Update app.py

app.py

@@ -1,37 +1,36 @@
+#Imports-------------------------------------------------------------
 import gradio as gr
 import subprocess
 import torch
+from transformers import pipeline
+
+#User defined functions (UDF)
 from functions.charts import spider_chart
 from functions.dictionaries import calculate_average, transform_dict
 from functions.icon import generate_icon
-from transformers import pipeline
 from functions.timestamp import format_timestamp
 from functions.youtube import get_youtube_video_id
+#---------------------------------------------------------------------
 
 MODEL_NAME = "openai/whisper-medium"
 BATCH_SIZE = 8
-
 device = 0 if torch.cuda.is_available() else "cpu"
 
+#Transformers pipeline
 pipe = pipeline(
     task="automatic-speech-recognition",
     model=MODEL_NAME,
     chunk_length_s=30,
-    device=device,
+    device=device
 )
 
-#Formating
+#Formating---------------------------------------------------------------------------------------------
 title = "Whisper Demo: Transcribe Audio"
 MODEL_NAME1 = "jpdiazpardo/whisper-tiny-metal"
 description = ("Transcribe long-form audio inputs with the click of a button! Demo uses the"
         f" checkpoint [{MODEL_NAME1}](https://huggingface.co/{MODEL_NAME1}) and 🤗 Transformers to transcribe audio files"
         " of arbitrary length. Check some of the 'cool' examples below")
 
-examples = [["https://www.youtube.com/watch?v=W72Lnz1n-jw&ab_channel=Whitechapel-Topic",None,None,
-            "examples/When a Demon Defiles a Witch.wav",True, True],
-            ["https://www.youtube.com/watch?v=BnO3Io0KOl4&ab_channel=MotionlessInWhite-Topic",None,None,
-             "examples/Immaculate Misconception.wav",True, True]]
-
 linkedin = generate_icon("linkedin")
 github = generate_icon("github")
 article = ("<div style='text-align: center; max-width:800px; margin:10px auto;'>"
@@ -45,6 +44,7 @@ title = "Scream: Fine-Tuned Whisper model for automatic gutural speech recogniti
 #Define classifier for sentiment analysis
 classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=None)
 
+#Functions-----------------------------------------------------------------------------------------------------------------------
 def transcribe(*args):#file, return_timestamps, *kwargs):
     '''inputs: file, return_timestamps'''
     outputs = pipe(args[3], batch_size=BATCH_SIZE, generate_kwargs={"task": 'transcribe'}, return_timestamps=True)
@@ -70,15 +70,6 @@ def transcribe(*args):#file, return_timestamps, *kwargs):
     
     return args[3], text, fig, av_dict
 
-def filter(choice):
-    if choice=="YouTube":
-        return yt_link.update(interactive=True), audio_input.update(interactive=False)
-    elif choice == "Upload File":
-        return yt_link.update(value=None,interactive=False), audio_input.update(interactive=True)
-    else:
-        return yt_link.update(interactive=False), audio_input.update(interactive=False)
-
-
 embed_html = '<iframe src="https://www.youtube.com/embed/YOUTUBE_ID'\
               'title="YouTube video player" frameborder="0" allow="accelerometer;'\
               'autoplay; clipboard-write; encrypted-media; gyroscope;'\
@@ -93,7 +84,11 @@ def hide_sa(value):
     return sa_plot.update(visible=True), sa_frequency.update(visible=True)
   else:
     return sa_plot.update(visible=False), sa_frequency.update(visible=False)
-  
+
+#----------------------------------------------------------------------------------------------------------------------------------------------
+      
+#Components------------------------------------------------------------------------------------------------------------------------------------      
+      
 #Input components        
 yt_link = gr.Textbox(value=None,label="YouTube link", info = "Optional: Copy and paste YouTube URL")
 audio_input = gr.Audio(source="upload", type="filepath", label="Upload audio file for transcription")
@@ -115,6 +110,10 @@ sa_frequency = gr.Label(label="Frequency")
 
 outputs = [audio_out, gr.outputs.HTML("text"), sa_plot, sa_frequency]
 
+#----------------------------------------------------------------------------------------------------------------------------------------------------
+
+#Launch demo-----------------------------------------------------------------------------------------------------------------------------------------
+
 with gr.Blocks() as demo:
   download_button.click(download, inputs=[yt_link], outputs=[thumbnail])
   sa_checkbox.change(hide_sa, inputs=[sa_checkbox], outputs=[sa_plot, sa_frequency])
@@ -125,6 +124,8 @@ with gr.Blocks() as demo:
                    description=description, cache_examples=True, allow_flagging="never", article = article , examples='examples')
             
 demo.queue(concurrency_count=3)
-demo.launch(debug = True)
 
+if __name__ == "__main__":
+    demo.launch(debug = True)
 
+#----------------------------------------------------------------------------------------------------------------------------------------------------