Add patient and clinician summaries to display after assessment completion.

app.py

@@ -293,6 +293,37 @@ def transcript_to_text(chat_history: List[Tuple[str, str]]) -> str:
     return "\n".join(lines)
 
 
+def build_patient_summary(chat_history: List[Tuple[str, str]], meta: Dict[str, Any], display_json: Dict[str, Any]) -> str:
+    severity = meta.get("Severity") or display_json.get("Severity")
+    total = meta.get("Total_Score") or display_json.get("Total_Score")
+    transcript_text = transcript_to_text(chat_history)
+    return (
+        f"### Summary for You\n\n"
+        f"Thank you for your time. Here is a copy of our conversation so you can review it later.\n\n"
+        f"```") + transcript_text + "\n```"
+    )
+
+
+def build_clinician_summary(chat_history: List[Tuple[str, str]], meta: Dict[str, Any], display_json: Dict[str, Any]) -> str:
+    scores = display_json.get("PHQ9_Scores", {})
+    confidences = display_json.get("Confidences", [])
+    severity = meta.get("Severity") or display_json.get("Severity")
+    total = meta.get("Total_Score") or display_json.get("Total_Score")
+    risk = display_json.get("High_Risk")
+    transcript_text = transcript_to_text(chat_history)
+    scores_lines = "\n".join([f"- {k}: {v}" for k, v in scores.items()])
+    conf_str = ", ".join([f"{c:.2f}" for c in confidences]) if confidences else ""
+    return (
+        f"### Clinician Summary\n\n"
+        f"- Severity: **{severity}**  \n"
+        f"- PHQ‑9 Total: **{total}**  \n"
+        f"- High Risk: **{risk}**\n\n"
+        f"#### Item Scores\n{scores_lines}\n\n"
+        f"#### Item Confidences\n{conf_str}\n\n"
+        f"#### Conversation Transcript\n\n"
+        f"```") + transcript_text + "\n```"
+    )
+
 def generate_recording_agent_reply(chat_history: List[Tuple[str, str]]) -> str:
     transcript = transcript_to_text(chat_history)
     system_prompt = (
@@ -688,6 +719,8 @@ def create_demo():
                     text_main = gr.Textbox(value="", visible=False)
                     # Autoplay clinician voice output (player hidden with CSS)
                     tts_audio_main = gr.Audio(label=None, interactive=False, autoplay=True, show_label=False, elem_id="tts-player")
+                    # Final summaries (shown after assessment ends)
+                    main_summary = gr.Markdown(visible=False)
 
             with gr.TabItem("Advanced"):
                 with gr.Column():
@@ -728,12 +761,18 @@ def create_demo():
                 new_path = synthesize_tts(chat_history[-1][1], provider=provider, coqui_model_name=coqui_model, coqui_speaker=coqui_speaker)
             else:
                 new_path = None
-            return chat_history, display_json, severity, finished_o, turns_o, None, None, new_path, new_path
+            # If finished, hide the mic and display summaries in Main
+            if finished_o:
+                patient_md = build_patient_summary(chat_history, {"Severity": severity, "Total_Score": display_json.get("Total_Score")}, display_json)
+                clinician_md = build_clinician_summary(chat_history, {"Severity": severity, "Total_Score": display_json.get("Total_Score")}, display_json)
+                summary_md = patient_md + "\n\n---\n\n" + clinician_md
+                return chat_history, display_json, severity, finished_o, turns_o, gr.update(visible=False), None, new_path, new_path, gr.update(value=summary_md, visible=True)
+            return chat_history, display_json, severity, finished_o, turns_o, None, None, new_path, new_path, gr.update(visible=False)
 
         audio_main.stop_recording(
             fn=_process_with_tts,
             inputs=[audio_main, text_main, chatbot, threshold, tts_enable, finished_state, turns_state, scores_state, meta_state, tts_provider_dd, coqui_model_tb, coqui_speaker_dd],
-            outputs=[chatbot, score_json, severity_label, finished_state, turns_state, audio_main, text_main, tts_audio, tts_audio_main],
+            outputs=[chatbot, score_json, severity_label, finished_state, turns_state, audio_main, text_main, tts_audio, tts_audio_main, main_summary],
             queue=True,
             api_name="message",
         )