Update app.py

app.py

@@ -1,4 +1,4 @@
-# Clean Imports
+# Import Libraries
 import os
 import gc
 import torch
@@ -28,7 +28,7 @@ def setup_cookies():
 # Call cookie setup when app starts
 setup_cookies()
 
-# Hugging Face Login (optional - uses environment variable)
+# Hugging Face Login Setup
 hf_token = os.getenv('HF_TOKEN')
 if hf_token:
     login(hf_token, add_to_git_credential=True)
@@ -93,7 +93,7 @@ def transcription_whisper(source):
     torch.cuda.empty_cache()
     return formatted_output, result
 
-# System Prompts
+# Prompts
 system_prompt = """
 You are an expert assistant that generates clear, concise, and well-structured
 Minutes of Meeting (MOM) documents from raw meeting transcripts.
@@ -143,7 +143,7 @@ quant_config = BitsAndBytesConfig(
     bnb_4bit_quant_type='nf4'
 )
 
-# Generate MOM
+# Generate MOM / Summarization 
 def generate(model_name, source):
     messages = messages_for(source)
     tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
@@ -177,7 +177,7 @@ def generate(model_name, source):
     
     yield mom_output.strip()
 
-# Translation Functions
+# Translation Functions : Valid Language or Not
 def valid_language(lang):
     return bool(
         pycountry.languages.get(name=lang.capitalize()) or
@@ -185,6 +185,7 @@ def valid_language(lang):
         pycountry.languages.get(alpha_3=lang.lower())
     )
 
+# Translate Prompts
 system_prompt_translate = "You are a translation assistant. Given a target language and some content, translate the content accurately into that language, preserving meaning, tone, and style, and return only the translated text. Also maintain proper format."
 
 def user_prompt_translate(source, lang):
@@ -306,7 +307,7 @@ def translate_transcribe_gemma(Gemma, source, lang):
     
     yield translate_output
 
-# Optimization Functions
+# Optimization Functions for MOM
 def optimize(model_name, source):
     if model_name == 'LLAMA':
         result = generate(LLAMA, source)
@@ -319,7 +320,8 @@ def optimize(model_name, source):
     
     for chunk in result:
         yield chunk
-
+        
+# Optimization Functions for Translation
 def optimize_translate(model_name, source, lang):
     if model_name == 'LLAMA':
         translate = translate_transcribe(LLAMA, source, lang)
@@ -335,7 +337,7 @@ def optimize_translate(model_name, source, lang):
     for chunk_tr in translate:
         yield chunk_tr
 
-# Helper function for file/link input
+# Helper Functon for Gradio UI
 def get_source_input(file, link):
     if file is not None:
         return file.name if hasattr(file, 'name') else file
@@ -409,7 +411,7 @@ with gr.Blocks(css=css) as ui:
         for result in optimize_translate(model, source, lang):
             yield result
     
-    # Event handlers with file/link support
+    # Event handlers with file or link support
     transcribe.click(
         fn=lambda file, link: transcription_whisper(get_source_input(file, link))[0],
         inputs=[input_file, input_link],
@@ -430,4 +432,4 @@ with gr.Blocks(css=css) as ui:
 
 # Launch the app
 if __name__ == "__main__":
-    ui.launch(server_name="0.0.0.0", server_port=7860)
+    ui.launch(server_name="0.0.0.0", server_port=7860)