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 matplotlib.pyplot as plt
import streamlit as st
from PIL import Image
import io
import zipfile
import os

# --- 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]
        cup_confidence = seg_probs[0, 2, :, :].mean().item() * 100
        disc_confidence = seg_probs[0, 1, :, :].mean().item() * 100
        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

# --- Streamlit Interface ---
def main():
    st.set_page_config(layout="wide")
    st.title("Batch Glaucoma Screening from Retinal Fundus Images")
    
    # Explanation for the confidence threshold
    st.sidebar.write("**Confidence Threshold** (optional): Set a threshold to filter images based on the model's confidence in glaucoma classification.")
    confidence_threshold = st.sidebar.slider("Confidence Threshold (%)", 0, 100, 70)
    uploaded_files = st.sidebar.file_uploader("Upload Images", type=['png', 'jpeg', 'jpg'], accept_multiple_files=True)
    
    confident_images = []
    download_confident_images = []

    if uploaded_files:
        for uploaded_file in uploaded_files:
            image = Image.open(uploaded_file).convert('RGB')
            image_np = np.array(image).astype(np.uint8)
            
            with st.spinner(f'Processing {uploaded_file.name}...'):
                model = GlaucomaModel(device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu"))
                disease_idx, disc_cup_image, vcdr, cls_conf, cup_conf, disc_conf, cropped_image = model.process(image_np)
                
                # Confidence-based grouping
                is_confident = cls_conf >= confidence_threshold
                if is_confident:
                    confident_images.append(uploaded_file.name)
                    download_confident_images.append((cropped_image, uploaded_file.name))
                
                # Display Results
                st.subheader(f"Results for {uploaded_file.name}")
                cols = st.beta_columns(4)  # Use st.beta_columns for compatibility with older Streamlit
                cols[0].image(image_np, caption="Input Image", use_column_width=True)
                cols[1].image(disc_cup_image, caption="Disc/Cup Segmentation", use_column_width=True)
                cols[2].image(image_np, caption="Class Activation Map", use_column_width=True)
                cols[3].image(cropped_image, caption="Cropped Image", use_column_width=True)

                # Display confidence and metrics
                st.write(f"**Vertical cup-to-disc ratio:** {vcdr:.04f}")
                st.write(f"**Category:** {model.cls_id2label[disease_idx]} ({cls_conf:.02f}% confidence)")
                st.write(f"**Optic Cup Segmentation Confidence:** {cup_conf:.02f}%")
                st.write(f"**Optic Disc Segmentation Confidence:** {disc_conf:.02f}%")
                st.write(f"**Confidence Group:** {'Confident' if is_confident else 'Not Confident'}")
        
        # Download Link for Confident Images
        if download_confident_images:
            with zipfile.ZipFile("confident_cropped_images.zip", "w") as zf:
                for cropped_image, name in download_confident_images:
                    img_buffer = io.BytesIO()
                    Image.fromarray(cropped_image).save(img_buffer, format="PNG")
                    zf.writestr(f"{name}_cropped.png", img_buffer.getvalue())
                    
            # Provide a markdown link to the ZIP file
            st.sidebar.markdown(
                f"[Download Confident Cropped Images](./confident_cropped_images.zip)", 
                unsafe_allow_html=True
            )
    else:
        st.sidebar.info("Upload images to begin analysis.")

if __name__ == '__main__':
    main()