m

app.py

@@ -183,61 +183,38 @@ 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)
     
-    print("Header rendered...") # Debug print
+    # Simple sidebar without columns
+    st.sidebar.markdown("### 📤 Upload Images")
+    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"
+    )
     
-    # 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:
-            # Replace st.metric with regular markdown
-            st.markdown(f"**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")
+    st.sidebar.markdown("### Settings")
+    max_batch = st.sidebar.number_input("Max Batch Size", 
+        min_value=1, 
+        max_value=100, 
+        value=20)
     
     if uploaded_files:
-        # Validate batch size
         if len(uploaded_files) > max_batch:
-            st.warning(f"⚠️ Please upload maximum {max_batch} images at once. Current: {len(uploaded_files)}")
+            st.warning(f"Please upload maximum {max_batch} images at once.")
             return
 
-        # Add loading animation
-        with st.spinner('🔄 Initializing model...'):
-            model = GlaucomaModel(device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu"))
+        st.markdown(f"Total images: {len(uploaded_files)}")
+        st.markdown(f"Using: {'GPU' 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'}")
+        # Initialize model
+        model = GlaucomaModel(device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu"))
         
-        # Prepare images data
+        # Process images
         images_data = []
         original_images = []
         for file in uploaded_files:
@@ -250,43 +227,44 @@ def main():
                 st.error(f"Error loading {file.name}: {str(e)}")
                 continue
         
-        progress_bar = st.progress(0)
+        progress = st.progress(0)
         st.write(f"Processing {len(images_data)} images...")
         
         # Process all images
-        results = process_batch(model, images_data, progress_bar)
+        results = process_batch(model, images_data, progress)
         
         if results:
-            # Generate ZIP with results
+            # Show results one by one
+            for result in results:
+                st.markdown(f"### Results for {result['file_name']}")
+                st.markdown(f"**Diagnosis:** {result['diagnosis']}")
+                st.markdown(f"**Confidence:** {result['confidence']:.1f}%")
+                st.markdown(f"**VCDR:** {result['vcdr']:.3f}")
+                
+                # Display images
+                st.image(result['processed_image'], caption="Segmentation")
+                st.image(result['cropped_image'], caption="ROI")
+                st.markdown("---")
+            
+            # Generate downloads
             zip_data = save_results(results, original_images)
             
-            # 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,  # Now zip_data is properly defined
-                    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"
-                )
+            st.markdown("### Download Results")
+            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"
+            )
+            
+            # Simple summary
+            st.markdown("### Summary")
+            glaucoma_count = sum(1 for r in results if r['diagnosis'] == 'Glaucoma')
+            normal_count = len(results) - glaucoma_count
+            st.markdown(f"**Total Processed:** {len(results)}")
+            st.markdown(f"**Glaucoma Detected:** {glaucoma_count}")
+            st.markdown(f"**Normal:** {normal_count}")
+            st.markdown(f"**Average Confidence:** {sum(r['confidence'] for r in results) / len(results):.1f}%")
 
-# Add this at the end of the file
 if __name__ == "__main__":
-    print("Running main...") # Debug print
     main()