app.py

import io
import cv2
import matplotlib.pyplot as plt
import matplotlib
import gradio as gr
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers as L, models
from PIL import Image
import os
# Disable Gradio queue for direct REST API access - MUST be before gradio import
os.environ["GRADIO_QUEUE"] = "false"
os.environ["HF_HUB_DISABLE_GRADIO_QUEUE"] = "1"

matplotlib.use('Agg')  # Use non-interactive backend

# Import XAI libraries with error handling
try:
    import shap
    SHAP_AVAILABLE = True
except ImportError:
    SHAP_AVAILABLE = False
    print("Warning: SHAP not available")

try:
    from lime import lime_image
    LIME_AVAILABLE = True
except ImportError:
    LIME_AVAILABLE = False
    print("Warning: LIME not available")

try:
    from skimage.segmentation import mark_boundaries
    SKIMAGE_AVAILABLE = True
except ImportError:
    SKIMAGE_AVAILABLE = False
    print("Warning: scikit-image not available")


# -----------------------------
# Model Architecture Components
# -----------------------------


class Patches(L.Layer):
    def __init__(self, patch_size, **kwargs):
        super(Patches, self).__init__(**kwargs)
        self.patch_size = patch_size

    def call(self, images):
        batch_size = tf.shape(images)[0]
        patches = tf.image.extract_patches(
            images=images,
            sizes=[1, self.patch_size, self.patch_size, 1],
            strides=[1, self.patch_size, self.patch_size, 1],
            rates=[1, 1, 1, 1],
            padding="VALID"
        )
        patch_dims = patches.shape[-1]
        patches = tf.reshape(patches, [batch_size, -1, patch_dims])
        return patches


class PatchEncoder(L.Layer):
    def __init__(self, num_patches, projection_dim, **kwargs):
        super(PatchEncoder, self).__init__(**kwargs)
        self.num_patches = num_patches
        self.projection = L.Dense(units=projection_dim)
        self.position_embedding = L.Embedding(
            input_dim=num_patches, output_dim=projection_dim)

    def call(self, patch):
        positions = tf.range(start=0, limit=self.num_patches, delta=1)
        encoded = self.projection(patch) + self.position_embedding(positions)
        return encoded


# -----------------------------
# Model Configuration
# -----------------------------
image_size = 224
patch_size = 8
projection_dim = 64
transformer_layers = 4
num_heads = 4
mlp_head_units = [128, 64]

# Class names (update based on your dataset)
class_names = ['GERD', 'GERD NORMAL', 'POLYP',
               'POLYP_NORMAL']  # Update with actual class names

# -----------------------------
# Load Model
# -----------------------------
try:
    model = tf.keras.models.load_model(
        'best_fold_model.h5',
        custom_objects={
            'Patches': Patches,
            'PatchEncoder': PatchEncoder
        }
    )
    print("✓ Model loaded successfully")
except Exception as e:
    print(f"Error loading model: {e}")
    model = None

# -----------------------------
# Preprocessing Function
# -----------------------------


def preprocess_image(image):
    """

    Preprocess image for model prediction.

    """
    # Handle different input types
    if isinstance(image, str):
        # If it's a file path or URL, load it
        image = Image.open(image)
    elif not isinstance(image, Image.Image):
        # If it's a numpy array, convert to PIL
        image = Image.fromarray(image)

    # Convert to RGB if necessary
    if image.mode != 'RGB':
        image = image.convert('RGB')

    # Resize to model input size
    image = image.resize((image_size, image_size))

    # Convert to numpy array and normalize
    img_array = np.array(image, dtype=np.float32)
    img_array = img_array / 255.0  # Normalize to [0, 1]

    # Add batch dimension
    img_array = np.expand_dims(img_array, axis=0)

    return img_array

# -----------------------------
# Prediction Function
# -----------------------------


def predict(image):
    """

    Make prediction on input image.

    """
    if model is None:
        # Return zero confidence for all classes when model not loaded
        return {class_name: 0.0 for class_name in class_names}

    if image is None:
        # Return zero confidence for all classes when no image provided
        return {class_name: 0.0 for class_name in class_names}

    try:
        # Preprocess image
        processed_image = preprocess_image(image)

        # Make prediction
        predictions = model.predict(processed_image, verbose=0)

        # Get probabilities for each class
        probabilities = predictions[0]

        # Create result dictionary with validated float values
        results = {}
        for i in range(len(class_names)):
            prob = probabilities[i]
            # Ensure the probability is a valid number
            if prob is None or (isinstance(prob, float) and (np.isnan(prob) or np.isinf(prob))):
                results[class_names[i]] = 0.0
            else:
                results[class_names[i]] = float(prob)

        return results

    except Exception as e:
        print(f"Prediction error: {e}")
        # Return zero confidence for all classes on error
        return {class_name: 0.0 for class_name in class_names}


# -----------------------------
# GradCAM Implementation
# -----------------------------


def make_gradcam_heatmap(img_array, model, pred_index=None):
    """

    Generate Grad-CAM heatmap for lightweight ViT model

    Uses the transformer output before global pooling

    """
    try:
        # Find the layer before GlobalAveragePooling (typically the last Add or LayerNormalization)
        target_layer = None
        for layer in reversed(model.layers):
            # Look for the last Add layer (from transformer blocks)
            if isinstance(layer, tf.keras.layers.Add):
                target_layer = layer
                break
            # Or the LayerNormalization before classification head
            if isinstance(layer, tf.keras.layers.LayerNormalization) and 'representation' not in layer.name:
                target_layer = layer
                break

        if target_layer is None:
            # Fallback: find any layer with 3D output (batch, seq_len, features)
            for layer in reversed(model.layers):
                if hasattr(layer, 'output_shape') and len(layer.output_shape) == 3:
                    target_layer = layer
                    break

        if target_layer is None:
            print("Warning: No suitable layer found for Grad-CAM")
            return None, pred_index

        # Create a model that outputs both the target layer output and final predictions
        grad_model = tf.keras.models.Model(
            inputs=model.inputs,
            outputs=[model.get_layer(target_layer.name).output, model.output]
        )

        # Compute gradients
        with tf.GradientTape() as tape:
            layer_output, predictions = grad_model(img_array, training=False)
            if pred_index is None:
                pred_index = tf.argmax(predictions[0])
            class_channel = predictions[:, pred_index]

        # Get gradients of the predicted class with respect to the layer output
        grads = tape.gradient(class_channel, layer_output)

        if grads is None:
            print("Warning: Gradients are None. Using simple attention map.")
            # Fallback: use attention weights
            layer_output_np = layer_output[0].numpy()
            heatmap = np.mean(np.abs(layer_output_np), axis=-1)
            # Reshape to 2D grid
            num_patches = heatmap.shape[0]
            grid_size = int(np.sqrt(num_patches))
            heatmap = heatmap[:grid_size *
                              grid_size].reshape(grid_size, grid_size)
            heatmap = (heatmap - heatmap.min()) / \
                (heatmap.max() - heatmap.min() + 1e-10)
            return heatmap, int(pred_index.numpy())

        # Global average pooling on gradients
        if len(grads.shape) == 3:  # (batch, seq_len, features)
            pooled_grads = tf.reduce_mean(grads, axis=(0, 1))
            layer_output = layer_output[0]

            # Weight the sequence by the gradients
            heatmap = layer_output @ pooled_grads[..., tf.newaxis]
            heatmap = tf.squeeze(heatmap)

            # Reshape to 2D grid
            num_patches = heatmap.shape[0]
            grid_size = int(np.sqrt(num_patches))
            if grid_size * grid_size != num_patches:
                # Handle case where sqrt is not exact
                # Exclude class token if present
                grid_size = int(np.sqrt(num_patches - 1))
                heatmap = heatmap[1:grid_size*grid_size+1]  # Skip class token
            else:
                heatmap = heatmap[:grid_size*grid_size]
            heatmap = tf.reshape(heatmap, (grid_size, grid_size))
        else:
            pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
            layer_output = layer_output[0]
            heatmap = layer_output @ pooled_grads[..., tf.newaxis]
            heatmap = tf.squeeze(heatmap)

        # Normalize between 0 and 1
        heatmap = tf.maximum(heatmap, 0) / \
            (tf.math.reduce_max(heatmap) + 1e-10)
        return heatmap.numpy(), int(pred_index.numpy())

    except Exception as e:
        print(f"GradCAM error: {e}")
        import traceback
        traceback.print_exc()
        return None, pred_index


def apply_gradcam(image, heatmap, alpha=0.4):
    """

    Apply GradCAM heatmap overlay on the original image.

    """
    try:
        if heatmap is None:
            return image

        # Convert image to numpy array
        if isinstance(image, Image.Image):
            img_array = np.array(image.resize((image_size, image_size)))
        else:
            img_array = image

        # Resize heatmap to match input image size
        heatmap_resized = cv2.resize(
            heatmap, (img_array.shape[1], img_array.shape[0]))

        # Convert heatmap to RGB
        heatmap_uint8 = np.uint8(255 * heatmap_resized)
        heatmap_colored = cv2.applyColorMap(heatmap_uint8, cv2.COLORMAP_JET)
        heatmap_colored = cv2.cvtColor(heatmap_colored, cv2.COLOR_BGR2RGB)

        # Normalize image if needed
        if img_array.max() <= 1.0:
            img_uint8 = (img_array * 255).astype('uint8')
        else:
            img_uint8 = img_array.astype('uint8')

        # Superimpose the heatmap on original image
        superimposed_img = heatmap_colored * alpha + img_uint8 * (1 - alpha)
        superimposed_img = np.clip(superimposed_img, 0, 255).astype('uint8')

        return Image.fromarray(superimposed_img)

    except Exception as e:
        print(f"Apply GradCAM error: {e}")
        return image


def generate_gradcam(image):
    """

    Generate GradCAM visualization.

    """
    if model is None or image is None:
        return None

    try:
        # Preprocess image
        processed_image = preprocess_image(image)

        # Make prediction
        predictions = model.predict(processed_image, verbose=0)
        pred_class = np.argmax(predictions[0])

        # Generate heatmap
        heatmap, _ = make_gradcam_heatmap(processed_image, model, pred_class)

        if heatmap is None:
            return None

        # Apply heatmap
        gradcam_image = apply_gradcam(image, heatmap, alpha=0.4)

        return gradcam_image

    except Exception as e:
        print(f"Error generating GradCAM: {e}")
        return None


# -----------------------------
# SHAP Implementation
# -----------------------------


def generate_shap(image):
    """

    Generate SHAP explanation visualization.

    """
    if not SHAP_AVAILABLE:
        return None

    if model is None or image is None:
        return None

    try:
        # Preprocess image
        if isinstance(image, Image.Image):
            img_array = np.array(image.resize((image_size, image_size)))
        else:
            img_array = image

        # Ensure image is uint8
        if img_array.dtype != np.uint8:
            img_array = np.uint8(
                img_array * 255 if img_array.max() <= 1 else img_array)

        # Define model prediction function
        def model_predict(x):
            # Normalize to [0, 1] before prediction
            preds = model(tf.convert_to_tensor(x / 255.0))
            return preds.numpy()

        # Create masker
        masker = shap.maskers.Image("inpaint_telea", img_array.shape)

        # Create explainer
        explainer = shap.Explainer(
            model_predict, masker, output_names=class_names)

        # Get SHAP values for the top predicted class
        shap_values = explainer(
            img_array[np.newaxis, ...], outputs=shap.Explanation.argsort.flip[:1])

        # Create visualization
        plt.figure(figsize=(10, 8))
        shap.image_plot(shap_values, img_array[np.newaxis, ...], show=False)

        # Save to buffer
        buf = io.BytesIO()
        plt.savefig(buf, format='png', bbox_inches='tight', dpi=100)
        buf.seek(0)
        shap_image = Image.open(buf)
        plt.close()

        return shap_image

    except Exception as e:
        print(f"SHAP error: {e}")
        return None


# -----------------------------
# LIME Implementation
# -----------------------------


def generate_lime(image):
    """

    Generate LIME explanation visualization.

    """
    if not LIME_AVAILABLE or not SKIMAGE_AVAILABLE:
        return None, None

    if model is None or image is None:
        return None, None

    try:
        # Preprocess image
        if isinstance(image, Image.Image):
            img_array = np.array(image.resize((image_size, image_size)))
        else:
            img_array = image

        # Normalize
        img_normalized = img_array / 255.0 if img_array.max() > 1 else img_array

        # Create LIME explainer
        explainer = lime_image.LimeImageExplainer()

        # Generate explanation
        explanation = explainer.explain_instance(
            img_normalized.astype('float64'),
            model.predict,
            top_labels=3,
            hide_color=0,
            num_samples=1000,
            batch_size=32
        )

        # Create visualizations
        # Positive features only
        temp_positive, mask_positive = explanation.get_image_and_mask(
            explanation.top_labels[0],
            positive_only=True,
            num_features=10,
            hide_rest=False
        )
        lime_positive = mark_boundaries(temp_positive, mask_positive)

        # Positive and negative features
        temp_both, mask_both = explanation.get_image_and_mask(
            explanation.top_labels[0],
            positive_only=False,
            num_features=10,
            hide_rest=False
        )
        lime_both = mark_boundaries(temp_both, mask_both)

        # Convert to PIL Images
        lime_positive_img = Image.fromarray(
            (lime_positive * 255).astype(np.uint8))
        lime_both_img = Image.fromarray((lime_both * 255).astype(np.uint8))

        return lime_positive_img, lime_both_img

    except Exception as e:
        print(f"LIME error: {e}")
        return None, None


# -----------------------------
# Unified Prediction with XAI
# -----------------------------


def predict_with_xai(image):
    """

    Make prediction and generate all XAI explanations at once.

    """
    if model is None or image is None:
        return {class_name: 0.0 for class_name in class_names}, None, None, None, None

    try:
        # Make prediction
        prediction_results = predict(image)

        # Generate GradCAM
        gradcam_img = generate_gradcam(image)

        # Generate SHAP (can be slow)
        shap_img = generate_shap(image)

        # Generate LIME (can be slow)
        lime_positive, lime_both = generate_lime(image)

        return prediction_results, gradcam_img, shap_img, lime_positive, lime_both

    except Exception as e:
        print(f"Error in predict_with_xai: {e}")
        return {class_name: 0.0 for class_name in class_names}, None, None, None, None


# -----------------------------
# Gradio Interface
# -----------------------------
title = "🔬 GERD Lightweight Vision Transformer with XAI"
description = """

<div style="text-align: center; padding: 20px;">

    <h2 style="color: #2E86AB;">Advanced Medical Image Analysis with Explainable AI</h2>

    <p style="font-size: 16px; color: #555;">

        Upload an endoscopic image to classify using a <b>Lightweight Vision Transformer</b> model.

        Get predictions with <b>three explainability methods</b> to understand the AI's decision.

    </p>

    <div style="display: flex; justify-content: center; gap: 20px; margin-top: 15px;">

        <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 15px; border-radius: 10px; color: white;">

            <b>📊 Model Architecture</b><br>

            Image: 224×224 | Patches: 8×8<br>

            Projection: 64 | Layers: 4 | Heads: 4

        </div>

        <div style="background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); padding: 15px; border-radius: 10px; color: white;">

            <b>🎯 XAI Methods</b><br>

            GradCAM | SHAP | LIME<br>

            Visual Explanations

        </div>

    </div>

</div>

"""

# Custom CSS for creative styling
custom_css = """

.gradio-container {

    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif !important;

}

h1 {

    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);

    -webkit-background-clip: text;

    -webkit-text-fill-color: transparent;

    font-size: 2.5em !important;

    text-align: center !important;

}

.button-primary {

    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;

    border: none !important;

    color: white !important;

    font-weight: bold !important;

    padding: 12px 30px !important;

    border-radius: 25px !important;

    font-size: 16px !important;

    transition: all 0.3s ease !important;

}

.button-primary:hover {

    transform: scale(1.05) !important;

    box-shadow: 0 8px 15px rgba(102, 126, 234, 0.4) !important;

}

"""

# Create Gradio interface using Blocks with creative design
with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
    gr.HTML(f"<h1>{title}</h1>")
    gr.HTML(description)

    with gr.Row():
        with gr.Column(scale=1):
            input_image = gr.Image(
                type="pil", label="📤 Upload Endoscopic Image")
            predict_btn = gr.Button(
                "🔍 Classify & Explain", variant="primary", elem_classes="button-primary", size="lg")

            gr.Markdown("""

            <div style="background: #f0f4f8; padding: 15px; border-radius: 10px; margin-top: 10px;">

                <b>ℹ️ Instructions:</b>

                <ul>

                    <li>Upload an endoscopic image (JPG, PNG)</li>

                    <li>Click "Classify & Explain" to get results</li>

                    <li>View prediction + XAI explanations below</li>

                    <li><i>Note: SHAP and LIME may take 30-60 seconds</i></li>

                </ul>

            </div>

            """)

        with gr.Column(scale=1):
            output_label = gr.Label(
                num_top_classes=4, label="📊 Prediction Results", show_label=True)

    # Explanations Section
    gr.Markdown("""

    <div style="text-align: center; margin-top: 30px; margin-bottom: 20px;">

        <h2 style="color: #2E86AB;">🎯 Explainable AI Visualizations</h2>

        <p style="color: #666;">Understanding how the model makes its predictions</p>

    </div>

    """)

    with gr.Row():
        # GradCAM
        with gr.Column(scale=1):
            gr.Markdown("""

            <div style="background: linear-gradient(135deg, #fff3e0 0%, #ffe0b2 100%); padding: 15px; border-radius: 10px; margin-bottom: 10px;">

                <h3 style="margin: 0; color: #e65100;">🔥 Grad-CAM</h3>

                <p style="margin: 5px 0 0 0; font-size: 14px;">

                    <b>Gradient-weighted Class Activation Mapping</b><br>

                    Highlights regions most important for prediction. Red = high importance.

                </p>

            </div>

            """)
            output_gradcam = gr.Image(
                label="Grad-CAM Heatmap", show_label=False)

    with gr.Row():
        # SHAP
        with gr.Column(scale=1):
            gr.Markdown("""

            <div style="background: linear-gradient(135deg, #e8f5e9 0%, #c8e6c9 100%); padding: 15px; border-radius: 10px; margin-bottom: 10px;">

                <h3 style="margin: 0; color: #2e7d32;">🎯 SHAP</h3>

                <p style="margin: 5px 0 0 0; font-size: 14px;">

                    <b>SHapley Additive exPlanations</b><br>

                    Red pixels push toward predicted class, blue pixels push away.

                </p>

            </div>

            """)
            output_shap = gr.Image(label="SHAP Explanation", show_label=False)

    with gr.Row():
        # LIME
        with gr.Column(scale=1):
            gr.Markdown("""

            <div style="background: linear-gradient(135deg, #fce4ec 0%, #f8bbd0 100%); padding: 15px; border-radius: 10px; margin-bottom: 10px;">

                <h3 style="margin: 0; color: #c2185b;">🍋 LIME - Positive Features</h3>

                <p style="margin: 5px 0 0 0; font-size: 14px;">

                    <b>Local Interpretable Model-agnostic Explanations</b><br>

                    Green boundaries show regions supporting the prediction.

                </p>

            </div>

            """)
            output_lime_positive = gr.Image(
                label="LIME Positive", show_label=False)

        with gr.Column(scale=1):
            gr.Markdown("""

            <div style="background: linear-gradient(135deg, #e1f5fe 0%, #b3e5fc 100%); padding: 15px; border-radius: 10px; margin-bottom: 10px;">

                <h3 style="margin: 0; color: #01579b;">🍋 LIME - All Features</h3>

                <p style="margin: 5px 0 0 0; font-size: 14px;">

                    <b>Positive & Negative Contributions</b><br>

                    Shows both supporting and opposing regions.

                </p>

            </div>

            """)
            output_lime_both = gr.Image(
                label="LIME Positive & Negative", show_label=False)

    # Footer
    gr.Markdown("""

    <div style="text-align: center; margin-top: 30px; padding: 20px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border-radius: 10px; color: white;">

        <h3>🏥 Medical AI with Transparency</h3>

        <p>This tool combines state-of-the-art Vision Transformer technology with explainable AI methods 

        to provide transparent and interpretable medical image analysis.</p>

        <p style="font-size: 12px; margin-top: 10px;">

            <b>Classes:</b> GERD, GERD NORMAL, POLYP, POLYP NORMAL

        </p>

    </div>

    """)

    # Connect button to unified function
    predict_btn.click(
        fn=predict_with_xai,
        inputs=input_image,
        outputs=[output_label, output_gradcam, output_shap,
                 output_lime_positive, output_lime_both],
        api_name="predict"
    )

# Launch with error reporting enabled
if __name__ == "__main__":
    demo.launch(show_error=True)