m

app.py

@@ -102,16 +102,17 @@ def add_mask(image, mask, classes, colors, alpha=0.5):
     return output, overlay
 
 def get_confidence_level(confidence):
+    """Enhanced confidence descriptions for segmentation"""
     if confidence >= 90:
-        return "Very High"
+        return "Excellent (Very clear boundaries)"
     elif confidence >= 75:
-        return "High"
+        return "Good (Clear boundaries)"
     elif confidence >= 60:
-        return "Moderate"
+        return "Fair (Visible but some unclear areas)"
     elif confidence >= 45:
-        return "Low"
+        return "Poor (Difficult to determine)"
     else:
-        return "Very Low"
+        return "Very Poor (Not reliable)"
 
 def process_batch(model, images_data, progress_bar=None):
     results = []
@@ -222,21 +223,43 @@ def main():
                     # Get predictions
                     disease_idx, disc_cup_image, vcdr, cls_conf, cup_conf, disc_conf, cropped_image = model.process(image_np)
                     
-                    # Display results using old methods
+                    # Enhanced results display
                     st.write("---")
                     st.write(f"Results for {file.name}")
                     
-                    # Show diagnosis
-                    diagnosis = model.cls_id2label[disease_idx]
-                    st.write(f"Diagnosis: {diagnosis}")
-                    st.write(f"Confidence: {cls_conf:.1f}%")
-                    st.write(f"VCDR: {vcdr:.3f}")
+                    # Diagnosis section
+                    st.write("📊 **Diagnosis Results:**")
+                    st.write(f"• Finding: {model.cls_id2label[disease_idx]}")
+                    st.write(f"• AI Confidence: {cls_conf:.1f}% ({get_confidence_level(cls_conf)})")
                     
-                    # Display images
-                    st.write("Segmentation Result:")
-                    st.image(disc_cup_image, caption="Green: Optic Disc | Red: Optic Cup")
-                    st.write("Region of Interest:")
-                    st.image(cropped_image, caption="ROI")
+                    # Segmentation confidence section
+                    st.write("\n🔍 **Segmentation Quality:**")
+                    st.write(f"• Optic Cup Detection: {cup_conf:.1f}% - {get_confidence_level(cup_conf)}")
+                    st.write(f"• Optic Disc Detection: {disc_conf:.1f}% - {get_confidence_level(disc_conf)}")
+                    
+                    # Clinical metrics
+                    st.write("\n📏 **Clinical Measurements:**")
+                    st.write(f"• Cup-to-Disc Ratio (VCDR): {vcdr:.3f}")
+                    if vcdr > 0.7:
+                        st.write("  ⚠️ High VCDR - Potential risk indicator")
+                    elif vcdr > 0.5:
+                        st.write("  ℹ️ Borderline VCDR - Follow-up recommended")
+                    else:
+                        st.write("  ✅ Normal VCDR range")
+                    
+                    # Image display with enhanced captions
+                    st.write("\n🖼️ **Visual Analysis:**")
+                    st.image(disc_cup_image, caption="""
+                        Segmentation Overlay
+                        • Green outline: Optic Disc boundary
+                        • Red area: Optic Cup region
+                        • Transparency shows underlying retina
+                        """)
+                    st.image(cropped_image, caption="Zoomed Region of Interest")
+                    
+                    # Add quality note if needed
+                    if cup_conf < 60 or disc_conf < 60:
+                        st.write("\n⚠️ Note: Low segmentation confidence. Image quality might affect measurements.")
                     
                 except Exception as e:
                     st.error(f"Error processing {file.name}: {str(e)}")