app.py

"""
YouTube Clip Analyzer - Identifies viral/interesting timestamps in videos
using Hugging Face models for AI processing.
"""
import gradio as gr
from pytube import YouTube
from moviepy.editor import VideoFileClip
import os
import logging
import time
import requests
import json
import torch
import whisper
from transformers import pipeline

# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# Initialize models
try:
    logger.info("Initializing models")
    whisper_model = whisper.load_model("tiny")
    sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
except Exception as e:
    logger.error(f"Failed to initialize models: {str(e)}")
    whisper_model = None
    sentiment_analyzer = None
    summarizer = None

def download_youtube_audio(youtube_url):
    """Download audio from YouTube video"""
    try:
        yt = YouTube(youtube_url)
        audio_stream = yt.streams.filter(only_audio=True).first()
        audio_path = "temp_audio.mp4"
        audio_stream.download(filename=audio_path)
        
        # Convert to WAV for better compatibility with speech recognition
        video = VideoFileClip(audio_path)
        wav_path = "temp_audio.wav"
        video.audio.write_audiofile(wav_path, ffmpeg_params=["-ac", "1", "-ar", "16000"])
        video.close()
        os.remove(audio_path)
        
        return wav_path, yt.title
    except Exception as e:
        logger.error(f"Error downloading YouTube audio: {str(e)}")
        raise

def analyze_youtube(youtube_url, progress=gr.Progress()):
    """Main function to analyze YouTube video"""
    try:
        progress(0.1, desc="Downloading YouTube audio...")
        
        # Download audio
        wav_path, video_title = download_youtube_audio(youtube_url)
        
        progress(0.3, desc="Transcribing audio...")
        # Transcribe audio
        result = whisper_model.transcribe(wav_path, fp16=False)
        segments = result["segments"]
        
        progress(0.5, desc="Processing transcript...")
        # Find clips
        clips = []
        for i in range(len(segments)):
            start_time = segments[i]["start"]
            for j in range(i, min(i + 10, len(segments))):
                end_time = segments[j]["end"]
                duration = end_time - start_time
                if 30 <= duration <= 60:
                    text = " ".join([seg["text"] for seg in segments[i:j+1]])
                    if text.strip():
                        # Analyze sentiment
                        sentiment_result = sentiment_analyzer(text)[0]
                        score = sentiment_result["score"]
                        
                        # Generate summary if text is long enough
                        summary = text
                        if len(text) > 100:
                            try:
                                summary_result = summarizer(text, max_length=100, min_length=30, do_sample=False)
                                summary = summary_result[0]["summary_text"]
                            except Exception as e:
                                logger.error(f"Summarization error: {str(e)}")
                        
                        clips.append({
                            "start": start_time,
                            "end": end_time,
                            "score": score,
                            "text": text,
                            "summary": summary
                        })
        
        progress(0.9, desc="Finalizing results...")
        # Clean up
        if os.path.exists(wav_path):
            os.remove(wav_path)
        
        # Sort and format results
        clips.sort(key=lambda x: x["score"], reverse=True)
        top_clips = clips[:3]
        
        output = f"## Analysis Results for: {video_title}\n\n"
        for i, clip in enumerate(top_clips, 1):
            start_time_fmt = f"{int(clip['start']//60):02d}:{int(clip['start']%60):02d}"
            end_time_fmt = f"{int(clip['end']//60):02d}:{int(clip['end']%60):02d}"
            
            output += f"### Clip {i}\n"
            output += f"⏱️ Time: {start_time_fmt} - {end_time_fmt}\n"
            output += f"📊 Interest Score: {clip['score']:.2f}\n"
            output += f"💬 Summary: {clip['summary']}\n\n"
            
            # Add direct link to timestamp
            video_id = youtube_url.split("v=")[1].split("&")[0] if "v=" in youtube_url else ""
            if video_id:
                timestamp_seconds = int(clip["start"])
                output += f"🔗 [Watch this segment](https://youtu.be/{video_id}?t={timestamp_seconds})\n\n"
        
        progress(1.0, desc="Done!")
        return youtube_url, output
    except Exception as e:
        logger.error(f"Error: {str(e)}")
        return None, f"Error processing video: {str(e)}"

# Create Gradio interface
demo = gr.Interface(
    fn=analyze_youtube,
    inputs=gr.Textbox(
        label="YouTube URL",
        placeholder="Enter YouTube URL (e.g., https://www.youtube.com/watch?v=dQw4w9WgXcQ)"
    ),
    outputs=[
        gr.Video(label="Video"),
        gr.Markdown(label="Analysis Results")
    ],
    title="YouTube Viral Clip Analyzer",
    description="Identify the most interesting timestamps in YouTube videos using AI analysis.",
    examples=[
        ["https://www.youtube.com/watch?v=Yf_1w00qIKc"],
        ["https://www.youtube.com/watch?v=dQw4w9WgXcQ"]
    ]
)

# Launch the app
if __name__ == "__main__":
    try:
        demo.launch(server_port=7861)
    except Exception as e:
        logger.error(f"Failed to launch on port 7861: {str(e)}")
        # Try with different settings
        demo.launch(share=True)