m

app.py

@@ -12,6 +12,7 @@ import pandas as pd
 from datetime import datetime
 import os
 import tempfile
+import base64
 
 # --- GlaucomaModel Class ---
 class GlaucomaModel(object):
@@ -183,6 +184,8 @@ def save_results(results, original_images):
 def main():
     st.set_page_config(layout="wide", page_title="Glaucoma Screening Tool")
     
+    print("App started") # 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>
@@ -197,6 +200,8 @@ def main():
         help="Support multiple images in PNG, JPEG formats"
     )
     
+    print(f"Files uploaded: {uploaded_files}") # Debug print
+    
     st.sidebar.markdown("### Settings")
     max_batch = st.sidebar.number_input("Max Batch Size", 
         min_value=1, 
@@ -204,6 +209,8 @@ def main():
         value=20)
     
     if uploaded_files:
+        print("Processing uploaded files") # Debug print
+        
         if len(uploaded_files) > max_batch:
             st.warning(f"Please upload maximum {max_batch} images at once.")
             return
@@ -211,60 +218,79 @@ def main():
         st.markdown(f"Total images: {len(uploaded_files)}")
         st.markdown(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"))
-        
-        # Process images
-        images_data = []
-        original_images = []
-        for file in uploaded_files:
-            try:
-                image = Image.open(file).convert('RGB')
-                image_np = np.array(image)
-                images_data.append((file.name, image_np))
-                original_images.append(image_np)
-            except Exception as e:
-                st.error(f"Error loading {file.name}: {str(e)}")
-                continue
-        
-        progress = st.progress(0)
-        st.write(f"Processing {len(images_data)} images...")
-        
-        # Process all images
-        results = process_batch(model, images_data, progress)
-        
-        if 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("---")
+        try:
+            # Initialize model
+            print("Initializing model") # Debug print
+            model = GlaucomaModel(device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu"))
             
-            # Generate downloads
-            zip_data = save_results(results, original_images)
+            # Process images
+            images_data = []
+            original_images = []
+            print("Starting image processing") # Debug print
             
-            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"
-            )
+            for file in uploaded_files:
+                try:
+                    print(f"Processing file: {file.name}") # Debug print
+                    image = Image.open(file).convert('RGB')
+                    image_np = np.array(image)
+                    images_data.append((file.name, image_np))
+                    original_images.append(image_np)
+                except Exception as e:
+                    print(f"Error processing file {file.name}: {str(e)}") # Debug print
+                    st.error(f"Error loading {file.name}: {str(e)}")
+                    continue
             
-            # 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}%")
+            if not images_data:
+                st.error("No valid images to process!")
+                return
+                
+            progress = st.progress(0)
+            st.write(f"Processing {len(images_data)} images...")
+            
+            # Process all images
+            print("Starting batch processing") # Debug print
+            results = process_batch(model, images_data, progress)
+            print(f"Batch processing complete. Results: {len(results)}") # Debug print
+            
+            if results:
+                print("Showing results") # Debug print
+                # 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
+                print("Generating ZIP file") # Debug print
+                zip_data = save_results(results, original_images)
+                
+                st.markdown("### Download Results")
+                # Replace download_button with direct download link
+                filename = f"glaucoma_screening_{datetime.now().strftime('%Y%m%d_%H%M%S')}.zip"
+                st.markdown(
+                    f'<a href="data:application/zip;base64,{base64.b64encode(zip_data).decode()}" download="{filename}">Download All Results (ZIP)</a>', 
+                    unsafe_allow_html=True
+                )
+                
+                # 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}%")
+        
+        except Exception as e:
+            print(f"Error in main processing: {str(e)}") # Debug print
+            st.error(f"An error occurred: {str(e)}")
 
 if __name__ == "__main__":
+    print("Starting main") # Debug print
     main()