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
import base64

# Add at the top with other constants
MODEL_OPTIONS = {
    "Default (ferferefer/segformer)": "ferferefer/segformer",
    "Pamixsun": "pamixsun/segformer_for_optic_disc_cup_segmentation"
}

# --- GlaucomaModel Class ---
class GlaucomaModel(object):
    def __init__(self, 
                 cls_model_path="pamixsun/swinv2_tiny_for_glaucoma_classification", 
                 seg_model_path=None,
                 device=torch.device('cpu')):
        self.device = device
        self.seg_model_path = seg_model_path or MODEL_OPTIONS["Pamixsun"]
        
        # Classification model setup remains the same
        self.cls_extractor = AutoImageProcessor.from_pretrained(cls_model_path)
        self.cls_model = Swinv2ForImageClassification.from_pretrained(cls_model_path).to(device).eval()
        
        # Segmentation model setup with model type detection
        self.seg_extractor = AutoImageProcessor.from_pretrained(self.seg_model_path)
        self.seg_model = SegformerForSemanticSegmentation.from_pretrained(self.seg_model_path).to(device).eval()
        
        # Detect model type
        self.is_ferferefer = "ferferefer" in self.seg_model_path.lower()
        
        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
        )
        
        if self.is_ferferefer:
            # ferferefer model specific processing
            seg_probs = F.softmax(upsampled_logits, dim=1)
            pred_disc_cup = upsampled_logits.argmax(dim=1)[0]
            
            # Map ferferefer model classes to match Pamixsun format
            # Assuming ferferefer uses different class indices
            class_mapping = {
                0: 0,  # background
                1: 1,  # disc
                2: 2   # cup
            }
            
            pred_disc_cup_mapped = torch.zeros_like(pred_disc_cup)
            for old_class, new_class in class_mapping.items():
                pred_disc_cup_mapped[pred_disc_cup == old_class] = new_class
            pred_disc_cup = pred_disc_cup_mapped
        else:
            # Pamixsun model processing (original logic)
            seg_probs = F.softmax(upsampled_logits, dim=1)
            pred_disc_cup = upsampled_logits.argmax(dim=1)[0]
        
        # Calculate confidences
        cup_mask = pred_disc_cup == 2
        disc_mask = pred_disc_cup == 1
        
        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):
    """Enhanced confidence descriptions for segmentation"""
    if confidence >= 90:
        return "Excellent (Model is very certain about the detected boundaries)"
    elif confidence >= 75:
        return "Good (Model is confident about most of the detected area)"
    elif confidence >= 60:
        return "Fair (Model has some uncertainty in parts of the detection)"
    elif confidence >= 45:
        return "Poor (Model is uncertain about many detected areas)"
    else:
        return "Very Poor (Model's detection is highly uncertain)"

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():
    # Use the old layout setting method
    st.set_page_config(layout="wide")
    
    # Header with credit
    st.markdown("""
    # Glaucoma Screening from Retinal Fundus Images
    ### Developed by Dr Fernando Ly
    
    This application provides automated glaucoma detection and optic disc/cup segmentation from retinal fundus images. 
    The system uses advanced deep learning models to assist in glaucoma screening.
    """)
    
    # Hidden model selection with default to Pamixsun
    selected_model = list(MODEL_OPTIONS.keys())[1]  # Default to Pamixsun
    
    st.sidebar.title("Upload Images")
    st.set_option('deprecation.showfileUploaderEncoding', False)  # Important for old versions
    uploaded_files = st.sidebar.file_uploader(
        "Upload retinal images", 
        type=['png', 'jpeg', 'jpg'], 
        accept_multiple_files=True
    )
    
    # Enhanced explanation in sidebar
    st.sidebar.markdown("""
        ### Understanding Results:
        - **Diagnosis Confidence**: AI model's certainty level in classification
        - **VCDR**: Vertical Cup to Disc Ratio (>0.7 indicates high risk)
        - **Segmentation**: Green represents optic disc, Red represents optic cup
        
        ### About
        This tool combines state-of-the-art deep learning models for:
        1. Glaucoma Classification
        2. Optic Disc/Cup Segmentation
        
        For optimal results, please use clear retinal fundus images.
    """)
    
    if uploaded_files:
        try:
            # Load models silently without displaying information
            model = GlaucomaModel(
                device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu"),
                seg_model_path=MODEL_OPTIONS[selected_model]
            )
            
            for file in uploaded_files:
                try:
                    st.info(f"📸 Processing: {file.name}")
                    image = Image.open(file).convert('RGB')
                    image_np = np.array(image)
                    
                    disease_idx, disc_cup_image, vcdr, cls_conf, cup_conf, disc_conf, cropped_image = model.process(image_np)
                    
                    st.write("---")
                    st.success(f"Results for: {file.name}")
                    
                    # Key findings with better visuals
                    st.info("📊 Key Findings")
                    
                    # Diagnosis with color-coded warning levels
                    diagnosis = model.cls_id2label[disease_idx]
                    if diagnosis == "Glaucoma":
                        st.warning(f"Diagnosis: {diagnosis} ({cls_conf:.1f}% confidence)")
                    else:
                        st.success(f"Diagnosis: {diagnosis} ({cls_conf:.1f}% confidence)")
                    
                    # VCDR with risk levels
                    if vcdr > 0.7:
                        st.warning(f"VCDR: {vcdr:.3f} - ⚠️ High Risk")
                    elif vcdr > 0.5:
                        st.warning(f"VCDR: {vcdr:.3f} - ⚠️ Borderline")
                    else:
                        st.success(f"VCDR: {vcdr:.3f} - ✅ Normal")
                    
                    # Segmentation confidence
                    st.info("🔍 Segmentation Confidence")
                    st.write("""
                    • Optic Cup (red area): Central depression
                    • Optic Disc (green outline): Entire nerve area
                    """)
                    
                    # Cup and Disc confidence with warnings
                    if cup_conf < 60:
                        st.warning(f"Cup Detection: {cup_conf:.1f}% - Low Confidence")
                    else:
                        st.write(f"Cup Detection: {cup_conf:.1f}%")
                        
                    if disc_conf < 60:
                        st.warning(f"Disc Detection: {disc_conf:.1f}% - Low Confidence")
                    else:
                        st.write(f"Disc Detection: {disc_conf:.1f}%")
                    
                    # Images with clear sections
                    st.info("🖼️ Analysis Images")
                    st.image(disc_cup_image, caption="Green: Optic Disc | Red: Optic Cup")
                    st.image(cropped_image, caption="Region of Interest")
                    
                except Exception as e:
                    st.error(f"Error processing {file.name}: {str(e)}")
                    continue
            
            # Download section
            try:
                st.info("📥 Preparing Download")
                results = []
                original_images = []
                for file in uploaded_files:
                    image = Image.open(file).convert('RGB')
                    image_np = np.array(image)
                    disease_idx, disc_cup_image, vcdr, cls_conf, cup_conf, disc_conf, cropped_image = model.process(image_np)
                    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
                    })
                    original_images.append(image_np)
                
                zip_data = save_results(results, original_images)
                b64_zip = base64.b64encode(zip_data).decode()
                
                st.success("✅ Download Ready")
                href = f'<a href="data:application/zip;base64,{b64_zip}" download="glaucoma_results.zip">📥 Download All Results (ZIP)</a>'
                st.markdown(href, unsafe_allow_html=True)
                
            except Exception as e:
                st.error(f"Error preparing download: {str(e)}")
            
            st.success("✅ All Processing Complete!")
            
        except Exception as e:
            st.error(f"An error occurred: {str(e)}")

if __name__ == "__main__":
    main()