app.py

from typing import Self
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)
openai = OpenAI()

# Mukesh: For evaluation

from pydantic import BaseModel

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

gemini = OpenAI(
    api_key=os.getenv("GOOGLE_API_KEY"), 
    base_url="https://generativelanguage.googleapis.com/v1beta/openai/"
)

groq = OpenAI(
    api_key=os.getenv("GROQ_API_KEY"),
    base_url="https://api.groq.com/openai/v1"
)

# Mukesh: End for evaluation
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 = "Mukesh Patil"
        reader = PdfReader("me/LinkedIn Profile.pdf")
        self.linkedin = ""
        for page in reader.pages:
            text = page.extract_text()
            if text:
                self.linkedin += text
        
        Resume_reader = PdfReader("me/Mukesh Patil Resume.pdf")
        self.Resume = ""
        for page in reader.pages:
            text = page.extract_text()
            if text:
                self.Resume += 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, "
            f"particularly questions related to {self.name}'s career, background, skills, and experience. "
            f"Your responsibility is to represent {self.name} for interactions on the website as faithfully as possible. "
            f"You are given a summary of {self.name}'s background, LinkedIn profile, and Resume, which you can use to answer questions. "
            f"Be professional and engaging, as if talking to a potential client or future employer who came across the website. "
            f"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, "
            f"even if it's about something trivial or unrelated to career. "
            f"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. "
            f"If the user has already provided their email in the chat, do not ask again."
        )

        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"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
    
    ########################################################
    ### Addtional code added by Mukesh for evaluator
    ########################################################


    def evaluate_system_prompt (self):
        evaluator_system_prompt = (
            f"You are an evaluator that decides whether a response to a question is acceptable. "
            f"You are provided with a conversation between a User and an Agent. "
            f"Your task is to decide whether the Agent's latest response is acceptable quality. "
            f"The Agent is playing the role of {self.name} and is representing {self.name} on their website. "
            f"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. "
            f"The Agent has been provided with context on {self.name} in the form of their summary and LinkedIn details. Here's the information:"
            f"\n\n## Summary:\n{self.summary}"
            f"\n\n## LinkedIn Profile:\n{self.linkedin}"
            f"\n\n## Resume:\n{self.Resume}\n\n"
            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):
        evaluator_user_prompt = f"Here's the conversation between the User and the Agent: \n\n{history}\n\n"
        evaluator_user_prompt += f"Here's the latest message from the User: \n\n{message}\n\n"
        evaluator_user_prompt += f"Here's the latest response from the Agent: \n\n{reply}\n\n"
        evaluator_user_prompt += "Please evaluate the response, replying with whether it is acceptable and your feedback."
        return evaluator_user_prompt



    def evaluate(self, reply, message, history) -> Evaluation:
        messages = [{"role": "system", "content": self.evaluate_system_prompt()}] + [{"role": "user", "content": self.evaluator_user_prompt(reply, message, history)}]
        #response = gemini.beta.chat.completions.parse(model="gemini-2.5-flash", messages=messages, response_format=Evaluation)
        response = groq.chat.completions.parse(model="openai/gpt-oss-120b", 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 = openai.chat.completions.create(model="gpt-4o-mini", messages=messages)
        return response.choices[0].message.content

    ## Modified function which calls evaluator
    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 "patent" in message:
         #   system = self.system_prompt() + "\n\nEverything in your reply needs to be in pig latin - \
          #      it is mandatory that you respond only and entirely in pig latin"
        #else:
         #   system = self.system_prompt()
        #messages = [{"role": "system", "content": system}] + history + [{"role": "user", "content": message}]
        #response = openai.chat.completions.create(model="gpt-4o-mini", messages=messages)
        reply =response.choices[0].message.content

        evaluation = self.evaluate(reply, message, history)

        if evaluation.is_acceptable:
            print("Passed evaluation - returning reply")
        else:
            print("Failed evaluation - retrying")
            print(evaluation.feedback)
            reply = self.rerun(reply, message, history, evaluation.feedback)       
        return reply


## Funtion without evaluator
# 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) 
#


## End of additional code added by Mukesh for evaluator

if __name__ == "__main__":
    me = Me()
    gr.ChatInterface(me.chat, type="messages").launch()