app.py

import streamlit as st
from transformers import pipeline
from pydub import AudioSegment
import os

st.title("🧠 Atma.ai – Mental Health Session Summarizer")

uploaded_file = st.file_uploader("Upload an audio file", type=["wav", "mp3", "m4a"])

if uploaded_file:
    st.audio(uploaded_file)
    
    # Save the uploaded file
    audio_path = "temp_audio.wav"
    audio = AudioSegment.from_file(uploaded_file)
    audio = audio.set_channels(1).set_frame_rate(16000)
    audio.export(audio_path, format="wav")

    st.write("✅ Audio converted. Starting transcription...")

    st.spinner("Transcribing with Whisper...")
    asr = pipeline("automatic-speech-recognition", model="openai/whisper-small")
    result = asr(audio_path)
    transcript = result["text"]

    st.subheader("Transcript")
    st.write(transcript)

    st.subheader("Summary")
    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
    summary = summarizer(transcript, max_length=200, min_length=40, do_sample=False)
    st.write(summary[0]["summary_text"])

    os.remove(audio_path)  # clean up temp file