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
import logging

# Load environment variables
load_dotenv(override=True)
TOOL_SIMULATION = os.getenv("TOOL_SIMULATION", "false").lower() == "true"

# Setup logging
logging.basicConfig(filename="tool_logs.log", level=logging.INFO)

def push(text):
    if TOOL_SIMULATION:
        print(f"[SIMULATED PUSH]: {text}")
    else:
        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"):
    msg = f"Recording {name} with email {email} and notes {notes}"
    push(msg)
    logging.info(msg)
    return {"recorded": "ok"}

def record_unknown_question(question):
    msg = f"Recording unknown question: {question}"
    push(msg)
    logging.info(msg)
    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",
                "format": "email",
                "pattern": "^\\S+@\\S+\\.\\S+$"
            },
            "name": {
                "type": "string",
                "description": "The user's name, if they provided it"
            },
            "notes": {
                "type": "string",
                "description": "generate a summary of the conversation. 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 Evaluation(BaseModel):
    is_acceptable: bool
    feedback: str

class Me:

    def __init__(self):
        self.openai = OpenAI()
        self.name = "Sarthak Pawar"
        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 = []
        valid_tools = {
            "record_user_details": record_user_details,
            "record_unknown_question": record_unknown_question
        }
        for tool_call in tool_calls:
            try:
                tool_name = tool_call.function.name
                arguments = json.loads(tool_call.function.arguments)
                print(f"Tool called: {tool_name} with args: {arguments}", flush=True)
                if tool_name not in valid_tools:
                    push(f"Invalid tool call attempted: {tool_name}")
                    results.append({
                        "role": "tool",
                        "content": json.dumps({"error": f"Unknown tool: {tool_name}"}),
                        "tool_call_id": tool_call.id
                    })
                else:
                    result = valid_tools[tool_name](**arguments)
                    results.append({
                        "role": "tool",
                        "content": json.dumps(result),
                        "tool_call_id": tool_call.id
                    })
            except Exception as e:
                push(f"Error handling tool call: {str(e)}")
                results.append({
                    "role": "tool",
                    "content": json.dumps({"error": f"Error handling tool call: {str(e)}"}),
                    "tool_call_id": tool_call.id
                })
        return results

    def system_prompt(self):
        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. \

        IMPORTANT: If you don't know the answer to any question OR if the question is unrelated to {self.name}'s career/background/skills/experience, YOU MUST USE THE `record_unknown_question` tool. \

        If the user is engaging in discussion related to {self.name}'s career/background/skills/experience or wants to work with {self.name}, try to steer them towards getting in touch via email. \

        If the user provides their name and email, you should use the `record_user_details` tool.



        ## gaurd rails:

        - if the user is asking about {self.name}'s career/background/skills/experience or wants to work with {self.name}, the agent should get the user to provide their name and email and do not call any tools.

        - if the user is asking about anything unrelated to {self.name}'s career/background/skills/experience, the agent should use the `record_unknown_question` tool and try to guide the user back to the topic of {self.name}'s career/background/skills/experience.

        - if and only if the user provides their name and email, the agent should use the `record_user_details` tool and reply with a message that says "great, I'll get back to you as soon as possible."

        - if you think the conversation is going off topic, restric the bot to bring it back on topic: {self.name}'s career/background/skills/experience 



        """

        prompt += """

        

        ## Tool Call Evaluation Criteria:

        tool information:

        - only call record_user_details if the user provides their name or email.

        - only call record_unknown_question if the user is asking about anything unrelated to {self.name}'s career/background/skills/experience or you don't know the answer.



        if a specific tool was spposed to be called, but wasn't, that's a failure.

        if a tool was called, but the response is not related to the tool call, that's a failure.

        for example, 

        user: can you help me with my web development project?

        assistant: sure, I can help you with that.

        tool call: null

        this is a failure because the tool call was null. it should have been record_unknown_question.



        user: how can I contact you?

        assistant: please provide your name and email and I'll get back to you as soon as possible.

        tool call: null

        this is a success. the user is asking to be contacted, so the agent should get the user to provide their name and email.



        user: my name is shivam and you can contact me at [email protected]

        assistant: null

        tool call: record_user_details

        this is a success because the tool call was record_user_details and the response is related to the tool call.



        user: do you watch f1

        assistant: yes, I do.

        tool call: null

        this is a failure because the tool call was null. it should have been record_unknown_question. any question unrelated to the user's career/background/skills/experience should call record_unknown_question.



        user: who is your father

        assistant: null

        tool call: record_unknown_question

        this is a success because the tool call was record_unknown_question and the response is related to the tool call.



        user: how many stars are there in the milky way?

        assistant: null

        tool call: record_unknown_question

        this is a success because the tool call was record_unknown_question and the response is related to the tool call.

        

        

        user: would you be open to working with me?

        assistant: sure, I'd be happy to work with you. please provide your name and email and I'll get back to you as soon as possible.

        tool call: null

        this is a success because the user is asking to work with the agent, so the agent should get the user to provide their name and email.



        user: you can contact me at [email protected]

        assistant: null

        tool call: record_user_details

        this is a success because the user is providing their name and email, so the agent should use the `record_user_details` tool.



        



        """

        prompt += f"\n\n## Summary:\n{self.summary}\n\n## LinkedIn Profile:\n{self.linkedin}\n\n"
        return prompt

    def get_evaluator_prompt(self) -> str:
        return self.system_prompt() + "\n\nYou are now evaluating if the assistant is behaving correctly per these guidelines."

    def get_tool_response_prompt(self) -> str:
        return f"""You are {self.name} responding to a user after a tool has been called. 



## Context:

- A tool was just executed (either recording user details or recording an unknown question)

- You should now provide a natural, conversational response to the user

- Do NOT call any tools - just respond conversationally

- Keep the response professional and engaging

- If the tool was `record_user_details`, acknowledge that you'll get back to them

- If the tool was `record_unknown_question`, guide the conversation back to your career/background/skills/experience



## Your background:

{self.summary}



## LinkedIn Profile:

{self.linkedin}



Remember: You are representing {self.name} professionally. Be helpful, engaging, and steer conversations toward your expertise and career opportunities."""

    def evaluator_user_prompt(self, reply: str, message: str, history: str, tool_call) -> str:
        return f"""You are evaluating a conversation between a user and an AI assistant impersonating a real person.



---



## Conversation History:

{history}



---



## Latest User Message:

{message}



---



## Assistant's Latest Reply:

{reply}



---



## Tool Call:

{tool_call}



## Tool Call Evaluation Criteria:



tool information:

- only call record_user_details if the user provides their name or email.

- only call record_unknown_question if the user is asking about anything unrelated to {self.name}'s career/background/skills/experience or ##you don't know the answer.



if a specific tool was spposed to be called, but wasn't, that's a failure.

if a tool was called, but the response is not related to the tool call, that's a failure.

for example, 

    user: can you help me with my web development project?

    assistant: sure, I can help you with that.

    tool call: null

    this is a failure because the tool call was null. it should have been record_unknown_question.



    user: how can I contact you?

    assistant: please provide your name and email and I'll get back to you as soon as possible.

    tool call: null

    this is a success. the user is asking to be contacted, so the agent should get the user to provide their name and email.



    user: my name is shivam and you can contact me at [email protected]

    assistant: null

    tool call: record_user_details

    this is a success because the tool call was record_user_details and the response is related to the tool call.



    user: do you watch f1

    assistant: yes, I do.

    tool call: null

    this is a failure because the tool call was null. it should have been record_unknown_question. any question unrelated to the user's career/background/skills/experience should call record_unknown_question.



    user: who is your father

    assistant: null

    tool call: record_unknown_question

    this is a success because the tool call was record_unknown_question and the response is related to the tool call.



    user: how many stars are there in the milky way?

    assistant: null

    tool call: record_unknown_question

    this is a success because the tool call was record_unknown_question and the response is related to the tool call.

    

    

    user: would you be open to working with me?

    assistant: sure, I'd be happy to work with you. please provide your name and email and I'll get back to you as soon as possible.

    tool call: null

    this is a success because the user is asking to work with the agent, so the agent should get the user to provide their name and email.



    user: you can contact me at [email protected]

    assistant: null

    tool call: record_user_details

    this is a success because the user is providing their name and email, so the agent should use the `record_user_details` tool.



    

    



## gaurd rails:

- when a tool call is made, the agent will not provide a reply. that will be handled in the next step. so don't judge the reply when a tool call is made. because it will be null.

- if the user is asking about {self.name}'s career/background/skills/experience or wants to work with {self.name}, the agent should get the user to provide their name and email and do not call any tools.

- if the user is asking about anything unrelated to {self.name}'s career/background/skills/experience, the agent should use the `record_unknown_question` tool and try to guide the user back to the topic of {self.name}'s career/background/skills/experience.

- if and only if the user provides their name and email, the agent should use the `record_user_details` tool and reply with a message that says "great, I'll get back to you as soon as possible."

- if you think the conversation is going off topic, restric the bot to bring it back on topic: {self.name}'s career/background/skills/experience 

    



Please evaluate the assistant's response.

- Is the response acceptable? (True/False)

- Feedback: (Explain what was good or what needs improvement)"""

    def evaluate(self, reply, message, history, tool_call) -> Evaluation:
        messages = [
            {"role": "system", "content": self.get_evaluator_prompt()},
            {"role": "user", "content": self.evaluator_user_prompt(reply, message, history, tool_call)}
        ]
        try:
            response = self.openai.beta.chat.completions.parse(
                model="gpt-4.1-mini",
                messages=messages,
                response_format=Evaluation
            )
            return response.choices[0].message.parsed
        except Exception as e:
            push(f"Evaluation failed: {str(e)}")
            return Evaluation(is_acceptable=False, feedback="Evaluation parsing failed or incomplete.")

    def rerun(self, reply, message, history, feedback):
        updated_system_prompt = self.system_prompt() + f"\n\n## Previous answer rejected:\n{reply}\n\nReason: {feedback}\n respond appropriately according to the gaurd rails."
        messages = [{"role": "system", "content": updated_system_prompt}] + history + [{"role": "user", "content": message}]
        return self.openai.chat.completions.create(model="gpt-4.1-mini", messages=messages, tools=tools)

    def chat(self, message, history):
        history = history[-10:]
        messages = [{"role": "system", "content": self.system_prompt()}] + history + [{"role": "user", "content": message}]
        satisfied = False
        max_retries = 3
        retries = 0

        response = self.openai.chat.completions.create(model="gpt-4.1-mini", messages=messages, tools=tools)
        reply = response.choices[0].message.content
        finish_reason = response.choices[0].finish_reason
        tool_call = response.choices[0].message.tool_calls

        feedback = ""

        while not satisfied and retries < max_retries:

            if(retries > 0):
                response = self.rerun(reply, message, history, feedback)
                reply = response.choices[0].message.content
                finish_reason = response.choices[0].finish_reason
                tool_call = response.choices[0].message.tool_calls

                print(f"rerun_message: {response.choices[0].message}\n\n", flush=True)
                print(f"rerun_reply: {reply}\n\n", flush=True)
                print(f"rerun_message: {message}\n\n", flush=True)
                print(f"rerun_history: {history}\n\n", flush=True)
                print(f"rerun_tool_call: {tool_call}\n\n", flush=True)
                

            evaluation = self.evaluate(reply, message, history, tool_call)
            if evaluation.is_acceptable:
                retries = 0
                print(f"Evaluation successful: {evaluation.feedback}", flush=True)
                if finish_reason == "tool_calls":
                    print(f"Tool calls: {tool_call}", flush=True)
                    tool_calls = response.choices[0].message.tool_calls
                    results = self.handle_tool_call(tool_calls)
                    messages.append(response.choices[0].message)
                    messages.extend(results)
                    response_messages = [{"role": "system", "content": self.get_tool_response_prompt()}] + history + [{"role": "user", "content": message}]
                    response = self.openai.chat.completions.create(model="gpt-4.1-mini", messages=response_messages)
                    reply = response.choices[0].message.content
                    print(f"response: {response.choices[0].message}\n\n", flush=True)

                    satisfied = True
                    return reply
                print(f"satisfied\n\n", flush=True)
                satisfied = True
            else:
                print(f"reply: {reply}\n\n", flush=True)
                print(f"message: {message}\n\n", flush=True)
                print(f"history: {history}\n\n", flush=True)
                print(f"tool_call: {tool_call}\n\n", flush=True)
                print(f"Evaluation failed: {evaluation.feedback}\n\n", flush=True)
                feedback = evaluation.feedback
                retries += 1
        if(retries >= max_retries):
            print(f"Max retries reached\n\n", flush=True)
            return "I'm sorry, I'm having trouble answering your question. can we move back to talking about my career/background/skills/experience?"
        
        return reply


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