Update app.py

app.py

@@ -1,34 +1,62 @@
 import gradio as gr
 from textblob import TextBlob
 
-def sentiment_analysis(text: str) -> dict:
-    """
-    Analyze the sentiment of the given text.
+import yt_dlp 
+import os 
 
+def download_video(url, request):
+    """downlad video and audio from youtube url
     Args:
-        text (str): The text to analyze
-
+        url (str): youtube video url
+        output_path (str): path to save the downloaded files
     Returns:
-        dict: A dictionary containing polarity, subjectivity, and assessment
+        video_filename (str): path to the downloaded video file
+        audio_filename (str): path to the downloaded audio file
     """
-    blob = TextBlob(text)
-    sentiment = blob.sentiment
-    
-    return {
-        "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"
+
+    # instanciate output path
+    output_path='./cache'
+    if not os.path.exists(output_path):
+        os.mkdir(output_path)
+
+    # get video
+    ydl_opts_video = {
+        'format': 'worst[ext=mp4]',
+        'outtmpl': output_path+'/video/'+'%(title)s_video.%(ext)s',
+        'quiet': True
     }
+    print('Downloading video...')
+    with yt_dlp.YoutubeDL(ydl_opts_video) as ydl:
+        info_dict = ydl.extract_info(url, download=True)
+        video_filename = ydl.prepare_filename(info_dict)
 
+    # get audio
+    audio_opts = {
+        'format': 'bestaudio[ext=m4a]',
+        'outtmpl': output_path+'/audio/'+'%(title)s.audio.%(ext)s',
+        'quiet': False,
+        'noplaylist': True,
+    }
+    print('Downloading audio...')
+    with yt_dlp.YoutubeDL(audio_opts) as ydl:
+        info_dict = ydl.extract_info(url, download=True)
+        audio_filename = ydl.prepare_filename(info_dict)
+
+        
+    return {
+        "video_path": video_filename,
+        "audio_path": audio_filename,
+        "request": request
+    }
 # Create the Gradio interface
 demo = gr.Interface(
-    fn=sentiment_analysis,
-    inputs=gr.Textbox(placeholder="Enter text to analyze..."),
+    fn=download_video, # will use function arguments to define inputs names
+    inputs=[gr.Textbox(placeholder="Enter video url"),gr.Textbox(placeholder="Enter request")],
     outputs=gr.JSON(),
-    title="Text Sentiment Analysis",
-    description="Analyze the sentiment of text using TextBlob"
+    title="Video information request",
+    description="Retreive user's request information from a video"
 )
 
 # Launch the interface and MCP server
 if __name__ == "__main__":
-    demo.launch(mcp_server=True)
+    demo.launch()