uu

app.py

@@ -3,38 +3,55 @@ from pytube import YouTube
 import subprocess
 from huggingsound import SpeechRecognitionModel
 import torch
-import librosa
 from transformers import pipeline
 
 def process_video(video_url):
+    response = {
+        'status': 'Success',
+        'message': '',
+        'data': ''
+    }
+
     try:
-        # Download audio from YouTube
         yt = YouTube(video_url)
         audio_file = yt.streams.filter(only_audio=True, file_extension='mp4').first().download(filename='ytaudio.mp4')
-    except Exception as e:
-        return f"Error downloading audio from YouTube: {e}"
-
-    try:
-        # Convert to suitable format for speech recognition
         subprocess.run(['ffmpeg', '-i', 'ytaudio.mp4', '-acodec', 'pcm_s16le', '-ar', '16000', 'ytaudio.wav'], check=True)
-    except subprocess.CalledProcessError as e:
-        return f"Error converting audio file: {e}"
+    except Exception as e:
+        response['status'] = 'Error'
+        response['message'] = f'Failed to download and convert video: {str(e)}'
+        return response
 
     try:
-        # Speech Recognition
         device = "cuda" if torch.cuda.is_available() else "cpu"
         model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-english", device=device)
         transcription = model.transcribe(['ytaudio.wav'])[0]['transcription']
     except Exception as e:
-        return f"Error in speech recognition: {e}"
+        response['status'] = 'Error'
+        response['message'] = f'Failed during speech recognition: {str(e)}'
+        return response
 
     try:
-        # Summarize Transcription
         summarization = pipeline('summarization')
         summarized_text = summarization(transcription, max_length=130, min_length=30, do_sample=False)
-        return summarized_text[0]['summary_text']
+        response['data'] = summarized_text[0]['summary_text']
     except Exception as e:
-        return f"Error summarizing text: {e}"
+        response['status'] = 'Error'
+        response['message'] = f'Failed during summarization: {str(e)}'
+        return response
+
+    return response
+
+iface = gr.Interface(
+    fn=process_video,
+    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter YouTube Video URL Here..."),
+    outputs=[
+        gr.outputs.Textbox(label="Status"),
+        gr.outputs.Textbox(label="Message"),
+        gr.outputs.Textbox(label="Summary")
+    ],
+    title="YouTube Video Summarizer",
+    description="This tool extracts audio from a YouTube video, transcribes it, and provides a summary.",
+    enable_queue=True  # Enable request queuing
+)
 
-iface = gr.Interface(fn=process_video, inputs=gr.inputs.Textbox(lines=2, placeholder="Enter YouTube Video URL Here..."), outputs="text", title="YouTube Video Summarizer", description="This tool extracts audio from a YouTube video, transcribes it, and provides a summary.")
 iface.launch()