Update src/ui_components_original.py

src/ui_components_original.py

@@ -58,11 +58,25 @@ def standalone_run_analysis(
     database_manager,
     wound_analyzer,
     # UI inputs
-    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,
-    manual_annotation=None
+    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,
+    manual_mask_path=None,
+    mask_adjustment=0,
 ):
     """Runs in the ZeroGPU worker; returns HTML for the UI."""
     def _label_to_id(label: str):
@@ -183,10 +197,10 @@ def standalone_run_analysis(
             'medications': meds,
             'additional_notes': notes
         }
-
-        # Attach manual annotation mask if provided
-        if manual_annotation:
-            q_for_ai['manual_mask'] = manual_annotation
+        # Include optional manual mask and adjustment in questionnaire data
+        if manual_mask_path:
+            q_for_ai['manual_mask_path'] = manual_mask_path
+        q_for_ai['mask_adjustment'] = mask_adjustment
 
         # Run AI
         analysis_result = wound_analyzer.analyze_wound(img_path, q_for_ai)
@@ -674,9 +688,6 @@ 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")
-                                    # Optional manual wound mask upload
-                                    gr.HTML("<h3>✏️ Manual Annotation (optional)</h3>")
-                                    manual_annotation = gr.Image(label="Upload Manual Wound Mask", type="filepath")
                                     gr.HTML("<h3>📝 Medical History</h3>")
                                     previous_treatment = gr.Textbox(label="Previous Treatment", lines=3)
                                     medical_history = gr.Textbox(label="Medical History", lines=3)
@@ -684,6 +695,20 @@ button.gr-button:hover, button.gr-button-primary:hover {
                                     allergies = gr.Textbox(label="Known Allergies", lines=2)
                                     additional_notes = gr.Textbox(label="Additional Notes", lines=3)
 
+                                    # Optional manual segmentation mask and adjustment
+                                    manual_mask = gr.Image(
+                                        label="Manual Segmentation Mask (optional)",
+                                        type="filepath",
+                                        interactive=True
+                                    )
+                                    mask_adjustment = gr.Slider(
+                                        -10,
+                                        10,
+                                        value=0,
+                                        step=1,
+                                        label="Segmentation Adjustment (neg = shrink, pos = expand)"
+                                    )
+
                             analyze_btn = gr.Button("🔬 Analyze Wound", variant="primary", elem_id="analyze-btn")
                             analysis_output = gr.HTML("")
 
@@ -874,18 +899,54 @@ button.gr-button:hover, button.gr-button-primary:hover {
 
             # --- 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, annot: \
+                fn=lambda mode, ex_lbl, np_n, np_a, np_g, wl, wd, p, m, i, d, pt, mh, med, al, nt, img, man_mask, mask_adj: \
                     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, annot
+                        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,
+                        man_mask,
+                        mask_adj
                     ),
                 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, manual_annotation
+                    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,
+                    manual_mask,
+                    mask_adjustment,
                 ],
-                outputs=[analysis_output]
+                outputs=[analysis_output],
             )
 
             history_btn.click(load_history, outputs=[patient_history_output])
@@ -908,13 +969,13 @@ 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)
-            detection_confidence = visual_analysis.get('detection_confidence', 0)
+            # Skin tone and tissue classification
+            skin_tone_label = visual_analysis.get('skin_tone_label', 'Unknown')
+            ita_degrees = visual_analysis.get('ita_degrees', 0.0)
+            tissue_type = visual_analysis.get('tissue_type', 'Unknown')
 
             detection_image_path = visual_analysis.get('detection_image_path', '')
             segmentation_image_path = visual_analysis.get('segmentation_image_path', '')
@@ -966,7 +1027,7 @@ button.gr-button:hover, button.gr-button-primary:hover {
                     <div class="image-item">
                         <img src="{detection_image_base64}" alt="Wound Detection">
                         <h4>🎯 Wound Detection</h4>
-                        <p>AI-detected wound boundaries with {detection_confidence:.1%} confidence</p>
+                        <p>AI-detected wound boundaries</p>
                     </div>
                     '''
                 if segmentation_image_base64:
@@ -1006,22 +1067,22 @@ button.gr-button:hover, button.gr-button-primary:hover {
                     <div style="background: #f8f9fa; padding: 30px; border-radius: 12px; margin-bottom: 30px;">
                         <h2 style="color: #2d3748; margin-top: 0;">🔍 Wound Detection & Classification</h2>
                         <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 20px; margin: 20px 0;">
+                            <!-- Wound Type -->
                             <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;">Wound Type</h3>
                                 <p style="font-weight: 600; font-size: 18px; color: #2d3748; margin: 0;">{html.escape(str(wound_type))}</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;">Detection Confidence</h3>
-                                <p style="font-weight: 600; font-size: 18px; color: #2d3748; margin: 0;">{detection_confidence:.1%}</p>
-                            </div>
+                            <!-- Location -->
                             <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;">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>
+                            <!-- Skin Tone -->
                             <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>
+                            <!-- Tissue Type -->
                             <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>