from datetime import date
from functools import partial
import json
from typing import List
from fastapi import FastAPI, HTTPException, File, UploadFile, WebSocket
from fastapi.responses import StreamingResponse
from fastapi.middleware.cors import CORSMiddleware
from fastrtc import AdditionalOutputs, ReplyOnPause, Stream
from google import genai
from google.genai import types
from asyncio import sleep
import uuid
import shutil
import os
import gradio
from markitdown import MarkItDown
from dotenv import load_dotenv
from convert import clean_json_string
from functions import create_prompt
from pydantic import BaseModel
import functions
from groq import Groq
from elevenlabs import ElevenLabs
import numpy as np

import voice

app = FastAPI()
md = MarkItDown()

load_dotenv()
gemini_api_key = os.environ.get("GEMINI_API_KEY", "empty")
if not gemini_api_key or gemini_api_key == "empty":
    raise ValueError("GEMINI_API_KEY environment variable is not set or is empty.")


client = genai.Client(api_key=gemini_api_key)
groq_client = voice.groq_client
tts_client = voice.tts_client

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


@app.get("/")
def root():
    return {"message": "Welcome to the FastAPI application!"}


class StartSessionRequest(BaseModel):
    note_content: str
    title: str = "Learning with Vibe Learning"


@app.post("/start-voice-session")
def start_voice_session(request: StartSessionRequest):
    """
    This endpoint creates a new, context-aware voice chat session.
    It returns a public URL to the fastrtc/gradio interface.
    The frontend should open this URL (e.g., in an iframe or a new tab).
    """
    print(f"Received request to start session with title: '{request.title}'")

    # Use functools.partial to create a new handler function with the note_content "baked in"
    handler_with_context = partial(
        voice.voice_teacher_handler, note_content=request.note_content
    )

    # Create a new Stream instance for this specific session
    stream = Stream(
        handler=ReplyOnPause(handler_with_context, input_sample_rate=16000),
        modality="audio",
        mode="send-receive",
        ui_args={
            "title": request.title,
            "chatbot_initial": (
                [
                    {
                        "role": "assistant",
                        "content": "Hello! I'm ready to help you review your notes. What would you like to go over first?",
                    }
                ],
            ),
        },
    )

    # Launch the Gradio app in a separate thread and get the shareable URL
    # `share=True` is necessary to make it accessible from the internet.
    # In a production environment, you would host this behind a proper domain.
    share_url = stream.ui.launch(share=True, strict_cors=False)

    print(f"Session created. Frontend can connect at: {share_url}")
    return {"session_url": share_url}


@app.post("/documents")
async def generate_note_from_documents(file: UploadFile = File(...)):
    unique_id = uuid.uuid4()
    temp_dir = f"./temp/{unique_id}"
    try:
        # Create temp directory
        os.makedirs(temp_dir, exist_ok=True)

        # Save uploaded file
        file_path = f"{temp_dir}/{file.filename}"
        with open(file_path, "wb") as f:
            shutil.copyfileobj(file.file, f)

        # Convert file to Markdown (assuming MarkItDown handles the file path)
        result = md.convert(file_path)
        content = result.text_content

        # Summarize with Gemini API
        response = client.models.generate_content(
            model="gemini-2.5-flash",
            contents=functions.create_document_summarize_prompt(content),
        )
        summary = response.text  # Adjust based on actual response structure

        print(f"Generated summary: {summary}")
        print(f"Generated content: {content}")

        # Clean up temp directory
        shutil.rmtree(temp_dir)

        return {"summary": clean_json_string(summary)}

    except Exception as e:
        # Clean up on error
        if os.path.exists(temp_dir):
            shutil.rmtree(temp_dir)
        raise HTTPException(status_code=500, detail=f"Error processing file: {str(e)}")


class CreateQuizzesRequest(BaseModel):
    quiz_id: str
    note_content: str


class AnswerResponse:
    options_text: str
    is_correct: str


class QuestionResponse:
    quiz_id: str
    question_text: str
    question_type: str
    answers: List[AnswerResponse]


@app.post("/quizzes")
async def generate_quizzes_on_notes(request: CreateQuizzesRequest):
    print(request.note_content, functions.quiz_response_format)

    response = client.models.generate_content(
        model="gemini-2.5-flash",
        contents=functions.create_quizzes_on_notes_prompt(
            request.note_content, functions.quiz_response_format
        ),
    )

    quizzes_str = clean_json_string(response.text)
    print(quizzes_str)

    quizzes = json.loads(quizzes_str)

    for quiz in quizzes:
        quiz["quiz_id"] = request.quiz_id
        print(f"{quiz}\n")
        print("---------------------------------------------------------------------\n")

    return {"quizzes": quizzes}


class CreateStudySchedulesRequest(BaseModel):
    note_content: str
    startDay: date
    deadlineDay: date


@app.post("/study-schedules")
async def generate_study_schedules_on_notes(request: CreateQuizzesRequest):
    print(request.note_content, functions.quiz_response_format)

    response = client.models.generate_content(
        model="gemini-2.5-flash",
        contents=functions.create_quizzes_on_notes_prompt(
            request.note_content, functions.quiz_response_format
        ),
    )

    quizzes_str = clean_json_string(response.text)
    print(quizzes_str)

    quizzes = json.loads(quizzes_str)

    for quiz in quizzes:
        quiz["quiz_id"] = request.quiz_id
        print(f"{quiz}\n")
        print("---------------------------------------------------------------------\n")

    return {"quizzes": quizzes}


if __name__ == "__main__":
    import uvicorn

    uvicorn.run(app, host="0.0.0.0", port=8000)