import os.path
import traceback

from flask import Flask, render_template, request
from flask_cors import CORS
from flask_executor import Executor
from flask_socketio import SocketIO, emit
from gevent import monkey
from utils import get_search_index
from scipy.io import wavfile
import base64, io
import numpy as np
import whisper
from main import run

monkey.patch_all(ssl=False)

app = Flask(__name__)
app.config['SECRET_KEY'] = 'secret!'

socketio = SocketIO(app, cors_allowed_origins="*", logger=True)
# cors = CORS(app)
executor = Executor(app)

executor.init_app(app)
app.config['EXECUTOR_MAX_WORKERS'] = 5

model = whisper.load_model('small.en')

@app.route('/')
def index():
    get_search_index()
    return render_template('index.html')


@socketio.on('message')
def handle_message(data):
    question = data['question']
    print("question: " + question)

    if executor.futures:
        emit('response', {'response': 'Server is busy, please try again later'})
        return

    try:
        future = executor.submit(run, question)
        response = future.result()
        emit('response', {'response': response})
    except Exception as e:
        traceback.print_exc()
        # print(f"Error processing request: {str(e)}")
        emit('response', {'response': 'Server is busy. Please try again later.'})

@app.route('/audio', methods=['POST'])
def handle_audio():
    # print the request files and names
    print(request.files)
    audio_data = request.files['audio']
    audio_data.save('audio.webm')
    print("audio data received: " + str(audio_data))

    if os.path.isfile('audio.webm'):
        print("audio file exists")
        # Transcribe the audio data using OpenAI Whisper
        transcript = whisper.transcribe(model, 'audio.webm')
        data = {'question': transcript['text']}
        handle_message(data)


if __name__ == '__main__':
    socketio.run(app, port=5001)