import gradio as gr

demo = gr.load("models/vrclc/Malasar_medium_MTF",
               examples = [["./luke1.1.wav", "transcribe"],
                          ],
               title = "VRCLC Malasar Speech Recognition Demo",
               description=("Transcribe microphone or audio inputs with the click of a button!"),
               cache_examples=False
              )

demo.launch()
# import gradio as gr
# import torch
# import soundfile as sf
# from transformers import pipeline

# device = "cuda:0" if torch.cuda.is_available() else "cpu"
# pipe = pipeline(
#     "automatic-speech-recognition",
#     model="vrclc/Malasar_medium_MTF",
#     chunk_length_s=10,
#     device=device,
# )

# def transcribe(audio):
#     """Transcribes Malasar speech from an audio file."""
#     try:
#         if audio is None:
#             return "Please record or upload an audio file."

#         print(f"[DEBUG] Received audio: {audio}")

#         # Handle filepath case from Gradio
#         audio_path = audio if isinstance(audio, str) else audio.get("name", None)
#         if audio_path is None:
#             return "Could not read audio file."

#         print(f"[DEBUG] Reading audio file: {audio_path}")
#         audio_data, sample_rate = sf.read(audio_path)

#         print(f"[DEBUG] Audio sample rate: {sample_rate}, shape: {audio_data.shape}")

#         transcription = pipe(
#             {"array": audio_data, "sampling_rate": sample_rate},
#             chunk_length_s=10,
#             batch_size=8,
#         )["text"]

#         print(f"[DEBUG] Transcription: {transcription}")
#         return transcription

#     except Exception as e:
#         import traceback
#         print("[ERROR] Exception during transcription:")
#         traceback.print_exc()
#         return f"Error: {str(e)}"

# iface = gr.Interface(
#     fn=transcribe,
#     inputs=gr.Audio(sources=["microphone", "upload"], type="filepath"),
#     outputs="text",
#     title="Malasar Speech Recognition",
#     description="Record or upload Malasar speech and submit to get the transcribed text.",
#     examples=[["luke1.1.wav"]],
# )
# iface.launch()