Update app.py

app.py

@@ -12,9 +12,9 @@ from functions.icon import generate_icon
 from functions.timestamp import format_timestamp
 from functions.youtube import get_youtube_video_id
 #---------------------------------------------------------------------
-examples = [[#"When a Demon Defiles a Witch.wav", #"https://www.youtube.com/watch?v=W72Lnz1n-jw&ab_channel=Whitechapel-Topic",
-#"When a Demon Defiles a Witch.wav", #"<iframe src='https://www.youtube.com/embed/W72Lnz1n-jw' title='YouTube video player' frameborder='0' allow='accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture' allowfullscreen></iframe>",
-            "When a Demon Defiles a Witch.wav",True]]#, True]]
+examples = [["https://www.youtube.com/watch?v=W72Lnz1n-jw&ab_channel=Whitechapel-Topic",
+"<iframe src='https://www.youtube.com/embed/W72Lnz1n-jw' title='YouTube video player' frameborder='0' allow='accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture' allowfullscreen></iframe>",
+"When a Demon Defiles a Witch.wav",True, True]]
 
 MODEL_NAME = "openai/whisper-medium"
 BATCH_SIZE = 8
@@ -49,7 +49,7 @@ 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(file,use_timestamps):#,sentiment_analysis):#file, return_timestamps, *kwargs):
+def transcribe(link,thumbnail,file,use_timestamps,sentiment_analysis):#file, return_timestamps, *kwargs):
     '''inputs: file, return_timestamps'''
     outputs = pipe(file, batch_size=BATCH_SIZE, generate_kwargs={"task": 'transcribe'}, return_timestamps=True)
     text = outputs["text"]
@@ -100,12 +100,12 @@ download_button = gr.Button(value="Download")
 thumbnail = gr.HTML(value=embed_html, visible=False, label = "Thumbnail")
 sa_checkbox = gr.Checkbox(value=True, label="Sentiment analysis")
 
-inputs = [#yt_link, #0
+inputs = [yt_link, #0
           #download_button, #1
-          #thumbnail, #2
+          thumbnail, #2
           audio_input, #3
           gr.Checkbox(value=True, label="Return timestamps")]#, #4
-          #sa_checkbox] #5
+          sa_checkbox] #5
 
 #Ouput components
 audio_out = gr.Audio(label="Processed Audio", type="filepath", info = "Vocals only")