app.py

import gradio as gr
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
import cv2
import PIL.Image
import traceback
from io import BytesIO

# Function to generate a simple explanation based on the saliency map and prediction
def generate_explanation(model_prediction, confidence):
    explanation = (
        f"The model predicts the tumor type is '{model_prediction}' with a confidence of {confidence * 100:.2f}%. "
        "This prediction is based on the highlighted regions of the MRI scan which contributed most to the decision."
    )
    return explanation

def generate_saliency_map(model, img_array, class_index, img_size):
    with tf.GradientTape() as tape:
        img_tensor = tf.convert_to_tensor(img_array)
        tape.watch(img_tensor)
        predictions = model(img_tensor)
        target_class = predictions[:, class_index]

    gradients = tape.gradient(target_class, img_tensor)
    gradients = tf.math.abs(gradients)
    gradients = tf.reduce_max(gradients, axis=-1)
    gradients = gradients.numpy().squeeze()

    gradients = cv2.resize(gradients, img_size)

    # Create a circular mask to focus on the brain region
    center = (gradients.shape[0] // 2, gradients.shape[1] // 2)
    radius = min(center[0], center[1]) - 10
    y, x = np.ogrid[:gradients.shape[0], :gradients.shape[1]]
    mask = (x - center[0])**2 + (y - center[1])**2 <= radius**2

    gradients = gradients * mask

    # Normalize the gradients within the brain region
    brain_gradients = gradients[mask]
    if brain_gradients.max() > brain_gradients.min():
        brain_gradients = (brain_gradients - brain_gradients.min()) / (brain_gradients.max() - brain_gradients.min())
    gradients[mask] = brain_gradients

    # Apply thresholding to highlight important regions
    threshold = np.percentile(gradients[mask], 80)
    gradients[gradients < threshold] = 0

    # Apply Gaussian blur to smooth the saliency map
    gradients = cv2.GaussianBlur(gradients, (11, 11), 0)

    # Create a heatmap from the gradients
    heatmap = cv2.applyColorMap(np.uint8(255 * gradients), cv2.COLORMAP_JET)
    heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB)

    heatmap = cv2.resize(heatmap, img_size)

    # Superimpose the heatmap on the original image
    original_img = image.img_to_array(PIL.Image.fromarray((img_array[0] * 255).astype(np.uint8)))
    superimposed_img = heatmap * 0.7 + original_img * 0.3
    superimposed_img = superimposed_img.astype(np.uint8)

    return superimposed_img

def load_xception_model(model_path):
    img_shape = (299, 299, 3)
    base_model = tf.keras.applications.Xception(include_top=False, weights="imagenet", input_shape=img_shape, pooling='max')

    model = tf.keras.Sequential([
        base_model,
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dropout(rate=0.3),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dropout(rate=0.25),
        tf.keras.layers.Dense(4, activation='softmax')
    ])

    model.compile(optimizer=tf.keras.optimizers.Adamax(learning_rate=0.001),
                  loss='categorical_crossentropy',
                  metrics=['accuracy', tf.keras.metrics.Precision(), tf.keras.metrics.Recall()])
    model.load_weights(model_path)
    return model

def classify_brain_tumor(image_file, model_choice):
    try:
        # Load the selected model
        if model_choice == "Transfer Learning - Xception":
            model = load_xception_model('xception_model.weights.h5')
            img_size = (299, 299)
        else:
            model = load_model('cnn_model.h5')
            img_size = (224, 224)

        labels = ['Glioma', 'Meningioma', 'No Tumor', 'Pituitary']

        # Preprocess the input image
        img = image.load_img(image_file, target_size=img_size)
        img_array = image.img_to_array(img)
        img_array = np.expand_dims(img_array, axis=0)
        img_array /= 255.0

        # Make the prediction
        prediction = model.predict(img_array)
        class_index = np.argmax(prediction[0])
        result = labels[class_index]
        confidence = prediction[0][class_index]

        # Generate the saliency map
        saliency_map = generate_saliency_map(model, img_array, class_index, img_size)

        # Generate the explanation
        explanation = generate_explanation(result, confidence)

        # Prepare probabilities for all classes
        probabilities = prediction[0]
        prob_dict = dict(zip(labels, probabilities))

        # Return the outputs in the expected order
        return [
            result,
            confidence,
            saliency_map,
            explanation,
            "",  # Empty string for Logs
            prob_dict  # For displaying probabilities
        ]
    except Exception as e:
        # Return error information
        return [
            "Error",
            0.0,
            None,
            "",
            f"Error: {str(e)}\nTraceback:\n{traceback.format_exc()}",
            {}  # Empty probabilities
        ]

def main():
    # Define the interface
    interface = gr.Interface(
        fn=classify_brain_tumor,
        inputs=[
            gr.Image(type="filepath"),
            gr.Radio(choices=["Transfer Learning - Xception", "Custom CNN"], label="Select Model")
        ],
        outputs=[
            gr.Textbox(label="Prediction"),
            gr.Number(label="Confidence", precision=2),
            gr.Image(type="numpy", label="Saliency Map"),
            gr.Textbox(label="Explanation"),
            gr.Textbox(label="Logs"),
            gr.Label(num_top_classes=4, label="Class Probabilities")
        ],
        title="Brain Tumor Classification",
        description="Upload an MRI scan image to classify the tumor and view saliency maps with model explanations.",
    )
    interface.launch()

if __name__ == "__main__":
    main()