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,  # Make this optional
                 device=torch.device('cpu')):
        self.device = device
        # Classification model for glaucoma (always 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 - use provided path or default
        seg_path = seg_model_path or MODEL_OPTIONS["Pamixsun"]  # Default to Pamixsun if none provided
        self.seg_extractor = AutoImageProcessor.from_pretrained(seg_path)
        self.seg_model = SegformerForSemanticSegmentation.from_pretrained(seg_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):
    """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")
    
    # Use simple title instead of markdown
    st.title("Glaucoma Screening from Retinal Fundus Images")
    st.write("Upload retinal images for automated glaucoma detection and optic disc/cup segmentation")
    
    # Add model selection in sidebar before file upload
    st.sidebar.title("Model Settings")
    selected_model = st.sidebar.selectbox(
        "Select Segmentation Model",
        list(MODEL_OPTIONS.keys()),
        index=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
    )
    
    # Simple explanation in sidebar
    st.sidebar.markdown("""
        ### Understanding Results:
        - Diagnosis Confidence: AI certainty level
        - VCDR: Cup to disc ratio (>0.7 high risk)
        - Segmentation: Accuracy of detection
    """)
    
    if uploaded_files:
        try:
            # Enhanced model loading feedback
            st.write("🤖 Initializing AI models...")
            st.write(f"• Loading classification model: pamixsun/swinv2_tiny_for_glaucoma_classification")
            st.write(f"• Loading segmentation model: {selected_model}")
            
            # Initialize model with selected segmentation model
            model = GlaucomaModel(
                device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu"),
                seg_model_path=MODEL_OPTIONS[selected_model]
            )
            
            # Show model loading completion
            st.write("✅ Models loaded successfully")
            st.write(f"🖥️ Using: {'GPU' if torch.cuda.is_available() else 'CPU'} for processing")
            st.write("---")
            
            for file in uploaded_files:
                try:
                    # Process each image with enhanced feedback
                    st.write(f"📸 Processing image: {file.name}")
                    image = Image.open(file).convert('RGB')
                    image_np = np.array(image)
                    
                    # Get predictions
                    disease_idx, disc_cup_image, vcdr, cls_conf, cup_conf, disc_conf, cropped_image = model.process(image_np)
                    
                    # Enhanced results display
                    st.write("---")
                    st.write(f"Results for {file.name}")
                    
                    # Diagnosis section
                    st.write("📊 **Diagnosis Results:**")
                    st.write(f"• Finding: {model.cls_id2label[disease_idx]}")
                    st.write(f"• AI Confidence: {cls_conf:.1f}% ({get_confidence_level(cls_conf)})")
                    
                    # Enhanced Segmentation confidence section with detailed explanations
                    st.write("\n🔍 **Understanding Segmentation Confidence:**")
                    st.write("""
                    Segmentation confidence shows how certain the AI is about each pixel it classified:
                    • For the Optic Cup (central depression):
                      - Measures the AI's certainty that the red-colored pixels are truly part of the cup
                      - Higher confidence means clearer cup boundaries and more reliable VCDR
                      
                    • For the Optic Disc (entire circular area):
                      - Indicates how sure the AI is about the green-outlined disc boundary
                      - Higher confidence suggests better disc margin visibility
                    
                    Confidence scores are calculated by averaging the model's certainty 
                    for each pixel it identified as cup or disc. A score of 100% would mean 
                    the model is absolutely certain about every pixel's classification.
                    """)
                    
                    st.write("\n📊 **Current Segmentation Confidence Scores:**")
                    st.write(f"• Optic Cup Detection: {cup_conf:.1f}% - {get_confidence_level(cup_conf)}")
                    st.write(f"• Optic Disc Detection: {disc_conf:.1f}% - {get_confidence_level(disc_conf)}")
                    
                    # Add interpretation guidance
                    if cup_conf >= 75 and disc_conf >= 75:
                        st.write("✅ High confidence scores indicate reliable measurements")
                    elif cup_conf < 60 or disc_conf < 60:
                        st.write("""
                        ⚠️ Lower confidence scores might be due to:
                        • Image quality issues (blur, poor contrast)
                        • Unusual anatomical variations
                        • Pathological changes affecting visibility
                        • Poor image centering or focus
                        
                        Consider retaking the image if possible.
                        """)
                    
                    # Clinical metrics
                    st.write("\n📏 **Clinical Measurements:**")
                    st.write(f"• Cup-to-Disc Ratio (VCDR): {vcdr:.3f}")
                    if vcdr > 0.7:
                        st.write("  ⚠️ High VCDR - Potential risk indicator")
                    elif vcdr > 0.5:
                        st.write("  ℹ️ Borderline VCDR - Follow-up recommended")
                    else:
                        st.write("  ✅ Normal VCDR range")
                    
                    # Image display with enhanced captions
                    st.write("\n🖼️ **Visual Analysis:**")
                    st.image(disc_cup_image, caption="""
                        Segmentation Overlay
                        • Green outline: Optic Disc boundary
                        • Red area: Optic Cup region
                        • Transparency shows underlying retina
                        """)
                    st.image(cropped_image, caption="Zoomed Region of Interest")
                    
                    # Add quality note if needed
                    if cup_conf < 60 or disc_conf < 60:
                        st.write("\n⚠️ Note: Low segmentation confidence. Image quality might affect measurements.")
                    
                except Exception as e:
                    st.error(f"Error processing {file.name}: {str(e)}")
                    continue
            
            # Simple summary at the end
            st.write("---")
            st.write("Processing complete!")
            
        except Exception as e:
            st.error(f"An error occurred: {str(e)}")

if __name__ == "__main__":
    main()