Update app.py

app.py

@@ -29,118 +29,7 @@ qwen_model_name = "Qwen/Qwen2.5-3B-Instruct"
 qwen_tokenizer = AutoTokenizer.from_pretrained(qwen_model_name, trust_remote_code=True)
 qwen_model = AutoModelForCausalLM.from_pretrained(qwen_model_name, trust_remote_code=True).to(device)
 
-def download_audio_from_url(url):
-    try:
-        if "youtube.com" in url or "youtu.be" in url:
-            print("Processing YouTube URL...")
-            yt = YouTube(url)
-            audio_stream = yt.streams.filter(only_audio=True).first()
-            with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_file:
-                audio_stream.download(output_path=temp_file.name)
-                audio_bytes = open(temp_file.name, "rb").read()
-            os.unlink(temp_file.name)
-        elif "share" in url:
-            print("Processing shareable link...")
-            response = requests.get(url)
-            soup = BeautifulSoup(response.content, 'html.parser')
-            video_tag = soup.find('video')
-            if video_tag and 'src' in video_tag.attrs:
-                video_url = video_tag['src']
-                print(f"Extracted video URL: {video_url}")
-            else:
-                raise ValueError("Direct video URL not found in the shareable link.")
-            response = requests.get(video_url)
-            audio_bytes = response.content
-        else:
-            print(f"Downloading video from URL: {url}")
-            response = requests.get(url)
-            audio_bytes = response.content
-        
-        print(f"Successfully downloaded {len(audio_bytes)} bytes of data")
-        return audio_bytes
-    except Exception as e:
-        print(f"Error in download_audio_from_url: {str(e)}")
-        raise
-
-def transcribe_audio(audio_file):
-    try:
-        print("Loading audio file...")
-        audio = AudioSegment.from_file(audio_file)
-        audio = audio.set_channels(1).set_frame_rate(16000)
-        audio_array = torch.tensor(audio.get_array_of_samples()).float()
-        
-        print(f"Audio duration: {len(audio) / 1000:.2f} seconds")
-        print("Starting transcription...")
-        input_features = whisper_processor(audio_array, sampling_rate=16000, return_tensors="pt").input_features.to(device)
-        
-        # Create attention mask
-        attention_mask = torch.ones_like(input_features)
-        
-        # Generate with specific parameters
-        predicted_ids = whisper_model.generate(
-            input_features,
-            attention_mask=attention_mask,
-            language='en',
-            task='translate'
-        )
-        transcription = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True)
-        
-        print(f"Transcription complete. Length: {len(transcription[0])} characters")
-        if len(transcription[0]) < 10:
-            raise ValueError(f"Transcription too short: {transcription[0]}")
-        return transcription[0]
-    except Exception as e:
-        print(f"Error in transcribe_audio: {str(e)}")
-        raise
-
-def separate_speakers(transcription):
-    print("Starting speaker separation...")
-    prompt = f"""Analyze the following transcribed text and separate it into different speakers. Identify potential speaker changes based on context, content shifts, or dialogue patterns. Format the output as follows:
-
-1. Label speakers as "Speaker 1", "Speaker 2", etc.
-2. Start each speaker's text on a new line beginning with their label.
-3. Separate different speakers' contributions with a blank line.
-4. If the same speaker continues, do not insert a blank line or repeat the speaker label.
-
-Now, please process the following transcribed text:
-
-{transcription}
-"""
-    
-    inputs = qwen_tokenizer(prompt, return_tensors="pt").to(device)
-    with torch.no_grad():
-        outputs = qwen_model.generate(**inputs, max_new_tokens=4000)
-    result = qwen_tokenizer.decode(outputs[0], skip_special_tokens=True)
-    
-    # Extract the processed text (remove the instruction part)
-    processed_text = result.split("Now, please process the following transcribed text:")[-1].strip()
-    
-    print("Speaker separation complete.")
-    return processed_text
-
-def transcribe_video(url):
-    try:
-        print(f"Attempting to download audio from URL: {url}")
-        audio_bytes = download_audio_from_url(url)
-        print(f"Successfully downloaded {len(audio_bytes)} bytes of audio data")
-        
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio:
-            AudioSegment.from_file(io.BytesIO(audio_bytes)).export(temp_audio.name, format="wav")
-            transcript = transcribe_audio(temp_audio.name)
-        
-        os.unlink(temp_audio.name)
-        
-        if len(transcript) < 10:
-            raise ValueError("Transcription too short, possibly failed")
-        
-        print("Separating speakers...")
-        separated_transcript = separate_speakers(transcript)
-        
-        return separated_transcript
-    except Exception as e:
-        error_message = f"An error occurred: {str(e)}"
-        print(error_message)
-        return error_message
+# ... (keep all the existing functions as they are)
 
 app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
 
@@ -190,7 +79,7 @@ def update_transcription(n_clicks, url):
     if thread.is_alive():
         return "Transcription timed out after 10 minutes", {'display': 'none'}
 
-    transcript = thread.result if hasattr(thread, 'result') else "Transcription failed"
+    transcript = getattr(thread, 'result', "Transcription failed")
 
     if transcript and not transcript.startswith("An error occurred"):
         return dbc.Card([
@@ -217,5 +106,5 @@ def download_transcript(n_clicks, transcription_output):
 
 if __name__ == '__main__':
     print("Starting the Dash application...")
-    app.run(debug=True, host='0.0.0.0', port=7860)
+    app.run_server(debug=True, host='0.0.0.0', port=7860)
     print("Dash application has finished running.")