app.py

import cv2
import torch
import numpy as np
import torch.nn.functional as F
from torch import nn
from transformers import AutoImageProcessor, Swinv2ForImageClassification, SegformerForSemanticSegmentation
import streamlit as st
from PIL import Image
import io
import zipfile
import pandas as pd
from datetime import datetime
import os
import tempfile

# --- GlaucomaModel Class ---
class GlaucomaModel(object):
    def __init__(self, 
                 cls_model_path="pamixsun/swinv2_tiny_for_glaucoma_classification", 
                 seg_model_path='pamixsun/segformer_for_optic_disc_cup_segmentation',
                 device=torch.device('cpu')):
        self.device = device
        # Classification model for glaucoma
        self.cls_extractor = AutoImageProcessor.from_pretrained(cls_model_path)
        self.cls_model = Swinv2ForImageClassification.from_pretrained(cls_model_path).to(device).eval()
        # Segmentation model for optic disc and cup
        self.seg_extractor = AutoImageProcessor.from_pretrained(seg_model_path)
        self.seg_model = SegformerForSemanticSegmentation.from_pretrained(seg_model_path).to(device).eval()
        # Mapping for class labels
        self.cls_id2label = self.cls_model.config.id2label

    def glaucoma_pred(self, image):
        inputs = self.cls_extractor(images=image.copy(), return_tensors="pt")
        with torch.no_grad():
            inputs.to(self.device)
            outputs = self.cls_model(**inputs).logits
            probs = F.softmax(outputs, dim=-1)
            disease_idx = probs.cpu()[0, :].numpy().argmax()
            confidence = probs.cpu()[0, disease_idx].item() * 100
        return disease_idx, confidence

    def optic_disc_cup_pred(self, image):
        inputs = self.seg_extractor(images=image.copy(), return_tensors="pt")
        with torch.no_grad():
            inputs.to(self.device)
            outputs = self.seg_model(**inputs)
        logits = outputs.logits.cpu()
        upsampled_logits = nn.functional.interpolate(
            logits, size=image.shape[:2], mode="bilinear", align_corners=False
        )
        seg_probs = F.softmax(upsampled_logits, dim=1)
        pred_disc_cup = upsampled_logits.argmax(dim=1)[0]
        
        # Calculate segmentation confidence based on probability distribution
        # For each pixel classified as cup/disc, check how confident the model is
        cup_mask = pred_disc_cup == 2
        disc_mask = pred_disc_cup == 1
        
        # Get confidence only for pixels predicted as cup/disc
        cup_confidence = seg_probs[0, 2, cup_mask].mean().item() * 100 if cup_mask.any() else 0
        disc_confidence = seg_probs[0, 1, disc_mask].mean().item() * 100 if disc_mask.any() else 0
        
        return pred_disc_cup.numpy().astype(np.uint8), cup_confidence, disc_confidence

    def process(self, image):
        disease_idx, cls_confidence = self.glaucoma_pred(image)
        disc_cup, cup_confidence, disc_confidence = self.optic_disc_cup_pred(image)

        try:
            vcdr = simple_vcdr(disc_cup)
        except:
            vcdr = np.nan

        mask = (disc_cup > 0).astype(np.uint8)
        x, y, w, h = cv2.boundingRect(mask)
        padding = max(50, int(0.2 * max(w, h)))
        x = max(x - padding, 0)
        y = max(y - padding, 0)
        w = min(w + 2 * padding, image.shape[1] - x)
        h = min(h + 2 * padding, image.shape[0] - y)

        cropped_image = image[y:y+h, x:x+w] if w >= 50 and h >= 50 else image.copy()
        _, disc_cup_image = add_mask(image, disc_cup, [1, 2], [[0, 255, 0], [255, 0, 0]], 0.2)

        return disease_idx, disc_cup_image, vcdr, cls_confidence, cup_confidence, disc_confidence, cropped_image

# --- Utility Functions ---
def simple_vcdr(mask):
    disc_area = np.sum(mask == 1)
    cup_area = np.sum(mask == 2)
    if disc_area == 0:
        return np.nan
    vcdr = cup_area / disc_area
    return vcdr

def add_mask(image, mask, classes, colors, alpha=0.5):
    overlay = image.copy()
    for class_id, color in zip(classes, colors):
        overlay[mask == class_id] = color
    output = cv2.addWeighted(overlay, alpha, image, 1 - alpha, 0)
    return output, overlay

def get_confidence_level(confidence):
    if confidence >= 90:
        return "Very High"
    elif confidence >= 75:
        return "High"
    elif confidence >= 60:
        return "Moderate"
    elif confidence >= 45:
        return "Low"
    else:
        return "Very Low"

def process_batch(model, images_data, progress_bar=None):
    results = []
    for idx, (file_name, image) in enumerate(images_data):
        try:
            disease_idx, disc_cup_image, vcdr, cls_conf, cup_conf, disc_conf, cropped_image = model.process(image)
            results.append({
                'file_name': file_name,
                'diagnosis': model.cls_id2label[disease_idx],
                'confidence': cls_conf,
                'vcdr': vcdr,
                'cup_conf': cup_conf,
                'disc_conf': disc_conf,
                'processed_image': disc_cup_image,
                'cropped_image': cropped_image
            })
            if progress_bar:
                progress_bar.progress((idx + 1) / len(images_data))
        except Exception as e:
            st.error(f"Error processing {file_name}: {str(e)}")
    return results

def save_results(results, original_images):
    # Create temporary directory for results
    with tempfile.TemporaryDirectory() as temp_dir:
        # Save report as CSV
        df = pd.DataFrame([{
            'File': r['file_name'],
            'Diagnosis': r['diagnosis'],
            'Confidence (%)': f"{r['confidence']:.1f}",
            'VCDR': f"{r['vcdr']:.3f}",
            'Cup Confidence (%)': f"{r['cup_conf']:.1f}",
            'Disc Confidence (%)': f"{r['disc_conf']:.1f}"
        } for r in results])
        
        report_path = os.path.join(temp_dir, 'report.csv')
        df.to_csv(report_path, index=False)
        
        # Save processed images
        for result, orig_img in zip(results, original_images):
            img_name = result['file_name']
            base_name = os.path.splitext(img_name)[0]
            
            # Save original
            orig_path = os.path.join(temp_dir, f"{base_name}_original.jpg")
            Image.fromarray(orig_img).save(orig_path)
            
            # Save segmentation
            seg_path = os.path.join(temp_dir, f"{base_name}_segmentation.jpg")
            Image.fromarray(result['processed_image']).save(seg_path)
            
            # Save ROI
            roi_path = os.path.join(temp_dir, f"{base_name}_roi.jpg")
            Image.fromarray(result['cropped_image']).save(roi_path)
        
        # Create ZIP file
        zip_path = os.path.join(temp_dir, 'results.zip')
        with zipfile.ZipFile(zip_path, 'w') as zipf:
            for root, _, files in os.walk(temp_dir):
                for file in files:
                    if file != 'results.zip':
                        file_path = os.path.join(root, file)
                        arcname = os.path.basename(file_path)
                        zipf.write(file_path, arcname)
        
        with open(zip_path, 'rb') as f:
            return f.read()

# --- Streamlit Interface ---
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
    
    # 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:
        # 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"))
        
        # 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 = []
        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_bar = st.progress(0)
        st.write(f"Processing {len(images_data)} images...")
        
        # Process all images
        results = process_batch(model, images_data, progress_bar)
        
        if results:
            # 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}%")
            
            # Add filter options
            st.markdown("### 🔍 Filter Results")
            show_only = st.multiselect(
                "Show cases:",
                ["All", "Glaucoma", "Normal"],
                default=["All"]
            )
            
            # 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)
                    with cols[1]:
                        st.image(result['cropped_image'], caption="ROI", use_column_width=True)
                    with cols[2]:
                        st.write("### Metrics")
                        st.write(f"Diagnosis: {result['diagnosis']}")
                        st.write(f"Confidence: {result['confidence']:.1f}%")
                        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()