import transformers
import gradio as gr
import librosa
import torch
import spaces
import numpy as np

@spaces.GPU(duration=60)
def transcribe_and_respond(audio_file):
    try:
        pipe = transformers.pipeline(
            model='sarvamai/shuka_v1',
            trust_remote_code=True,
            device=0,
            torch_dtype=torch.bfloat16
        )

        # Load the audio file at 16kHz
        audio, sr = librosa.load(audio_file, sr=16000)

        # Print audio properties for debugging
        print(f"Audio dtype: {audio.dtype}, Audio shape: {audio.shape}, Sample rate: {sr}")

        turns = [
            {'role': 'system', 'content': 'Share the Key Take Aways and Action Steps'},
            {'role': 'user', 'content': '<|audio|>'}
        ]

        # Debug: Print the initial turns
        print(f"Initial turns: {turns}")

        # Call the model with the audio and prompt
        output = pipe({'audio': audio, 'turns': turns, 'sampling_rate': sr}, max_new_tokens=10000)

        # Debug: Print the final output from the model
        print(f"Model output: {output}")

        return output

    except Exception as e:
        return f"Error: {str(e)}"

iface = gr.Interface(
    fn=transcribe_and_respond,
    # Allow both file upload and microphone input by providing a list of sources.
    inputs=gr.Audio(sources=["upload", "microphone"], type="filepath"),
    outputs=[
    gr.Textbox(label="Transcript"),
    gr.File(label="Download Transcript")],
    title="ShukaNotesApp",
    description="Meeting Notes for Indian Offices Discussions. \n Enter your audio from meetings here and the model will share the key takeaways.",
    live=True
)

if __name__ == "__main__":
    iface.launch()