import gradio as gr
import pytube
from transformers import pipeline
from textblob import TextBlob

# Initialize sentiment analysis pipeline
sentiment_analyzer = pipeline("sentiment-analysis")

def analyze_youtube_content(youtube_url, transcript_text=""):
    """Main function to analyze YouTube content"""
    results = {}
    
    # If URL is provided, get video info
    if youtube_url:
        try:
            # Create a YouTube object
            yt = pytube.YouTube(youtube_url)
            results["video_info"] = {
                "title": yt.title,
                "status": "success"
            }
        except Exception as e:
            results["video_info"] = {
                "status": "error",
                "message": str(e)
            }
    
    # If transcript is provided, analyze it
    if transcript_text:
        # Analyze sentiment with TextBlob
        blob = TextBlob(transcript_text)
        textblob_sentiment = blob.sentiment
        
        # Analyze sentiment with Hugging Face
        hf_result = sentiment_analyzer(transcript_text[:512])[0]
        
        results["sentiment"] = {
            "textblob": {
                "polarity": round(textblob_sentiment.polarity, 2),
                "assessment": "positive" if textblob_sentiment.polarity > 0 else "negative" if textblob_sentiment.polarity < 0 else "neutral"
            },
            "huggingface": {
                "label": hf_result["label"],
                "score": round(hf_result["score"], 4)
            }
        }
        
        # Identify key moments based on sentiment
        sentences = [str(sentence) for sentence in blob.sentences]
        key_moments = []
        
        for i, sentence in enumerate(sentences):
            sentiment = TextBlob(sentence).sentiment.polarity
            if abs(sentiment) > 0.5:
                key_moments.append({
                    "text": sentence,
                    "sentiment": sentiment
                })
        
        results["key_moments"] = key_moments[:5]  # Top 5 moments
    
    return results

# Create Gradio interface
demo = gr.Interface(
    fn=analyze_youtube_content,
    inputs=[
        gr.Textbox(label="YouTube URL"),
        gr.Textbox(label="Transcript Text", lines=10)
    ],
    outputs=gr.JSON(label="Analysis Results"),
    title="YouTube Viral Moment Analyzer",
    description="Analyze viral moments from YouTube videos using ML models"
)

# Launch the app with MCP server enabled
if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", share=True, mcp_server=True)