App Code updated

app.py

@@ -84,84 +84,6 @@
 # if __name__ == '__main__' :
 #     demo.launch()
 
-# import requests
-# import gradio as gr
-# import tempfile
-# import os
-# from transformers import pipeline
-# from huggingface_hub import InferenceClient
-# import time
-# import torch
-
-# device = "cuda" if torch.cuda.is_available() else "cpu"
-
-
-# model_id = "openai/whisper-large-v3"
-# client = InferenceClient(model_id)
-# pipe = pipeline("automatic-speech-recognition", model=model_id, device=device)
-
-# # def transcribe(inputs, task):
-# #     if inputs is None:
-# #         raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
-
-# #     text = pipe(inputs, chunk_length_s=30)["text"]
-# #     return text
-
-# def transcribe(inputs, task):
-#     start = time.time()
-#     if inputs is None:
-#         raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
-
-#     try:
-  
-#         res = client.automatic_speech_recognition(inputs).text
-#         end = time.time() - start
-#         return res, end
-    
-#     except Exception as e:
-#         return fr'Error: {str(e)}'
-        
-
-# demo = gr.Blocks()
-
-# time_taken = gr.Textbox(label="Time taken", type="text")
-
-# mf_transcribe = gr.Interface(
-#     fn=transcribe,
-#     inputs=[
-#         gr.Audio(sources="microphone", type="filepath"),
-#         gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
-#     ],
-#     outputs=["text", time_taken],
-#     title="Whisper Large V3: Transcribe Audio",
-#     description=(
-#         "Transcribe long-form microphone or audio inputs with the click of a button!"
-#     ),
-#     allow_flagging="never",
-# )
-
-# file_transcribe = gr.Interface(
-#     fn=transcribe,
-#     inputs=[
-#         gr.Audio(sources="upload", type="filepath", label="Audio file"),
-#         gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
-#     ],
-#     outputs=["text", time_taken],
-#     title="Whisper Large V3: Transcribe Audio",
-#     description=(
-#         "Transcribe long-form microphone or audio inputs with the click of a button!"
-#     ),
-#     allow_flagging="never",
-# )
-
-
-
-# with demo:
-#     gr.TabbedInterface([mf_transcribe, file_transcribe], ["Microphone", "Audio file"])
-
-# if __name__ == "__main__":
-#     demo.queue().launch()
-
 import requests
 import gradio as gr
 import tempfile
@@ -185,9 +107,11 @@ def transcribe(inputs, task, use_api):
 
     try:
         if use_api:
+            print(f'Using API for transcription...')
             # Use InferenceClient (API) if checkbox is checked
             res = client.automatic_speech_recognition(inputs).text
         else:
+            print(f'Using local pipeline for transcription...')
             # Use local pipeline if checkbox is unchecked
             res = pipe(inputs, chunk_length_s=30)["text"]
         
@@ -197,15 +121,19 @@ def transcribe(inputs, task, use_api):
     except Exception as e:
         return fr'Error: {str(e)}', None
 
+def calculate_time_taken(start_time):
+    return time.time() - start_time
+
 demo = gr.Blocks()
 
 mf_transcribe = gr.Interface(
                 fn=transcribe,
                 inputs=[
                     gr.Audio(sources="microphone", type="filepath"),
-                    gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
+                    # gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
+                    gr.Checkbox(label="Use API", value=False)
                 ],
-                outputs=["text", "text"],  # Placeholder for transcribed text and time taken
+                outputs=["text",gr.Textbox(label="Time taken", type="text")],  # Placeholder for transcribed text and time taken
                 title="Whisper Large V3: Transcribe Audio",
                 description=(
                     "Transcribe long-form microphone or audio inputs with the click of a button!"
@@ -217,9 +145,10 @@ file_transcribe = gr.Interface(
                 fn=transcribe,
                 inputs=[
                     gr.Audio(sources="upload", type="filepath", label="Audio file"),
-                    gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
+                    # gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
+                    gr.Checkbox(label="Use API", value=False)  # Checkbox for API usage
                 ],
-                outputs=["text", "text"],  # Placeholder for transcribed text and time taken
+                outputs=["text",gr.Textbox(label="Time taken", type="text")],  # Placeholder for transcribed text and time taken
                 title="Whisper Large V3: Transcribe Audio",
                 description=(
                     "Transcribe long-form microphone or audio inputs with the click of a button!"
@@ -231,12 +160,11 @@ with demo:
     with gr.Row():
     # with gr.Column():
         # Group the tabs for microphone and file-based transcriptions
-        gr.TabbedInterface([mf_transcribe, file_transcribe], ["Microphone", "Audio file"])
-
-        with gr.Column():
-            use_api_checkbox = gr.Checkbox(label="Use API", value=False)  # Checkbox outside
-            time_taken = gr.Textbox(label="Time taken", type="text")  # Time taken outside the interfaces
+        tab = gr.TabbedInterface([mf_transcribe, file_transcribe], ["Microphone", "Audio file"])
 
+        # with gr.Column():
+        #     use_api_checkbox = gr.Checkbox(label="Use API", value=False)  # Checkbox outside
+        #     # time_taken = gr.Textbox(label="Time taken", type="text")  # Time taken outside the interfaces
 
 if __name__ == "__main__":
-    demo.queue().launch()
+    demo.queue().launch()