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")
    
    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)
    
    # 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"
    )
    
    st.sidebar.markdown("### Settings")
    max_batch = st.sidebar.number_input("Max Batch Size", 
        min_value=1, 
        max_value=100, 
        value=20)
    
    if uploaded_files:
        if len(uploaded_files) > max_batch:
            st.warning(f"Please upload maximum {max_batch} images at once.")
            return

        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("---")
            
            # Generate downloads
            zip_data = save_results(results, original_images)
            
            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}%")

if __name__ == "__main__":
    main()