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| import os | |
| from dotenv import load_dotenv | |
| from openai import OpenAI | |
| import gradio as gr | |
| import requests | |
| import json | |
| from PyPDF2 import PdfReader | |
| load_dotenv(override=True) | |
| 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): | |
| self.openai = OpenAI() | |
| self.name = "Hariprasad Bantwal" | |
| # Load LinkedIn PDF | |
| reader = PdfReader("./me/linkedin.pdf") | |
| self.linkedin = "" | |
| for page in reader.pages: | |
| text = page.extract_text() | |
| if text: | |
| self.linkedin += text | |
| # Load summary text | |
| with open("./me/summary.txt", "r", encoding="utf-8") as f: | |
| self.summary = f.read() | |
| # Load Resume PDF | |
| resume_reader = PdfReader("./me/Resume.pdf") | |
| self.resume = "" | |
| for resume_page in resume_reader.pages: | |
| text = resume_page.extract_text() | |
| if text: | |
| self.resume += text | |
| # Load 2004-08-Reference | |
| with open("./me/2004-08-Reference.txt", "r", encoding="utf-8") as f: | |
| self.reference_2004_08 = f.read() | |
| # Load 2008-10-Reference_1 | |
| with open("./me/2008-10-Reference_1.txt", "r", encoding="utf-8") as f: | |
| self.reference_2008_10_1 = f.read() | |
| # Load 2008-10-Reference | |
| with open("./me/2008-10-Reference.txt", "r", encoding="utf-8") as f: | |
| self.reference_2008_10 = f.read() | |
| # Load 2011-13-Reference | |
| with open("./me/2011-13-Reference.txt", "r", encoding="utf-8") as f: | |
| self.reference_2011_13 = f.read() | |
| # Load 2013-18-Reference | |
| with open("./me/2013-18-Reference.txt", "r", encoding="utf-8") as f: | |
| self.reference_2013_18 = f.read() | |
| # Load 2018-19-Reference | |
| with open("./me/2018-19-Reference.txt", "r", encoding="utf-8") as f: | |
| self.reference_2018_19 = f.read() | |
| # Load 2020-23-Reference | |
| with open("./me/2020-23-Reference.txt", "r", encoding="utf-8") as f: | |
| self.reference_2020_23 = f.read() | |
| # Load 2023-24-Reference | |
| with open("./me/2023-24-Reference.txt", "r", encoding="utf-8") as f: | |
| self.reference_2023_24 = 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. \ | |
| You are given a list of Reference Letters of {self.name}'s which gives you the context, time frame, company name you should also try to use this 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## Resume:\n{self.resume}\n\n" | |
| system_prompt += f"\n\n## 2004-08-Reference:\n{self.reference_2004_08}\n\n## 2008-10-Reference_1:\n{self.reference_2008_10_1}\n\n" | |
| system_prompt += f"\n\n## 2008-10-Reference:\n{self.reference_2008_10}\n\n## 2011-13-Reference:\n{self.reference_2011_13}\n\n" | |
| system_prompt += f"\n\n## 2013-18-Reference:\n{self.reference_2013_18}\n\n## 2018-19-Reference:\n{self.reference_2018_19}\n\n" | |
| system_prompt += f"\n\n## 2020-23-Reference:\n{self.reference_2020_23}\n\n## 2023-24-Reference:\n{self.reference_2023_24}\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 | |
| while not done: | |
| response = self.openai.chat.completions.create( | |
| model="gpt-4o-mini", | |
| 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 | |
| if __name__ == "__main__": | |
| me = Me() | |
| gr.ChatInterface(me.chat, type="messages").launch() | |