import gradio as gr

import torch
import librosa
import json
# Load model directly
from transformers import pipeline, AutoProcessor, AutoModelForSpeechSeq2Seq

pipe = pipeline("automatic-speech-recognition", model="dmatekenya/whisper-large-v3-chichewa")

def transcribe(audio_file_mic=None, audio_file_upload=None, language="English (eng)"):
    if audio_file_mic:
        audio_file = audio_file_mic
    elif audio_file_upload:
        audio_file = audio_file_upload
    else:
        return "Please upload an audio file or record one"

    # Make sure audio is 16kHz
    # speech, sample_rate = librosa.load(audio_file)
    # if sample_rate != 16000:
    #     speech = librosa.resample(speech, orig_sr=sample_rate, target_sr=16000)

    # Keep the same model in memory and simply switch out the language adapters by calling load_adapter() for the model and set_target_lang() for the tokenizer
    # language_code = iso_codes[language]
    # processor.tokenizer.set_target_lang(language_code)
    # model.load_adapter(language_code)

    result = pipe(audio_file)
    return result["text"]


description = ''''''

iface = gr.Interface(fn=transcribe,
                     inputs=[
                         gr.Audio(source="microphone", type="filepath", label="Record Audio"),
                         gr.Audio(source="upload", type="filepath", label="Upload Audio"),
                         ],
                     outputs=gr.Textbox(label="Transcription"),
                     description=description
                     )
iface.launch()