Update src/ui_components_original.py

src/ui_components_original.py

@@ -61,7 +61,8 @@ def standalone_run_analysis(
     mode, existing_label,
     np_name, np_age, np_gender,
     w_loc, w_dur, pain, moist, infect, diabetic,
-    prev_tx, med_hist, meds, alls, notes, img_path
+    prev_tx, med_hist, meds, alls, notes, img_path,
+    manual_annotation=None
 ):
     """Runs in the ZeroGPU worker; returns HTML for the UI."""
     def _label_to_id(label: str):
@@ -183,6 +184,9 @@ def standalone_run_analysis(
             'additional_notes': notes
         }
 
+        # Include manual annotation (mask) in questionnaire for AI
+        if manual_annotation is not None:
+            q_for_ai['manual_mask'] = manual_annotation
         # Run AI
         analysis_result = wound_analyzer.analyze_wound(img_path, q_for_ai)
         if not analysis_result or not analysis_result.get("success"):
@@ -669,6 +673,9 @@ button.gr-button:hover, button.gr-button-primary:hover {
                                 with gr.Column(scale=1):
                                     gr.HTML("<h3>📸 Wound Image</h3>")
                                     wound_image = gr.Image(label="Upload Wound Image", type="filepath")
+                                    # Manual annotation field: user can draw wound boundary if model fails
+                                    gr.HTML("<h3>✏️ Manual Annotation (optional)</h3>")
+                                    manual_annotation = gr.Image(label="Draw wound boundary", tool="sketch", type="pil", interactive=True)
                                     gr.HTML("<h3>📝 Medical History</h3>")
                                     previous_treatment = gr.Textbox(label="Previous Treatment", lines=3)
                                     medical_history = gr.Textbox(label="Medical History", lines=3)
@@ -864,18 +871,34 @@ button.gr-button:hover, button.gr-button-primary:hover {
                 outputs=[new_patient_group, existing_patient_dd]
             )
 
+            # Keep manual annotation preview in sync with uploaded image
+            def _update_annotation_preview(path):
+                try:
+                    if not path:
+                        return None
+                    from PIL import Image
+                    return Image.open(path).convert("RGB")
+                except Exception:
+                    return None
+
+            wound_image.change(
+                _update_annotation_preview,
+                inputs=[wound_image],
+                outputs=[manual_annotation]
+            )
+
             # --- IMPORTANT: call standalone GPU function via lambda to pass instance/ctx ---
             analyze_btn.click(
-                fn=lambda mode, ex_lbl, np_n, np_a, np_g, wl, wd, p, m, i, d, pt, mh, med, al, nt, img: \
+                fn=lambda mode, ex_lbl, np_n, np_a, np_g, wl, wd, p, m, i, d, pt, mh, med, al, nt, img, annot: \
                     standalone_run_analysis(
                         self, self.current_user, self.database_manager, self.wound_analyzer,
-                        mode, ex_lbl, np_n, np_a, np_g, wl, wd, p, m, i, d, pt, mh, med, al, nt, img
+                        mode, ex_lbl, np_n, np_a, np_g, wl, wd, p, m, i, d, pt, mh, med, al, nt, img, annot
                     ),
                 inputs=[
                     patient_mode, existing_patient_dd,
                     new_patient_name, new_patient_age, new_patient_gender,
                     wound_location, wound_duration, pain_level, moisture_level, infection_signs, diabetic_status,
-                    previous_treatment, medical_history, medications, allergies, additional_notes, wound_image
+                    previous_treatment, medical_history, medications, allergies, additional_notes, wound_image, manual_annotation
                 ],
                 outputs=[analysis_output]
             )
@@ -900,6 +923,9 @@ button.gr-button:hover, button.gr-button-primary:hover {
             saved_image_path = analysis_result.get('saved_image_path', '')
 
             wound_type = visual_analysis.get('wound_type', 'Unknown')
+            skin_tone_label = visual_analysis.get('skin_tone_label', 'Unknown')
+            ita_degrees = visual_analysis.get('ita_degrees', 0)
+            tissue_type = visual_analysis.get('tissue_type', 'Unknown')
             length_cm = visual_analysis.get('length_cm', 0)
             breadth_cm = visual_analysis.get('breadth_cm', 0)
             area_cm2 = visual_analysis.get('surface_area_cm2', 0)
@@ -1007,6 +1033,14 @@ button.gr-button:hover, button.gr-button-primary:hover {
                                 <h3 style="color: #3182ce; margin: 0 0 10px 0;">Location</h3>
                                 <p style="font-weight: 600; font-size: 18px; color: #2d3748; margin: 0;">{html.escape(str(questionnaire_data.get('wound_location', 'Not specified')))}</p>
                             </div>
+                            <div style="background: white; padding: 20px; border-radius: 8px; text-align: center; box-shadow: 0 2px 8px rgba(0,0,0,0.1);">
+                                <h3 style="color: #3182ce; margin: 0 0 10px 0;">Skin Tone</h3>
+                                <p style="font-weight: 600; font-size: 18px; color: #2d3748; margin: 0;">{html.escape(str(skin_tone_label))} ({ita_degrees:.1f}°)</p>
+                            </div>
+                            <div style="background: white; padding: 20px; border-radius: 8px; text-align: center; box-shadow: 0 2px 8px rgba(0,0,0,0.1);">
+                                <h3 style="color: #3182ce; margin: 0 0 10px 0;">Tissue Type</h3>
+                                <p style="font-weight: 600; font-size: 18px; color: #2d3748; margin: 0;">{html.escape(str(tissue_type))}</p>
+                            </div>
                         </div>
                     </div>