from pathlib import Path

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 {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 Application:

    def __init__(self):
        self.openai = OpenAI()
        self.name = "MBTA Customer Service"
        data_dir = Path("data")
        pdf_paths = sorted(data_dir.glob("*.pdf"))
        parts = []
        for pdf_path in pdf_paths:
            reader = PdfReader(pdf_path)
            doc_text = ""
            for page in reader.pages:
                text = page.extract_text()
                if text:
                    doc_text += text
            if doc_text:
                parts.append(f"\n\n--- {pdf_path.name} ---\n\n{doc_text}")
        self.pdf_content = "".join(parts) if parts else ""
        with open("data/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 {self.name}, a helpful customer service agent for the MBTA (Massachusetts Bay Transportation Authority), Greater Boston's rapid transit system. \
You answer questions about MBTA history, service, schedules, modes of transportation (subways, buses, ferries, commuter rail), and related topics. \
Use the summary and PDF reference documents below as your primary source of knowledge. Answer accurately and cite specific documents when relevant (e.g. \"According to [filename]...\"). \
Be professional, clear, and courteous. \
If you don't know the answer to a question, use your record_unknown_question tool to record it, even if it's trivial or outside MBTA topics. \
If a user wants to be contacted or leave their details for follow-up, ask for their email and use your record_user_details tool to record it."

        system_prompt += f"\n\n## Summary:\n{self.summary}\n\n## PDF documents:\n{self.pdf_content}\n\n"
        system_prompt += f"Use this context to assist the user. Stay 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
    

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