Update app.py

app.py

@@ -57,59 +57,53 @@ def hello2():
 # Route for uploading video files
 @app.route('/upload_video', methods=['POST'])
 def upload_video():
-    try:
-        start_time = time.time()
-        if 'video' not in request.files:
-            return jsonify({'error': 'No video file found in the request'})
-
-        video = request.files['video']
-        if video.filename == '':
-            return jsonify({'error': 'No selected video file'})
-
-        if video.mimetype.split('/')[0] != 'video':
-            return jsonify({'error': 'The file uploaded is not a video'})
-
-        model_name = request.form.get('modelName')
-        print("MODEL:", model_name)
-
-        # Save the uploaded video to a temporary directory
-        video_path = os.path.join(os.getcwd(), 'temp', secure_filename(video.filename))
-        video.save(video_path)
-
-        # Transcribe audio from the uploaded video
-        transcript = transcribe_audio(video_path)
-
-        # Save the video file to Hugging Face Spaces
-        video_file_id = hf_hub.upload(video_path, name=video.filename)
-
-        # Save the transcribed audio to a temporary WAV file
-        audio_path = os.path.join(os.getcwd(), 'temp', 'transcribed_audio.wav')
-        with open(audio_path, 'w') as audio_file:
-            audio_file.write(transcript)
-
-        # Save the audio file to Hugging Face Spaces
-        audio_file_id = hf_hub.upload(audio_path, name='transcribed_audio.wav')
-
-        # Summarize the transcribed text using the selected model
-        summary = ""
-        if model_name == 'T5':
-            summary = summarize_text_t5(transcript)
-        elif model_name == 'BART':
-            summary = summarize_text_bart(transcript)
-        else:
-            summary = summarizer(transcript)
-
-        # Calculate the elapsed time and print a success message
-        end_time = time.time()
-        elapsed_time = end_time - start_time
-        print(f"Video saved successfully. Time taken: {elapsed_time} seconds")
-
-        # Return the transcript, summary, model name, video file ID, and audio file ID in the response
-        return jsonify({'message': 'successful', 'transcript': transcript, 'summary': summary, 'modelName': model_name,
-                        'videoFileId': video_file_id, 'audioFileId': audio_file_id})
-    except Exception as e:
-        # If an error occurs during processing, return an error message
-        return jsonify({'error': str(e)})
+    start_time = time.time()
+    if 'video' not in request.files:
+        return jsonify({'error': 'No video file found in the request'})
+    video = request.files['video']
+    if video.mimetype.split('/')[0] != 'video':
+        return jsonify({'error': 'The file uploaded is not a video'})
+
+    model_name = request.form.get('modelName')
+    print("MODEL:", model_name)
+
+    video_path = os.path.join(os.getcwd(), secure_filename(video.filename))
+    video.save(video_path)
+
+    # Initialize HfApi and HfFolder
+    api = HfApi()
+    folder = HfFolder()
+
+    # Get your Hugging Face API token
+    token = folder.get_token()
+
+    # Specify your namespace (username or organization name)
+    namespace = "Dhrumit1314/notivai-backend"
+
+    # Upload the video file
+    video_file_id = api.upload_file(
+        token=token,
+        path=video_path,
+        filename=video.filename,
+        namespace=namespace
+    )
+
+    transcript = transcribe_audio(video_path)
+
+    summary = ""
+    if model_name == 'T5':
+        summary = summarize_text_t5(transcript)
+    elif model_name == 'BART':
+        summary = summarize_text_bart(transcript)
+    else:
+        summary = summarizer(transcript)
+
+    end_time = time.time()
+    elapsed_time = end_time - start_time
+    print(f"Video saved successfully. Time taken: {elapsed_time} seconds")
+
+    return jsonify({'message': 'successful', 'transcript': transcript, 'summary': summary, 'modelName': model_name, 'videoFileId': video_file_id})
+
         
 # def upload_video():
 #     start_time = time.time()