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import gradio as gr
import pytube
from transformers import pipeline
import os
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
with gr.Blocks(title="YouTube Viral Moment Analyzer") as demo:
    gr.Markdown("# YouTube Viral Moment Analyzer")
    
    with gr.Row():
        youtube_url = gr.Textbox(label="YouTube URL")
        
    with gr.Row():
        transcript_text = gr.Textbox(label="Transcript Text", lines=10)
    
    analyze_button = gr.Button("Analyze Content")
    output = gr.JSON(label="Analysis Results")
    
    analyze_button.click(
        fn=analyze_youtube_content,
        inputs=[youtube_url, transcript_text],
        outputs=output,
        api_name="analyze_content"
    )

# Launch the app with server name and port for proper deployment
if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)

	import gradio as gr
	import pytube
	from transformers import pipeline
	import os
	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
	with gr.Blocks(title="YouTube Viral Moment Analyzer") as demo:
	gr.Markdown("# YouTube Viral Moment Analyzer")

	with gr.Row():
	youtube_url = gr.Textbox(label="YouTube URL")

	with gr.Row():
	transcript_text = gr.Textbox(label="Transcript Text", lines=10)

	analyze_button = gr.Button("Analyze Content")
	output = gr.JSON(label="Analysis Results")

	analyze_button.click(
	fn=analyze_youtube_content,
	inputs=[youtube_url, transcript_text],
	outputs=output,
	api_name="analyze_content"
	)

	# Launch the app with server name and port for proper deployment
	if __name__ == "__main__":
	demo.launch(server_name="0.0.0.0", server_port=7860)