Update app.py

app.py

@@ -4,12 +4,16 @@ from transformers import pipeline
 from pydub import AudioSegment
 import os
 import re
+from docx import Document
+from docx.shared import Pt
+from docx.enum.text import WD_PARAGRAPH_ALIGNMENT
+from datetime import datetime
 
 # Page config
-st.set_page_config(page_title="Atma.ai - Advanced Session Summarizer", layout="wide")
+st.set_page_config(page_title="Atma.ai - Session Summarizer + Export", layout="wide")
 
 st.title("🧠 Atma.ai – Advanced Mental Health Session Summarizer")
-st.markdown("Upload a therapy session audio file (Tamil-English mix). The app provides a clean transcript with simulated speaker turns, a contextual summary, and emotional analysis.")
+st.markdown("Upload a therapy session audio (Tamil-English mix) to view the transcript, summary, emotional analysis, and export everything to Word!")
 
 # Upload audio
 uploaded_file = st.file_uploader("🎙️ Upload audio file", type=["wav", "mp3", "m4a"])
@@ -24,7 +28,7 @@ if uploaded_file:
     audio.export(audio_path, format="wav")
 
     try:
-        # Transcribe with explicit language forcing
+        # Transcribe
         st.info("🔄 Transcribing with Whisper (mixed-language support)...")
         asr = pipeline("automatic-speech-recognition", model="openai/whisper-large")
         result = asr(audio_path, return_timestamps=True, generate_kwargs={"language": "<|en|>"})
@@ -33,13 +37,13 @@ if uploaded_file:
         if not raw_transcript:
             st.error("❌ Could not generate a transcript. Please try a different audio.")
         else:
-            # Simulated Speaker Diarization by sentence splitting
+            # Simulated Speaker Diarization
             st.info("🗣️ Simulating speaker separation...")
             sentences = re.split(r'(?<=[.?!])\s+', raw_transcript)
             diarized_transcript = ""
             for idx, sentence in enumerate(sentences):
                 speaker = "Speaker 1" if idx % 2 == 0 else "Speaker 2"
-                diarized_transcript += f"**{speaker}:** {sentence}\n\n"
+                diarized_transcript += f"{speaker}: {sentence}\n\n"
 
             # Summarization
             st.info("📋 Summarizing conversation...")
@@ -66,6 +70,55 @@ if uploaded_file:
                 st.subheader("💬 Emotional Insights (Overall)")
                 for emo in emotion_scores[0]:
                     st.write(f"{emo['label']}: {round(emo['score']*100, 2)}%")
+
+            # Export Button
+            st.subheader("📥 Export Session Report")
+
+            def generate_docx(transcript, summary_text, emotions):
+                doc = Document()
+
+                # Title
+                title = doc.add_heading('Session Summary - Atma.ai', 0)
+                title.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
+
+                # Date
+                date_paragraph = doc.add_paragraph(f"Date: {datetime.now().strftime('%Y-%m-%d')}")
+                date_paragraph.runs[0].italic = True
+
+                doc.add_paragraph("\n")
+
+                # Transcript
+                doc.add_heading('📝 Transcript', level=1)
+                transcript_para = doc.add_paragraph(transcript)
+                transcript_para.runs[0].font.size = Pt(12)
+
+                doc.add_paragraph("\n")
+
+                # Summary
+                doc.add_heading('📋 Summary', level=1)
+                summary_para = doc.add_paragraph(summary_text)
+                summary_para.runs[0].font.size = Pt(12)
+
+                doc.add_paragraph("\n")
+
+                # Emotional Insights
+                doc.add_heading('💬 Emotional Insights', level=1)
+                for emo in emotions[0]:
+                    emotion_para = doc.add_paragraph(f"{emo['label']}: {round(emo['score']*100, 2)}%")
+                    emotion_para.runs[0].font.size = Pt(12)
+
+                # Footer
+                doc.add_paragraph("\n\n---\nGenerated by Atma.ai – Confidential", style="Intense Quote")
+
+                output_path = "session_summary.docx"
+                doc.save(output_path)
+                return output_path
+
+            if st.button("Generate and Download Report (.docx)"):
+                output_file = generate_docx(diarized_transcript, summary[0]["summary_text"], emotion_scores)
+                with open(output_file, "rb") as f:
+                    st.download_button(label="📥 Download Report", data=f, file_name="session_summary.docx", mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document")
+
     except Exception as err:
         st.error(f"❌ Processing failed: {err}")
     finally: