Update app.py

app.py

@@ -48,14 +48,14 @@ title = "Scream: Fine-Tuned Whisper model for automatic gutural speech recogniti
 classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=None)
 
 #Functions-----------------------------------------------------------------------------------------------------------------------
-def transcribe(*args):#file, return_timestamps, *kwargs):
+def transcribe(link,download,thumbnail,file,use_timestamps,sentiment_analysis):#file, return_timestamps, *kwargs):
     '''inputs: file, return_timestamps'''
-    outputs = pipe(args[3], batch_size=BATCH_SIZE, generate_kwargs={"task": 'transcribe'}, return_timestamps=True)
+    outputs = pipe(file, batch_size=BATCH_SIZE, generate_kwargs={"task": 'transcribe'}, return_timestamps=True)
     text = outputs["text"]
     timestamps = outputs["chunks"]
 
     #If return timestamps is True, return html text with timestamps format
-    if args[4]==True:
+    if use_timestamps==True:
       spider_text = [f"{chunk['text']}" for chunk in timestamps] #Text for spider chart without timestamps
       timestamps = [f"[{format_timestamp(chunk['timestamp'][0])} -> {format_timestamp(chunk['timestamp'][1])}] {chunk['text']}" for chunk in timestamps]
 
@@ -71,7 +71,7 @@ def transcribe(*args):#file, return_timestamps, *kwargs):
     av_dict = calculate_average(trans_dict)
     fig = spider_chart(av_dict)
     
-    return args[3], text, fig, av_dict
+    return file, text, fig, av_dict
 
 embed_html = '<iframe src="https://www.youtube.com/embed/YOUTUBE_ID'\
               'title="YouTube video player" frameborder="0" allow="accelerometer;'\