app.py

import keras
from keras import layers
import gradio as gr
import numpy as np
from PIL import Image
import io
import contextlib

# Load model
model = keras.models.load_model("dogs_and_cats_CNN.keras")

def get_model_summary(model):
    """Return the model summary as a string."""
    stream = io.StringIO()
    with contextlib.redirect_stdout(stream):
        model.summary()
    summary_str = stream.getvalue()
    return summary_str

def get_img_array(image, target_size):
    """Resize the image and return it as an array."""
    image = image.resize(target_size)
    array = keras.utils.img_to_array(image)
    array = np.expand_dims(array, axis=0)
    return array

def predict(image):
    img_tensor = get_img_array(image, target_size=(180, 180))

    # predict class
    predictions = model.predict(img_tensor)
    if predictions[0][0] > 0.5:
        predicted_class = "Dog"
        confidence = predictions[0][0]
    else:
        predicted_class = "Cat"
        confidence = 1 - predictions[0][0]

    prediction_text = f"## **Prediction:** {predicted_class}         **Confidence:** {confidence:.2%}"
    
    
    # Collect convolution and pooling layers
    layer_outputs = []
    layer_names = []
    for layer in model.layers:
        if isinstance(layer, (layers.Conv2D, layers.MaxPooling2D)):
            # If a layer of a convolution or max pooling layers, append it's outputs to the visualization
            layer_outputs.append(layer.output)
            layer_names.append(layer.name)
    
    activation_model = keras.Model(inputs=model.input, outputs=layer_outputs)
    activations = activation_model.predict(img_tensor)

    # Build visualization grids for each layer
    images = []
    images_per_row = 16

    for layer_name, layer_activation in zip(layer_names, activations):
        n_features = layer_activation.shape[-1]
        size = layer_activation.shape[1]
        n_cols = max(1, n_features // images_per_row)
        display_grid = np.zeros(
            ((size + 1) * n_cols - 1, images_per_row * (size + 1) - 1)
        )

        for col in range(n_cols):
            for row in range(images_per_row):
                channel_index = col * images_per_row + row
                if channel_index >= n_features:
                    break
                channel_image = layer_activation[0, :, :, channel_index].copy()
                if channel_image.std() > 1e-6:
                    channel_image -= channel_image.mean()
                    channel_image /= channel_image.std()
                    channel_image *= 64
                    channel_image += 128
                channel_image = np.clip(channel_image, 0, 255).astype("uint8")
                display_grid[
                    col * (size + 1):(col + 1) * size + col,
                    row * (size + 1):(row + 1) * size + row,
                ] = channel_image

        display_grid = display_grid / 255.0
        images.append((display_grid, layer_name))

    summary_text = get_model_summary(model)
    return images, summary_text, prediction_text



# Gradio interface with examples
with gr.Blocks() as demo:
    gr.Markdown("# CNN Intermediate Activations Visualizer")
    gr.Markdown("Visualizes activations of all convolutional and pooling layers and displays the model summary.")
    gr.Markdown("Model is trained on a subset of kaggle's dogs vs cats dataset: https://www.kaggle.com/c/dogs-vs-cats/data")
    gr.Markdown("Adapted from: https://deeplearningwithpython.io/chapters/chapter10_interpreting-what-convnets-learn/#visualizing-intermediate-activations")
    
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(type="pil", label="Upload an image")
            submit_btn = gr.Button("Analyze")

            # Example images
            gr.Examples(
                examples=[
                    ["images/cat_1.jpg"],
                    ["images/dog.jpg"],
                    ["images/cat_2.jpg"],
                    ["images/cat_and_dog.jpg"]
                ],
                inputs=input_image,
                label="Try an example:"
            )
        
        with gr.Column():
            output_gallery = gr.Gallery(label="Layer Activations", show_label=True, columns=1)
            output_prediction = gr.Markdown(label="Prediction")

            gr.Markdown("As you go deeper through the neural network, the activations become more abstract and relate more to the class prediction")

            output_summary = gr.Textbox(label="Model Summary", lines=20)
    
    
    submit_btn.click(
        fn=predict,
        inputs=input_image,
        outputs=[output_gallery, output_summary, output_prediction]
    )

demo.launch()