import gradio as gr
import torch
from transformers import pipeline

# Load models
transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=0 if torch.cuda.is_available() else -1)
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

# Function to process audio
def process_audio(audio_file):
    # Step 1: Transcribe audio
    transcription = transcriber(audio_file)["text"]
    
    # Step 2: Summarize transcription
    summary = summarizer(transcription, max_length=50, min_length=10, do_sample=False)[0]["summary_text"]
    
    return transcription, summary

# Gradio Interface with Horizontal Layout
with gr.Blocks() as interface:
    with gr.Row():
        # Upload button on the left
        with gr.Column():
            audio_input = gr.Audio(type="filepath", label="Upload Audio File")
            process_button = gr.Button("Process Audio")
        # Output text box on the right
        with gr.Column():
            transcription_output = gr.Textbox(label="Full Transcription", lines=10)
            summary_output = gr.Textbox(label="Summary", lines=5)

    # Link the button to the function
    process_button.click(
        process_audio,
        inputs=[audio_input],
        outputs=[transcription_output, summary_output]
    )

# Launch the interface with SSR disabled and optional public sharing
interface.launch