app.py

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

# Page config
st.set_page_config(page_title="Atma.ai - Advanced Session Summarizer", layout="centered")

st.title("🧠 Atma.ai – Advanced Mental Health Session Summarizer")
st.markdown("Upload a recorded therapy session to get a structured summary and emotional tone analysis. Now enhanced with dialogue-aware summarization!")

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

if uploaded_file:
    st.audio(uploaded_file)

    # Convert audio to required format
    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")

    # Transcribe
    st.info("🔄 Transcribing with Whisper...")
    asr = pipeline("automatic-speech-recognition", model="openai/whisper-small")
    result = asr(audio_path, return_timestamps=True)
    transcript = result["text"]

    st.subheader("📝 Transcript")
    st.markdown(transcript)

    # Dialogue-aware summarization using SAMSum-tuned model
    st.info("📋 Summarizing conversation contextually...")
    summarizer = pipeline("summarization", model="philschmid/bart-large-cnn-samsum")
    summary = summarizer(transcript, max_length=256, min_length=60, do_sample=False)

    st.subheader("📌 Summary")
    st.write(summary[0]["summary_text"])

    # Emotion tagging
    st.info("🎭 Extracting emotional tones...")
    emotion_model = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
    emotion_scores = emotion_model(transcript)

    st.subheader("💬 Emotional Insights (Overall)")
    for emo in emotion_scores[0]:
        st.write(f"{emo['label']}: {round(emo['score']*100, 2)}%")

    os.remove(audio_path)