removed comments from app.py

app.py

@@ -5,14 +5,11 @@ import os
 
 os.environ["HF_HOME"] = "/app/.cache"
 
-# Model path for the fine-tuned Whisper model
 model_path = "jacktol/whisper-medium.en-fine-tuned-for-ATC-faster-whisper"
 
-# Initialize the Whisper model and OpenAI client
 whisper_model = WhisperModel(model_path, device="cpu", compute_type="float32")
 client = AsyncOpenAI()
 
-# System prompt for converting transcript to standard ATC syntax
 system_prompt = """Convert the provided transcript into standard pilot-ATC syntax without altering the content.
 Ensure that all runway and heading numbers are formatted correctly (e.g., '11L' for 'one one left'). Use standard
 aviation phraseology wherever applicable. Maintain the segmentation of the transcript as provided, but exclude the timestamps.
@@ -20,26 +17,21 @@ Based on the context and segmentation of each transmission, label it as either '
 response place a horizontal div with "---" and then line-break, and then add a H2 which says "Transcription", and then
 proceed with the transcription."""
 
-# Function to transcribe audio and return the concatenated transcript with segment info
 def transcribe_audio(file_path):
     segments, info = whisper_model.transcribe(file_path, beam_size=5)
     transcript = []
 
-    # Combine all segments with timestamps
     for segment in segments:
         transcript.append(f"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}")
 
     return '\n'.join(transcript).strip()
 
-# Start chat session
 @cl.on_chat_start
 async def on_chat_start():
     try:
-        # Initialize the session data
         if cl.user_session.get("transcription_counter") is None:
             cl.user_session.set("transcription_counter", 0)
         
-        # Full welcome message
         welcome_message = """
 ## Welcome to the **ATC Transcription Assistant**
 
@@ -73,7 +65,6 @@ To get started, upload the audio below.
 """
         await cl.Message(content=welcome_message).send()
 
-        # Prompt the user to upload an audio file
         files = await cl.AskFileMessage(
             content="",
             accept={
@@ -87,38 +78,30 @@ To get started, upload the audio below.
         if files:
             audio_file = files[0]
 
-            # Get the full segmented transcription with timestamps
             transcription = transcribe_audio(audio_file.path)
 
-            # Send the entire transcription to the LLM for ATC syntax processing
             msg = cl.Message(content="")
             await msg.send()
 
-            # Process the transcription via the LLM
             stream = await client.chat.completions.create(
                 messages=[
                     {"role": "system", "content": system_prompt},
                     {"role": "user", "content": transcription},
                 ],
                 stream=True,
-                model="gpt-4o",  # Use the appropriate model
+                model="gpt-4o",
                 temperature=0,
             )
 
-            # Stream the ATC-processed output
             async for part in stream:
                 token = part.choices[0].delta.content or ""
                 await msg.stream_token(token)
 
-            # Ensure the final token is sent and the message stream is complete
-            await msg.send()  # This will mark the end of the streaming process
+            await msg.send()
 
     except Exception as e:
-        # Log any errors that occur during session initialization
         print(f"Error during on_chat_start: {str(e)}")
 
-# Stop chat session cleanup
 @cl.on_stop
 async def on_chat_stop():
-    # Clean up any session data or resources here, if needed
     print("Session ended, resources cleaned up.")