import streamlit as st
import whisper

# Load the Whisper model
@st.cache_resource
def load_model():
    return whisper.load_model("turbo")

model = load_model()

# Streamlit app
st.title("Audio Transcription App")
st.header("Using Whisper for Audio Transcription")

# File uploader
uploaded_file = st.file_uploader("Upload an audio file (e.g., MP3, WAV, etc.)", type=["mp3", "wav", "m4a"])

if uploaded_file is not None:
    st.audio(uploaded_file, format="audio/mp3", start_time=0)

    # Transcribe button
    if st.button("Transcribe Audio"):
        with st.spinner("Transcribing..."):
            # Save the uploaded file to a temporary location
            with open("temp_audio_file.mp3", "wb") as temp_file:
                temp_file.write(uploaded_file.read())

            # Perform transcription
            result = model.transcribe("temp_audio_file.mp3")
            transcription_text = result["text"]

            st.success("Transcription Completed!")
            st.subheader("Transcription:")
            st.text_area("Here is the transcription:", transcription_text, height=300)
else:
    st.info("Please upload an audio file to start the transcription.")