app.py

import gradio as gr
from transformers import pipeline
import yt_dlp
import whisper
import os
import uuid
import re

# Delete temporary files
def clean_temp_files():
    temp_files = ["temp_video.mp4", "temp_audio.mp3"]
    for file in temp_files:
        if os.path.exists(file):
            os.remove(file)

# Download TikTok video
def download_video(video_url):
    try:
        ydl_opts = {
            'format': 'bestvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]',
            'outtmpl': 'temp_video.mp4',
            'quiet': True,
            'no_warnings': True,
            'extractor_args': {'tiktok': {'skip_watermark': True}}
        }
        
        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
            ydl.download([video_url])
        return "temp_video.mp4"
    except Exception as e:
        print(f"Download error: {e}")
        return None

# Extract audio (temporary)
def extract_audio(video_path):
    os.system(f"ffmpeg -i \"{video_path}\" -vn -acodec libmp3lame -q:a 3 \"temp_audio.mp3\" -y")
    return "temp_audio.mp3" if os.path.exists("temp_audio.mp3") else None

# Transcribe audio
def transcribe_audio(audio_path):
    try:
        model = whisper.load_model("base")
        result = model.transcribe(audio_path)
        return result['text']
    except Exception as e:
        print(f"Transcription error: {e}")
        return None

# Classify content
def classify_content(text):
    try:
        if not text or len(text.strip()) == 0:
            return None, None
            
        classifier = pipeline("zero-shot-classification", 
                           model="facebook/bart-large-mnli")
        
        labels = ["educational", "entertainment", "news", "political", 
                "religious", "technical", "advertisement", "social"]
        
        result = classifier(text, 
                          candidate_labels=labels, 
                          hypothesis_template="This text is about {}.")
        
        return result['labels'][0], result['scores'][0]
    except Exception as e:
        print(f"Classification error: {e}")
        return None, None

# Main processing function
def process_video(video_url):
    clean_temp_files()
    
    if not video_url or len(video_url.strip()) == 0:
        return "Please enter a valid TikTok URL", ""
    
    if "tiktok.com" not in video_url and "vm.tiktok.com" not in video_url:
        return "This app is for TikTok links only", ""
    
    # Download video
    video_path = download_video(video_url)
    if not video_path:
        return "Failed to download video", ""
    
    # Extract audio
    audio_path = extract_audio(video_path)
    if not audio_path:
        clean_temp_files()
        return "Failed to extract audio", ""
    
    # Transcribe
    transcription = transcribe_audio(audio_path)
    if not transcription:
        clean_temp_files()
        return "Failed to transcribe audio", ""
    
    # Classify
    category, confidence = classify_content(transcription)
    if not category:
        clean_temp_files()
        return transcription, "Failed to classify content"
    
    # Clean up
    clean_temp_files()
    
    # Format classification result
    classification_result = f"{category} (confidence: {confidence:.2f})"
    return transcription, classification_result

# Gradio interface
with gr.Blocks(title="TikTok Content Analyzer") as demo:
    gr.Markdown("""
    # 🎬 TikTok Content Analyzer
    Enter a TikTok video URL to get transcription and content classification
    """)
    
    with gr.Row():
        url_input = gr.Textbox(
            label="TikTok URL",
            placeholder="Enter TikTok video URL here..."
        )
    
    with gr.Row():
        transcription_output = gr.Textbox(
            label="Transcription",
            interactive=True,
            lines=10,
            max_lines=20
        )
    
    with gr.Row():
        category_output = gr.Textbox(
            label="Content Category",
            interactive=False
        )
    
    submit_btn = gr.Button("Analyze Video", variant="primary")
    
    # Examples
    gr.Examples(
        examples=[
            ["https://www.tiktok.com/@example/video/123456789"],
            ["https://vm.tiktok.com/ZMexample/"]
        ],
        inputs=url_input
    )
    
    submit_btn.click(
        fn=process_video,
        inputs=url_input,
        outputs=[transcription_output, category_output]
    )

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)