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 unknown question: {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 context"},
        },
        "required": ["email"],
        "additionalProperties": False
    }
}

record_unknown_question_json = {
    "name": "record_unknown_question",
    "description": "Record any question that couldn't be answered",
    "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 JoshuaChatBot:
    def __init__(self):
        self.openai = OpenAI()
        self.name = "Joshua Sam Mathew"

        # Read resume
        reader = PdfReader("JoshuaCh/josh_resume.pdf")
        self.josh_resume = "".join(page.extract_text() or "" for page in reader.pages)

        # Read LinkedIn
        reader = PdfReader("JoshuaCh/josh_linkedin.pdf")
        self.josh_linkedin = "".join(page.extract_text() or "" for page in reader.pages)

        # Read summary
        with open("JoshuaCh/josh_summary.txt", "r", encoding="utf-8") as f:
            self.josh_summary = f.read()

    def system_prompt(self):
        return f"""
        You are acting as {self.name}. You are answering questions on {self.name}'s website,
        particularly about his career, background, skills, and experience.
        Be professional and engaging, as if speaking to a potential client or employer.
        Use the summary, resume, and LinkedIn information to answer accurately.
        If you don't know an answer, use the record_unknown_question tool. But if its a common question or basic question thats not related to my carear details u can answer it on your own keeping everything proffessional, even if it's about something trivial or unrelated to career.
        If a user wants to connect, ask for their email and record it with record_user_details.
        ## Summary:
        {self.josh_summary}
        ## Resume:
        {self.josh_resume}
        ## LinkedIn:
        {self.josh_linkedin}
        """

    def handle_tool_call(self, tool_calls):
        results = []
        for tool_call in tool_calls:
            fn = tool_call.function.name
            args = json.loads(tool_call.function.arguments)
            if fn == "record_user_details":
                result = record_user_details(**args)
            elif fn == "record_unknown_question":
                result = record_unknown_question(**args)
            else:
                result = {"error": "Unknown tool"}
            results.append({"role": "tool", "content": json.dumps(result)})
        return results

    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)
            msg = response.choices[0].message

            if hasattr(msg, "tool_calls") and msg.tool_calls:
                results = self.handle_tool_call(msg.tool_calls)
                messages.append(msg)
                messages.extend(results)
            else:
                done = True

        return response.choices[0].message.content

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