills and experience. \
105Your responsibility is to represent {self.name} for interactions on the website as faithfully as possible. \
106You are given a summary of {self.name}'s background and LinkedIn profile which you can use to answer questions. \
107Be professional and engaging, as if talking to a potential client or future employer who came across the website. \
108If 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. \
109If 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. "
110
111 system_prompt += f"\n\n## Summary:\n{self.summary}\n\n## LinkedIn Profile:\n{self.linkedin}\n\n"
112 system_prompt += f"With this context, please chat with the user, always staying in character as {self.name}."
113 return system_prompt
114
115 def chat(self, message, history):
116 messages = [{"role": "system", "content": self.system_prompt()}] + history + [{"role": "user", "content": message}]
117 done = False
118 while not done:
119 response = self.openai.chat.completions.create(model="gpt-4o-mini", messages=messages, tools=tools)
120 if response.choices[0].finish_reason=="tool_calls":
121 message = response.choices[0].message
122 tool_calls = message.tool_calls
123 results = self.handle_tool_call(tool_calls)
124 messages.append(message)
125 messages.extend(results)
126 else:
127 done = True
128 return response.choices[0].message.content
129
130
131if __name__ == "__main__":
132 me = Me()
133 gr.ChatInterface(me.chat, type="messages").launch()
134 