m

app.py

@@ -183,30 +183,58 @@ def save_results(results, original_images):
 def main():
     st.set_page_config(layout="wide", page_title="Glaucoma Screening Tool")
     
+    print("Starting app...") # Debug print
+    
     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 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")
+    print("Header rendered...") # Debug print
     
-    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")
+    # Add session state for better state management
+    if 'processed_count' not in st.session_state:
+        st.session_state.processed_count = 0
+
+    # Add a more informative sidebar
+    with st.sidebar:
+        st.markdown("### 📤 Upload Images")
+        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("### 📊 Processing Stats")
+        if 'processed_count' in st.session_state:
+            st.metric("Images Processed", st.session_state.processed_count)
+        
+        st.markdown("---")
+        
+        # Add batch size limit
+        max_batch = st.number_input("Max Batch Size", 
+            min_value=1, 
+            max_value=100, 
+            value=20,
+            help="Maximum number of images to process in one batch")
     
     if uploaded_files:
-        st.markdown("## 📊 Batch Analysis Results")
+        # Validate batch size
+        if len(uploaded_files) > max_batch:
+            st.warning(f"⚠️ Please upload maximum {max_batch} images at once. Current: {len(uploaded_files)}")
+            return
+
+        # Add loading animation
+        with st.spinner('🔄 Initializing model...'):
+            model = GlaucomaModel(device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu"))
         
-        # Initialize model once for all images
-        model = GlaucomaModel(device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu"))
+        # Add summary metrics at the top
+        col1, col2 = st.columns(2)
+        with col1:
+            st.info(f"📁 Total images: {len(uploaded_files)}")
+        with col2:
+            st.info(f"⚙️ Using: {'GPU' if torch.cuda.is_available() else 'CPU'}")
         
         # Prepare images data
         images_data = []
@@ -228,20 +256,44 @@ def main():
         results = process_batch(model, images_data, progress_bar)
         
         if results:
-            # Generate ZIP with results
-            zip_data = save_results(results, original_images)
+            # Add summary statistics
+            st.markdown("### 📊 Summary Statistics")
+            glaucoma_count = sum(1 for r in results if r['diagnosis'] == 'Glaucoma')
+            normal_count = len(results) - glaucoma_count
+            
+            cols = st.columns(4)
+            with cols[0]:
+                st.metric("Total Processed", len(results))
+            with cols[1]:
+                st.metric("Glaucoma Detected", glaucoma_count)
+            with cols[2]:
+                st.metric("Normal", normal_count)
+            with cols[3]:
+                avg_conf = sum(r['confidence'] for r in results) / len(results)
+                st.metric("Avg Confidence", f"{avg_conf:.1f}%")
             
-            # Download button for ZIP
-            st.download_button(
-                label="📥 Download All Results",
-                data=zip_data,
-                file_name=f"glaucoma_screening_{datetime.now().strftime('%Y%m%d_%H%M%S')}.zip",
-                mime="application/zip"
+            # Add filter options
+            st.markdown("### 🔍 Filter Results")
+            show_only = st.multiselect(
+                "Show cases:",
+                ["All", "Glaucoma", "Normal"],
+                default=["All"]
             )
             
-            # Display results
-            for result in results:
-                with st.expander(f"Results for {result['file_name']}"):
+            # Filter results based on selection
+            filtered_results = results
+            if "All" not in show_only:
+                filtered_results = [
+                    r for r in results 
+                    if (r['diagnosis'] == 'Glaucoma' and 'Glaucoma' in show_only) or
+                       (r['diagnosis'] == 'Normal' and 'Normal' in show_only)
+                ]
+            
+            # Display filtered results
+            for result in filtered_results:
+                with st.expander(
+                    f"📋 {result['file_name']} - {result['diagnosis']} ({result['confidence']:.1f}% confidence)"
+                ):
                     cols = st.columns(3)
                     with cols[0]:
                         st.image(result['processed_image'], caption="Segmentation", use_column_width=True)
@@ -254,3 +306,37 @@ def main():
                         st.write(f"VCDR: {result['vcdr']:.3f}")
                         st.write(f"Cup Confidence: {result['cup_conf']:.1f}%")
                         st.write(f"Disc Confidence: {result['disc_conf']:.1f}%")
+
+            # Update session state
+            st.session_state.processed_count += len(results)
+            
+            # Add export options
+            st.markdown("### 📥 Export Options")
+            col1, col2 = st.columns(2)
+            with col1:
+                st.download_button(
+                    label="📥 Download All Results (ZIP)",
+                    data=zip_data,
+                    file_name=f"glaucoma_screening_{datetime.now().strftime('%Y%m%d_%H%M%S')}.zip",
+                    mime="application/zip"
+                )
+            with col2:
+                # Add CSV-only download
+                csv_data = pd.DataFrame([{
+                    'File': r['file_name'],
+                    'Diagnosis': r['diagnosis'],
+                    'Confidence': r['confidence'],
+                    'VCDR': r['vcdr']
+                } for r in results]).to_csv(index=False)
+                
+                st.download_button(
+                    label="📊 Download Report (CSV)",
+                    data=csv_data,
+                    file_name=f"glaucoma_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
+                    mime="text/csv"
+                )
+
+# Add this at the end of the file
+if __name__ == "__main__":
+    print("Running main...") # Debug print
+    main()