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
from pydantic import BaseModel


class Evaluation(BaseModel):
    is_acceptable: bool
    feedback: str


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 = "Anindhya Kushagra"
        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 += "Please evaluate the response, replying with whether it is acceptable and your feedback."
        return user_prompt
    
    def evaluate(self, reply, message, history):
        messages = [{"role": "system", "content": self.evaluator_system_prompt()}] + [{"role": "user", "content": self.evaluator_user_prompt(reply, message, history)}]
        response = self.openai.chat.completions.create(model="gpt-4o-mini", messages=messages, response_format=Evaluation)
        return response.choices[0].message.parsed

    def rerun(self, reply, message, history, feedback):
        updated_system_prompt = self.system_prompt() + "\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}]
        response = self.openai.chat.completions.create(model="gpt-4o-mini", messages=messages)
        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
        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()