Update app.py

app.py

@@ -1,39 +1,36 @@
-
 import whisper
 import gradio as gr
-import pyperclip  # Add this to requirements.txt
 
 model = whisper.load_model("small")
 
 def transcribe(audio):
+    
+    #time.sleep(3)
+    # load audio and pad/trim it to fit 30 seconds
     audio = whisper.load_audio(audio)
     audio = whisper.pad_or_trim(audio)
 
+    # make log-Mel spectrogram and move to the same device as the model
     mel = whisper.log_mel_spectrogram(audio).to(model.device)
+
+    # detect the spoken language
     _, probs = model.detect_language(mel)
     print(f"Detected language: {max(probs, key=probs.get)}")
 
-    options = whisper.DecodingOptions(fp16=False)
+    # decode the audio
+    options = whisper.DecodingOptions(fp16 = False)
     result = whisper.decode(model, mel, options)
     return result.text
-
-def copy_to_clipboard(text):
-    pyperclip.copy(text)
-    return "Copied!"
-
-with gr.Blocks() as demo:
-    gr.Markdown("## Product Recommendations System Text")
     
-    audio_input = gr.Audio(source="microphone", type="filepath", label="Speak Here")
-    output_text = gr.Textbox(label="Transcribed Text", interactive=True)
-    copy_status = gr.Textbox(label="Copy Status", interactive=False)
-
-    with gr.Row():
-        transcribe_btn = gr.Button("Transcribe")
-        copy_btn = gr.Button("Copy Text")
-
-    transcribe_btn.click(transcribe, inputs=audio_input, outputs=output_text)
-    copy_btn.click(copy_to_clipboard, inputs=output_text, outputs=copy_status)
-
-demo.launch()
-
+    
+ 
+gr.Interface(
+    title = 'Product Recommendation System Text', 
+    fn=transcribe, 
+    inputs=[
+        gr.inputs.Audio(source="microphone", type="filepath")
+    ],
+    outputs=[
+        "textbox"
+    ],
+    live=True).launch()