Create app.py

app.py

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+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