# Import required libraries
from dotenv import load_dotenv
from openai import OpenAI
import json
import os
import requests
from pypdf import PdfReader
import gradio as gr

# Load environment variables from .env file (for API keys, etc.)
load_dotenv(override=True)

# Helper function to send push notifications using Pushover
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,
        }
    )

# Tool: Record user details (e.g., when a user provides their email)
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"}

# Tool: Record any question that could not be answered
def record_unknown_question(question):
    push(f"Recording {question}")
    return {"recorded": "ok"}

# JSON schema for the record_user_details tool (for OpenAI function calling)
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
    }
}

# JSON schema for the record_unknown_question tool (for OpenAI function calling)
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
    }
}

# List of available tools for the OpenAI agent
tools = [
    {"type": "function", "function": record_user_details_json},
    {"type": "function", "function": record_unknown_question_json}
]

# Main class representing "Me" (the agent persona)
class Me:

    def __init__(self):
        # Initialize OpenAI client and load profile information
        self.openai = OpenAI()
        self.name = "Harish"
        # Read LinkedIn profile from PDF
        reader = PdfReader("me/linkedin.pdf")
        self.linkedin = ""
        for page in reader.pages:
            text = page.extract_text()
            if text:
                self.linkedin += text
        # Read summary from text file
        with open("me/summary.txt", "r", encoding="utf-8") as f:
            self.summary = f.read()

    # Handle tool calls from the OpenAI agent (function calling)
    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
    
    # Compose the system prompt for the agent, including summary and LinkedIn profile
    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
    
    # Main chat method: handles conversation, tool calls, and responses
    def chat(self, message, history):
        # Build message history for OpenAI API
        messages = [{"role": "system", "content": self.system_prompt()}] + history + [{"role": "user", "content": message}]
        done = False
        while not done:
            # Call OpenAI chat completion with tools
            response = self.openai.chat.completions.create(model="gpt-4o-mini", messages=messages, tools=tools)
            if response.choices[0].finish_reason=="tool_calls":
                # If a tool call is required, handle it and continue
                message = response.choices[0].message
                tool_calls = message.tool_calls
                results = self.handle_tool_call(tool_calls)
                messages.append(message)
                messages.extend(results)
            else:
                # Otherwise, return the agent's response
                done = True
        return response.choices[0].message.content
    

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