from flask import Flask, request, render_template, redirect, url_for
import os
from moviepy.editor import VideoFileClip
import whisper
import hashlib

app = Flask(__name__)

# Configure the maximum content length for uploads (500 MB)
app.config['MAX_CONTENT_LENGTH'] = 1024 * 1024 * 500  # 500 MB limit

# Create directories for uploads and cache
UPLOAD_FOLDER = 'uploads'
AUDIO_FOLDER = 'audio_cache'
TRANSCRIPT_FOLDER = 'transcript_cache'
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
os.makedirs(AUDIO_FOLDER, exist_ok=True)
os.makedirs(TRANSCRIPT_FOLDER, exist_ok=True)

# Set environment variable for Whisper cache
os.environ["XDG_CACHE_HOME"] = "/app/.cache"

# Load the Whisper model
model = whisper.load_model("base")

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

@app.route('/upload', methods=['POST'])
def upload_video():
    if 'video' not in request.files:
        return redirect(url_for('index'))

    video_file = request.files['video']
    if video_file.filename == '':
        return redirect(url_for('index'))

    # Save the video file
    video_path = os.path.join(UPLOAD_FOLDER, video_file.filename)
    video_file.save(video_path)

    try:
        # Generate a unique hash for the video file to use as a cache key
        video_hash = hashlib.md5(video_file.read()).hexdigest()

        # Check if the audio and transcript are already cached
        audio_path = os.path.join(AUDIO_FOLDER, f"{video_hash}.wav")
        transcript_path = os.path.join(TRANSCRIPT_FOLDER, f"{video_hash}.txt")

        if not os.path.exists(audio_path):
            # Extract audio from the video if not cached
            audio_path = extract_audio(video_path, audio_path)

        if not os.path.exists(transcript_path):
            # Transcribe the audio if not cached
            transcript = transcribe_audio(audio_path)
            # Cache the transcript
            with open(transcript_path, 'w') as f:
                f.write(transcript)
        else:
            # Load cached transcript
            with open(transcript_path, 'r') as f:
                transcript = f.read()

    except Exception as e:
        return f"Error: {e}"

    return render_template('result.html', transcript=transcript)

def extract_audio(video_path, audio_path):
    try:
        # Use a temporary file to reduce the load on memory
        with VideoFileClip(video_path) as video:
            video.audio.write_audiofile(audio_path)
    except Exception as e:
        raise RuntimeError(f"Error extracting audio: {e}")
    return audio_path

def transcribe_audio(audio_path):
    if not os.path.exists(audio_path):
        raise FileNotFoundError(f"Audio file not found at {audio_path}")

    try:
        result = model.transcribe(audio_path)
        return result["text"]
    except Exception as e:
        raise RuntimeError(f"Error during transcription: {e}")

if __name__ == '__main__':
    app.run(debug=False, host='0.0.0.0', port=7860)