Update app.py

app.py

@@ -1,66 +1,34 @@
-import os
-import tempfile
 import gradio as gr
-from pytube import YouTube
-from moviepy import VideoFileClip
-import whisper
 from textblob import TextBlob
 
-# Step 1: Transcribe video
-def transcribe_video_from_url(url: str) -> str:
-    with tempfile.TemporaryDirectory() as tmpdir:
-        video_path = os.path.join(tmpdir, "video.mp4")
-        audio_path = os.path.join(tmpdir, "audio.wav")
-
-        # Download the video
-        yt = YouTube(url)
-        stream = yt.streams.filter(file_extension='mp4', only_video=False).first()
-        stream.download(output_path=tmpdir, filename="video.mp4")
-
-        # Extract audio
-        video_clip = VideoFileClip(video_path)
-        video_clip.audio.write_audiofile(audio_path, logger=None)
-        video_clip.close()
+def sentiment_analysis(text: str) -> dict:
+    """
+    Analyze the sentiment of the given text.
 
-        # Transcribe with Whisper
-        model = whisper.load_model("base")
-        result = model.transcribe(audio_path)
-        return result["text"]
+    Args:
+        text (str): The text to analyze
 
-# Step 2: Analyze sentiment
-def sentiment_analysis(text: str) -> dict:
+    Returns:
+        dict: A dictionary containing polarity, subjectivity, and assessment
+    """
     blob = TextBlob(text)
     sentiment = blob.sentiment
+    
     return {
-        "polarity": round(sentiment.polarity, 2),
-        "subjectivity": round(sentiment.subjectivity, 2),
+        "polarity": round(sentiment.polarity, 2),  # -1 (negative) to 1 (positive)
+        "subjectivity": round(sentiment.subjectivity, 2),  # 0 (objective) to 1 (subjective)
         "assessment": "positive" if sentiment.polarity > 0 else "negative" if sentiment.polarity < 0 else "neutral"
     }
 
-# Step 3: Main function for Gradio
-def analyze_sentiment_from_video(url: str) -> dict:
-    """
-    Transcribe audio from a video URL and analyze sentiment.
-    """
-    transcription = transcribe_video_from_url(url)
-    sentiment = sentiment_analysis(transcription)
-    sentiment["transcription"] = transcription
-    return sentiment
-
-# Gradio interface
+# Create the Gradio interface
 demo = gr.Interface(
-    fn=analyze_sentiment_from_video,
-    inputs=gr.Textbox(label="YouTube Video URL"),
-    outputs={
-        "assessment": gr.Textbox(label="Sentiment"),
-        "polarity": gr.Number(label="Polarity"),
-        "subjectivity": gr.Number(label="Subjectivity"),
-        "transcription": gr.Textbox(label="Transcribed Text", lines=10),
-    },
-    title="🎥 Sentiment Analysis from YouTube Video",
-    description="Enter a YouTube video URL. The app will transcribe its audio and analyze the sentiment of the spoken content."
+    fn=sentiment_analysis,
+    inputs=gr.Textbox(placeholder="Enter text to analyze..."),
+    outputs=gr.JSON(),
+    title="Text Sentiment Analysis",
+    description="Analyze the sentiment of text using TextBlob"
 )
 
-# Launch for Hugging Face Space
+# Launch the interface and MCP server
 if __name__ == "__main__":
-    demo.launch(mcp_server=True)
+    demo.launch(mcp_server=True)