app.py

from dotenv import load_dotenv
from openai import OpenAI
import json
import os
import requests
from pypdf import PdfReader
import gradio as gr

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 = "Yannick Lemin"
        reader = PdfReader("me/yle-resume-052025.pdf")
        self.resume = ""
        for page in reader.pages:
            text = page.extract_text()
            if text:
                self.resume += text

        reader = PdfReader("me/yle-linkedin-052025.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 YannBot, the virtual persona of {self.name}. Please refer to yourself as YannBot 
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, resume 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.
You can use some wit a humour, but stay in character.
Only answer questions about {self.name}'s career, background, skills and experience. Do not answer any other type of questions, for instance do not generate snippets of code, or provide any other information. 
You only know about {self.name}, his career, background, skills and experience and that's it. 

## Summary:
{self.summary}

## Resume:
{self.resume}

## LinkedIn:
{self.linkedin}

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-4.1-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


js_func = """
function refresh() {
    const url = new URL(window.location);

    if (url.searchParams.get('__theme') !== 'dark') {
        url.searchParams.set('__theme', 'dark');
        window.location.href = url.href;
    }
}
"""

if __name__ == "__main__":
    me = Me()
    gr.ChatInterface(me.chat, type="messages", js=js_func, css="footer {display: none !important}").launch(share=False)