# from transformers import pipeline
# import gradio as gr


# # Load the pipeline with the cache_dir parameter
# pipe = pipeline(model="tarteel-ai/whisper-base-ar-quran")

# def transcribe(audio):
#     text = pipe(audio)["text"]
#     return text

# iface = gr.Interface(
#     fn=transcribe,
#     inputs=gr.Audio(source="upload", type="filepath"),
#     outputs="text",
# )

# iface.launch()



# from transformers import pipeline

# model_id = "tarteel-ai/whisper-base-ar-quran"  # update with your model id
# pipe = pipeline("automatic-speech-recognition", model=model_id)

# def transcribe(filepath):
#     output = pipe(
#         filepath,
#         max_new_tokens=10000,
#     )
#     return output["text"]

# import gradio as gr

# iface = gr.Interface(
#     fn=transcribe,
#     inputs=gr.Audio(source="upload", type="filepath"),
#     outputs="text",
# )

# iface.launch()
import gradio as gr

gr.Interface.load("models/tarteel-ai/whisper-base-ar-quran").launch()