app.py

import gradio as gr
import numpy as np
import tensorflow as tf
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models, transforms
import cv2
from tensorflow.keras.models import load_model
from PIL import Image
import os
import pickle
from tensorflow.keras import backend as K

# I/O image dimensions
DISPLAY_DIMS = (256, 256)  # For display
CLASS_DIMS = (224, 224)    # For classification model input
SEG_DIMS = (128, 128)      # For segmentation model input

# Device configuration
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Define Dice Coefficient function for TensorFlow segmentation model
def dice_coefficient(y_true, y_pred, smooth=1):
    y_true_f = K.flatten(tf.cast(y_true, tf.float32))
    y_pred_f = K.flatten(tf.cast(y_pred, tf.float32))
    intersection = K.sum(y_true_f * y_pred_f)
    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)

# Define Classification Model (PyTorch)
class ClassificationModel(nn.Module):
    def __init__(self, input_dim):
        super(ClassificationModel, self).__init__()
        self.fc1 = nn.Linear(input_dim, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 16)
        self.fc4 = nn.Linear(16, 2)  # Binary Classification
        self.dropout = nn.Dropout(0.3)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = self.dropout(x)
        x = F.relu(self.fc2(x))
        x = self.dropout(x)
        x = F.relu(self.fc3(x))
        x = self.fc4(x)
        return x

# Load models
try:
    # Load ResNet feature extractor
    resnet = models.resnet18(pretrained=True)
    resnet = nn.Sequential(*list(resnet.children())[:-1])  # Remove FC layer
    resnet.to(device)
    resnet.eval()
    
    # Load Feature Selector
    with open("feature_selector.pkl", "rb") as f:
        selector = pickle.load(f)
    
    # Load Classification Model
    input_dim = selector.get_support().sum()  # Number of selected features
    classification_model = ClassificationModel(input_dim).to(device)
    classification_model.load_state_dict(torch.load("trained_model.pth", map_location=device))
    classification_model.eval()
    
    # Image transformation for PyTorch model
    transform = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])
    
    # Load segmentation model
    segmentation_model = None
    if os.path.exists("segmentation_model.h5"):
        segmentation_model = load_model("segmentation_model.h5", 
                                       custom_objects={'dice_coefficient': dice_coefficient}, 
                                       compile=False)
        print("Loaded segmentation_model.h5")
    elif os.path.exists("best_model.keras"):
        segmentation_model = load_model("best_model.keras", 
                                       custom_objects={'dice_coefficient': dice_coefficient}, 
                                       compile=False)
        print("Loaded best_model.keras")
    
    models_loaded = True
    print("Models loaded successfully!")
except Exception as e:
    print(f"Error loading models: {e}")
    print("The app will run in demo mode with simulated predictions.")
    models_loaded = False
    resnet = None
    selector = None
    classification_model = None
    segmentation_model = None
    transform = None

# Function to preprocess image for classification
def preprocess_for_classification(image):
    if not isinstance(image, Image.Image):
        image = Image.fromarray(np.array(image))
    image = image.convert("RGB")  # Ensure RGB
    return transform(image).unsqueeze(0).to(device)

# Function to preprocess image for segmentation
def preprocess_for_segmentation(image):
    if isinstance(image, Image.Image):
        image = np.array(image)
    
    # Convert to RGB if needed
    if len(image.shape) == 2:  # Grayscale
        image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
    elif image.shape[2] == 4:  # RGBA
        image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
    
    # Resize to segmentation model's input size
    image = cv2.resize(image, SEG_DIMS)
    
    # Normalize
    image = image / 255.0
    
    # Add batch dimension
    image = np.expand_dims(image, axis=0)
    
    return image

# Function to classify COVID-19 using PyTorch model
def classify_image(image):
    if image is None:
        return "No image provided", None, 0
    
    try:
        if models_loaded and resnet is not None and classification_model is not None:
            # Preprocess and extract features
            img_tensor = preprocess_for_classification(image)
            
            with torch.no_grad():
                features = resnet(img_tensor).view(-1).cpu().numpy()
            
            # Select features using the feature selector
            features_selected = selector.transform(features.reshape(1, -1))
            input_tensor = torch.tensor(features_selected, dtype=torch.float32).to(device)
            
            # Make prediction
            with torch.no_grad():
                output = classification_model(input_tensor)
                print("Classification output:",output)
                predicted_class = torch.argmax(output, dim=1).item()
                print("Classification predicted class:",predicted_class)
                probabilities = F.softmax(output, dim=1)
                print("Classification probabilities:",probabilities)
                confidence = probabilities[0][predicted_class].item()
            
            # Map class index to label (0 -> COVID, 1 -> Non-COVID)
            status = "COVID" if predicted_class == 0 else "Non-COVID"
            
            return f"Predicted: {status} (Class: {predicted_class}, Confidence: {confidence:.2f})", image, predicted_class
        else:
            # Demo mode with simulated predictions
            import random
            predicted_class = random.randint(0, 1)  # 0 or 1
            confidence = random.uniform(0.7, 0.99)
            status = "COVID" if predicted_class == 0 else "Non-COVID"
            return f"Predicted: {status} (Class: {predicted_class}, Confidence: {confidence:.2f}) [DEMO]", image, predicted_class
    except Exception as e:
        return f"Error during classification: {str(e)}", image, 0

# Function to segment lesions in CT images
def segment_image(image):
    if image is None:
        return "No segmentation performed", None, None
    
    try:
        if models_loaded and segmentation_model is not None:
            # Preprocess for segmentation
            input_image = preprocess_for_segmentation(image)
            
            # Predict mask
            pred_mask = segmentation_model.predict(input_image)
            binary_mask = (pred_mask > 0.5).astype(np.uint8)
            
            # Create colored overlay
            if isinstance(image, Image.Image):
                display_image = np.array(image)
            else:
                display_image = np.array(image)
                
            # Resize original image for display
            display_image = cv2.resize(display_image, DISPLAY_DIMS)
            
            # Resize predicted mask to match display image
            display_mask = cv2.resize(binary_mask[0].squeeze(), DISPLAY_DIMS)
            
            # Create overlay
            overlay = display_image.copy()
            if len(overlay.shape) == 2:  # If grayscale
                overlay = cv2.cvtColor(overlay, cv2.COLOR_GRAY2RGB)
            elif overlay.shape[2] == 4:  # If RGBA
                overlay = cv2.cvtColor(overlay, cv2.COLOR_RGBA2RGB)
            
            # Apply red mask on segmented areas
            overlay[:, :, 0] = np.maximum(overlay[:, :, 0], display_mask * 255)  # Red channel
            overlay[:, :, 1] = np.where(display_mask > 0, overlay[:, :, 1] * 0.5, overlay[:, :, 1])  # Reduce green
            overlay[:, :, 2] = np.where(display_mask > 0, overlay[:, :, 2] * 0.5, overlay[:, :, 2])  # Reduce blue
            
            # Calculate lesion percentage
            lesion_percentage = np.sum(binary_mask) / binary_mask.size * 100
            
            # Enhance the segmentation mask for visibility
            # Convert to 3-channel image with a heatmap colormap
            enhanced_mask = cv2.normalize(display_mask, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
            enhanced_mask = cv2.applyColorMap(enhanced_mask, cv2.COLORMAP_JET)  # Apply color map for visibility
            
            # return f"Lesion Coverage: {lesion_percentage:.2f}%", enhanced_mask, overlay
            return enhanced_mask, overlay
        else:
            # Demo mode with simulated segmentation
            return simulate_segmentation(image)
    except Exception as e:
        return f"Error during segmentation: {str(e)}", None, image

# Function to simulate segmentation for demo mode
def simulate_segmentation(image):
    # For demo mode, create a simulated segmentation
    import random
    
    if isinstance(image, Image.Image):
        display_image = np.array(image)
    else:
        display_image = np.array(image)
        
    if len(display_image.shape) == 2:
        display_image = cv2.cvtColor(display_image, cv2.COLOR_GRAY2RGB)
    elif display_image.shape[2] == 4:
        display_image = cv2.cvtColor(display_image, cv2.COLOR_RGBA2RGB)
        
    display_image = cv2.resize(display_image, DISPLAY_DIMS)
    
    # Create a blank mask
    mask = np.zeros(DISPLAY_DIMS, dtype=np.uint8)
    
    # Simulate random blobs
    num_blobs = random.randint(1, 3)
    for i in range(num_blobs):
        center_x = random.randint(50, DISPLAY_DIMS[0]-50)
        center_y = random.randint(50, DISPLAY_DIMS[1]-50)
        radius = random.randint(10, 30)
        cv2.circle(mask, (center_x, center_y), radius, 1, -1)
    
    # Create colored overlay
    overlay = display_image.copy()
    
    # Apply red mask on segmented areas
    overlay[:, :, 0] = np.maximum(overlay[:, :, 0], mask * 255)  # Red channel
    overlay[:, :, 1] = np.where(mask > 0, overlay[:, :, 1] * 0.5, overlay[:, :, 1])  # Reduce green
    overlay[:, :, 2] = np.where(mask > 0, overlay[:, :, 2] * 0.5, overlay[:, :, 2])  # Reduce blue
    
    # Enhance the mask for visibility
    enhanced_mask = cv2.normalize(mask, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
    enhanced_mask = cv2.applyColorMap(enhanced_mask, cv2.COLORMAP_JET)  # Apply color map for visibility
    
    lesion_percentage = np.sum(mask) / mask.size * 100
    
    # return f"Lesion Coverage: {lesion_percentage:.2f}% [DEMO]", enhanced_mask, overlay
    return enhanced_mask, overlay

# Function to run both classification and segmentation
def process_image(image):
    if image is None:
        return None, "No image provided", None, "No image provided"
    
    # Run classification
    classification_result, processed_image, predicted_class = classify_image(image)
    
    # Run segmentation (now for all images regardless of class)
    # segmentation_result, segmentation_map, overlay_image = segment_image(image)
    segmentation_map, overlay_image = segment_image(image)
    
    # Combine results
    # combined_result = f"{classification_result}\n{segmentation_result}"
    
    # return overlay_image, combined_result, segmentation_map, classification_result
    return overlay_image, classification_result, segmentation_map, classification_result

# Load example images
def load_covid_examples():
    examples = []
    
    try:
        # Look for COVID example images
        for i in range(1, 6):
            covid_path = f"./examples/Covid ({i}).png"
            if os.path.exists(covid_path):
                examples.append([covid_path])
        
        # If no COVID examples were found, create placeholders
        if len(examples) == 0:
            for i in range(1, 6):
                covid_img = np.ones((256, 256, 3), dtype=np.uint8) * 200
                cv2.putText(covid_img, f"COVID Example {i}", (30, 128), 
                            cv2.FONT_HERSHEY_SIMPLEX, 0.8, (100, 100, 100), 2)
                examples.append([covid_img])
    except Exception as e:
        print(f"Could not load COVID examples: {e}")
    
    return examples

def load_non_covid_examples():
    examples = []
    
    try:
        # Look for Non-COVID example images
        for i in range(1, 6):
            non_covid_path = f"./examples/Non-Covid ({i}).png"
            if os.path.exists(non_covid_path):
                examples.append([non_covid_path])
        
        # If no Non-COVID examples were found, create placeholders
        if len(examples) == 0:
            for i in range(1, 6):
                non_covid_img = np.ones((256, 256, 3), dtype=np.uint8) * 200
                cv2.putText(non_covid_img, f"Non-COVID Example {i}", (30, 128), 
                            cv2.FONT_HERSHEY_SIMPLEX, 0.8, (100, 100, 100), 2)
                examples.append([non_covid_img])
    except Exception as e:
        print(f"Could not load Non-COVID examples: {e}")
    
    return examples

class GradioInterface:
    def __init__(self):
        self.covid_examples = load_covid_examples()
        self.non_covid_examples = load_non_covid_examples()

    def create_interface(self):
        app_styles = """
        <style>
            /* Global Styles */
            body, #root {
                font-family: Helvetica, Arial, sans-serif;
                background-color: #1a1a1a;
                color: #fafafa;
            }
            /* Header Styles */
            .app-header {
                background: linear-gradient(45deg, #1a1a1a 0%, #333333 100%);
                padding: 24px;
                border-radius: 8px;
                margin-bottom: 24px;
                text-align: center;
            }
            .app-title {
                font-size: 48px;
                margin: 0;
                color: #fafafa;
            }
            .app-subtitle {
                font-size: 24px;
                margin: 8px 0 16px;
                color: #fafafa;
            }
            .app-description {
                font-size: 16px;
                line-height: 1.6;
                opacity: 0.8;
                margin-bottom: 24px;
            }
            /* Button Styles */
            .publication-links {
                display: flex;
                justify-content: center;
                flex-wrap: wrap;
                gap: 8px;
                margin-bottom: 16px;
            }
            .publication-link {
                display: inline-flex;
                align-items: center;
                padding: 8px 16px;
                background-color: #333;
                color: #fff !important;
                text-decoration: none !important;
                border-radius: 20px;
                font-size: 14px;
                transition: background-color 0.3s;
            }
            .publication-link:hover {
                background-color: #555;
            }
            .publication-link i {
                margin-right: 8px;
            }
            /* Content Styles */
            .content-container {
                background-color: #2a2a2a;
                border-radius: 8px;
                padding: 24px;
                margin-bottom: 24px;
            }
            /* Image Styles */
            .image-preview img {
                max-width: 256px;
                max-height: 256px;  
                margin: 0 auto;
                border-radius: 4px;
                display: block;
                object-fit: contain;  
            }
            /* Control Styles */
            .control-panel {
                background-color: #333;
                padding: 16px;
                border-radius: 8px;
                margin-top: 16px;
            }
            /* Gradio Component Overrides */
            .gr-button {
                background-color: #4a4a4a;
                color: #fff;
                border: none;
                border-radius: 4px;
                padding: 8px 16px;
                cursor: pointer;
                transition: background-color 0.3s;
            }
            .gr-button:hover {
                background-color: #5a5a5a;
            }
            .gr-input, .gr-dropdown {
                background-color: #3a3a3a;
                color: #fff;
                border: 1px solid #4a4a4a;
                border-radius: 4px;
                padding: 8px;
            }
            .gr-form {
                background-color: transparent;
            }
            .gr-panel {
                border: none;
                background-color: transparent;
            }
        </style>
        """

        header_html = f"""
        <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/css/bulma.min.css">
        <link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.15.4/css/all.css">
        {app_styles}
        <div class="app-header">
            <h1 class="app-title">COVID-19 CT Analysis System</h1>
            <h2 class="app-subtitle">Classification & Lesion Segmentation</h2>
            <p class="app-description">
                Upload CT scan images to detect COVID-19 and segment lesions if present.
                The system uses ResNet-18 for feature extraction and a U-Net for lesion segmentation.
            </p>
        </div>
        """

        js_func = """
        function refresh() {
            const url = new URL(window.location);
            if (url.searchParams.get('__theme') !== 'dark') {
                url.searchParams.set('__theme', 'dark');
                window.location.href = url.href;
            }
        }
        """

        with gr.Blocks(js=js_func, theme=gr.themes.Default()) as demo:
            gr.HTML(header_html)
            
            with gr.Row(elem_classes="content-container"):
                with gr.Column():
                    input_image = gr.Image(label="Upload CT Scan Image", type="pil", image_mode="RGB", elem_classes="image-preview")
                    run_button = gr.Button("Analyze Image", elem_classes="gr-button")
                    
                    with gr.Row():
                        with gr.Column(scale=1):
                            covid_examples_title = gr.Markdown("### COVID Examples")
                            covid_examples = gr.Examples(
                                examples=self.covid_examples,
                                inputs=input_image,
                                label=""
                            )
                        
                        with gr.Column(scale=1):
                            non_covid_examples_title = gr.Markdown("### Non-COVID Examples")
                            non_covid_examples = gr.Examples(
                                examples=self.non_covid_examples,
                                inputs=input_image,
                                label=""
                            )
                    
                with gr.Column():
                    with gr.Tab("Results"):
                        overlay_image = gr.Image(label="Segmentation Overlay", elem_classes="image-preview")
                        result_text = gr.Textbox(label="Analysis Results")
                        
                    with gr.Tab("Segmentation Details"):
                        segmentation_image = gr.Image(label="Lesion Segmentation Map", elem_classes="image-preview")
                        classification_text = gr.Textbox(label="Classification Details")

            run_button.click(
                fn=process_image,
                inputs=input_image,
                outputs=[overlay_image, result_text, segmentation_image, classification_text],
            )

        return demo

def main():
    interface = GradioInterface()
    demo = interface.create_interface()
    demo.launch(share=True)

if __name__ == "__main__":
    main()