m

app.py

@@ -112,117 +112,83 @@ def get_confidence_level(confidence):
 def main():
     st.set_page_config(layout="wide", page_title="Glaucoma Screening Tool")
     
-    # Header with better styling
     st.markdown("""
         <h1 style='text-align: center;'>Glaucoma Screening from Retinal Fundus Images</h1>
         <p style='text-align: center; color: gray;'>Upload retinal images for automated glaucoma detection and optic disc/cup segmentation</p>
         """, unsafe_allow_html=True)
     
-    # Sidebar with better organization
-    with st.sidebar:
-        st.markdown("### Upload Settings")
-        uploaded_files = st.file_uploader("Upload Retinal Images", 
-            type=['png', 'jpeg', 'jpg'], 
-            accept_multiple_files=True,
-            help="Support multiple images in PNG, JPEG formats")
-        
-        st.markdown("### Analysis Settings")
-        st.info("📊 Set confidence threshold to filter results")
-        confidence_threshold = st.slider(
-            "Classification Confidence Threshold (%)", 
-            0, 100, 70,
-            help="Images with confidence above this threshold will be marked as reliable predictions")
+    # Sidebar settings
+    st.sidebar.markdown("### Upload Settings")
+    uploaded_files = st.sidebar.file_uploader("Upload Retinal Images", 
+        type=['png', 'jpeg', 'jpg'], 
+        accept_multiple_files=True,
+        help="Support multiple images in PNG, JPEG formats")
+    
+    st.sidebar.markdown("### Analysis Settings")
+    st.sidebar.info("📊 Set confidence threshold to filter results")
+    confidence_threshold = st.sidebar.slider(
+        "Classification Confidence Threshold (%)", 
+        0, 100, 70,
+        help="Images with confidence above this threshold will be marked as reliable predictions")
     
     if uploaded_files:
         for uploaded_file in uploaded_files:
             image = Image.open(uploaded_file).convert('RGB')
             image_np = np.array(image).astype(np.uint8)
             
+            st.markdown(f"## 📊 Analysis Results: {uploaded_file.name}")
+            
             with st.spinner(f'🔄 Processing {uploaded_file.name}...'):
                 model = GlaucomaModel(device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu"))
                 disease_idx, disc_cup_image, vcdr, cls_conf, cup_conf, disc_conf, cropped_image = model.process(image_np)
                 
-                # Create expandable section for each image
-                with st.expander(f"📊 Analysis Results: {uploaded_file.name}", expanded=True):
-                    # Image display section
-                    cols = st.columns(3)
-                    cols[0].image(image_np, caption="Original Image", use_column_width=True)
-                    cols[1].image(disc_cup_image, caption="Segmentation Overlay", use_column_width=True)
-                    cols[2].image(cropped_image, caption="Region of Interest", use_column_width=True)
-                    
-                    # Metrics section with clear separation
-                    st.markdown("---")
-                    metric_cols = st.columns(3)
-                    
-                    # Classification Results
-                    with metric_cols[0]:
-                        st.markdown("### 🔍 Classification")
-                        diagnosis = model.cls_id2label[disease_idx]
-                        is_confident = cls_conf >= confidence_threshold
-                        
-                        # Color-coded diagnosis
-                        if diagnosis == "Glaucoma":
-                            st.markdown(f"<div style='padding: 10px; background-color: #ffebee; border-radius: 5px;'>"
-                                      f"<h4 style='color: #c62828;'>Diagnosis: {diagnosis}</h4></div>", 
-                                      unsafe_allow_html=True)
-                        else:
-                            st.markdown(f"<div style='padding: 10px; background-color: #e8f5e9; border-radius: 5px;'>"
-                                      f"<h4 style='color: #2e7d32;'>Diagnosis: {diagnosis}</h4></div>", 
-                                      unsafe_allow_html=True)
-                        
-                        st.metric("Classification Confidence", f"{cls_conf:.1f}%")
-                        if not is_confident:
-                            st.warning("⚠️ Below confidence threshold")
-                    
-                    # Segmentation Results
-                    with metric_cols[1]:
-                        st.markdown("### 🎯 Segmentation Quality")
-                        st.metric("Optic Cup Confidence", f"{cup_conf:.1f}%")
-                        st.metric("Optic Disc Confidence", f"{disc_conf:.1f}%")
-                        
-                        # Confidence level explanation
-                        cup_level = get_confidence_level(cup_conf)
-                        disc_level = get_confidence_level(disc_conf)
-                        st.info(f"Cup Detection: {cup_level}\nDisc Detection: {disc_level}")
-                    
-                    # Clinical Metrics
-                    with metric_cols[2]:
-                        st.markdown("### 📏 Clinical Metrics")
-                        st.metric("Cup-to-Disc Ratio (CDR)", f"{vcdr:.3f}")
-                        
-                        # CDR interpretation
-                        if vcdr > 0.7:
-                            st.warning("⚠️ Elevated CDR (>0.7)")
-                        elif vcdr > 0.5:
-                            st.info("ℹ️ Borderline CDR (0.5-0.7)")
-                        else:
-                            st.success("✅ Normal CDR (<0.5)")
-        
-        # Download section
-        if download_confident_images:
-            st.sidebar.markdown("---")
-            st.sidebar.markdown("### Download Results")
-            with zipfile.ZipFile("confident_cropped_images.zip", "w") as zf:
-                for cropped_image, name in download_confident_images:
-                    img_buffer = io.BytesIO()
-                    Image.fromarray(cropped_image).save(img_buffer, format="PNG")
-                    zf.writestr(f"{name}_cropped.png", img_buffer.getvalue())
-            
-            st.sidebar.download_button(
-                label="📥 Download Analysis Results",
-                data=open("confident_cropped_images.zip", "rb"),
-                file_name="glaucoma_analysis_results.zip",
-                mime="application/zip",
-                help="Download cropped images and analysis results"
-            )
-    else:
-        # Welcome message when no files are uploaded
-        st.markdown("""
-            <div style='text-align: center; padding: 50px;'>
-                <h3>👋 Welcome to the Glaucoma Screening Tool</h3>
-                <p>Upload retinal fundus images using the sidebar to begin analysis</p>
-            </div>
-            """, unsafe_allow_html=True)
-
-if __name__ == '__main__':
-    main()
+                # Image display section
+                st.subheader("Original Image")
+                st.image(image_np, use_column_width=True)
+                st.subheader("Segmentation Overlay")
+                st.image(disc_cup_image, use_column_width=True)
+                st.subheader("Region of Interest")
+                st.image(cropped_image, use_column_width=True)
+                
+                st.markdown("---")
+                
+                # Classification Results
+                st.markdown("### 🔍 Classification")
+                diagnosis = model.cls_id2label[disease_idx]
+                is_confident = cls_conf >= confidence_threshold
+                
+                if diagnosis == "Glaucoma":
+                    st.markdown(f"<div style='padding: 10px; background-color: #ffebee; border-radius: 5px;'>"
+                              f"<h4 style='color: #c62828;'>Diagnosis: {diagnosis}</h4></div>", 
+                              unsafe_allow_html=True)
+                else:
+                    st.markdown(f"<div style='padding: 10px; background-color: #e8f5e9; border-radius: 5px;'>"
+                              f"<h4 style='color: #2e7d32;'>Diagnosis: {diagnosis}</h4></div>", 
+                              unsafe_allow_html=True)
+                
+                st.write(f"Classification Confidence: {cls_conf:.1f}%")
+                if not is_confident:
+                    st.warning("⚠️ Below confidence threshold")
+                
+                # Segmentation Results
+                st.markdown("### 🎯 Segmentation Quality")
+                st.write(f"Optic Cup Confidence: {cup_conf:.1f}%")
+                st.write(f"Optic Disc Confidence: {disc_conf:.1f}%")
+                
+                cup_level = get_confidence_level(cup_conf)
+                disc_level = get_confidence_level(disc_conf)
+                st.info(f"Cup Detection: {cup_level}\nDisc Detection: {disc_level}")
+                
+                # Clinical Metrics
+                st.markdown("### 📏 Clinical Metrics")
+                st.write(f"Cup-to-Disc Ratio (CDR): {vcdr:.3f}")
+                
+                if vcdr > 0.7:
+                    st.warning("⚠️ Elevated CDR (>0.7)")
+                elif vcdr > 0.5:
+                    st.info("ℹ️ Borderline CDR (0.5-0.7)")
+                else:
+                    st.success("✅ Normal CDR (<0.5)")
+                
+                st.markdown("---")
+# ... rest of the code remains the same ...