import streamlit as st
import requests

# Title of the app
st.title("Whisper API Transcription Service")
st.markdown("Upload an audio file (mp3, wav, or flac) and get transcription results.")

# Prompt the user for their Hugging Face API key
my_key = st.text_input('Enter your Hugging Face API Key', type='password')

# API URL for Whisper model
API_URL = "https://api-inference.huggingface.co/models/openai/whisper-large-v3-turbo"

# Set up the headers with the provided API key
headers = {"Authorization": f"Bearer {my_key}"}

# Function to send the file to the API and get the transcription result
def query(file_data):
    try:
        response = requests.post(API_URL, headers=headers, files={'file': file_data})
        return response.json()
    except requests.exceptions.RequestException as e:
        return {"error": str(e)}

# File uploader widget for audio files
uploaded_files = st.file_uploader("Choose an audio file", type=["mp3", "wav", "flac"], accept_multiple_files=True)

# Handle file uploads and process them if API key is provided
if my_key:  # Proceed only if the API key is provided
    if uploaded_files:
        results = {}
        for uploaded_file in uploaded_files:
            st.write(f"Processing file: {uploaded_file.name}")
            
            # Send the file to the Hugging Face API
            output = query(uploaded_file)
            
            # Store and display the result
            results[uploaded_file.name] = output

        # Show results for all files
        st.write("Results:")
        for file, result in results.items():
            st.write(f"**Results for {file}:**")
            st.json(result)
    else:
        st.write("Please upload an audio file to transcribe.")
else:
    st.write("Please enter your Hugging Face API key to proceed.")