# import streamlit as st
# import whisper
# from tempfile import NamedTemporaryFile
# import ffmpeg


# st.title("MinuteBot App")

# # upload audio file with streamlit
# audio_file = st.file_uploader("Unggah Meeting Audio", type=["mp3", "wav", "m4a"])

# # model = whisper.load_model("base") # loading the base model
# st.text("MinuteBot Model telah dimuat:")

# def load_whisper_model():

#     return model

    
# if st.sidebar.button("Transkripsikan Audio"):
#     if audio_file is not None:
#         with NamedTemporaryFile() as temp:
#             temp.write(audio_file.getvalue())
#             temp.seek(0)
#             model = whisper.load_model("large")
#             result = model.transcribe(temp.name)
#             st.write(result["text"])
        
# st.sidebar.header("Putar Berkas Audio")
# st.sidebar.audio(audio_file)

import streamlit as st
from tempfile import NamedTemporaryFile
import ffmpeg
from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
import librosa

st.title("TemplarX-Medium-Indonesian Transcription App")
st.text("Model Whisper (TemplarX-medium-Indonesian) telah dimuat:")

def load_whisper_model():
    model_name = "jonnatakusuma/TemplarX-medium-Indonesian"
    tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name)
    model = Wav2Vec2ForCTC.from_pretrained(model_name)
    return tokenizer, model

audio_file = st.file_uploader("Unggah Meeting Audio", type=["mp3", "wav", "m4a"])

if st.sidebar.button("Transkripsikan Audio"):
    if audio_file is not None:
        with NamedTemporaryFile() as temp:
            temp.write(audio_file.read())
            temp.seek(0)
            tokenizer, model = load_whisper_model()
            # Read the audio file and transcribe using the fine-tuned model
            audio_path = temp.name
            audio_input, _ = librosa.load(audio_path, sr=16000)
            transcription = model.stt(text)
            st.write(transcription)

st.sidebar.header("Putar Berkas Audio")
st.sidebar.audio(audio_file, format='audio/wav')