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app.py

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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)