app.py

import gradio as gr
import tensorflow as tf
import numpy as np
import cv2  # Add this import statement
from keras.utils import normalize

def dice_coef(y_true, y_pred):
    smooth = 1e-5
    intersection = K.sum(y_true * y_pred, axis=[1, 2, 3])
    union = K.sum(y_true, axis=[1, 2, 3]) + K.sum(y_pred, axis=[1, 2, 3])
    return K.mean((2.0 * intersection + smooth) / (union + smooth), axis=0)

def predict_segmentation(image):
    SIZE_X = 128
    SIZE_Y = 128

    train_images = []
   
    img = cv2.resize(image, (SIZE_Y, SIZE_X))
    train_images.append(img)

    train_images = np.array(train_images)
    train_images = np.expand_dims(train_images, axis=3)
    train_images = normalize(train_images, axis=1)
    X_test = train_images

    custom_objects = {'dice_coef': dice_coef}
    with tf.keras.utils.custom_object_scope(custom_objects):
        model = tf.keras.models.load_model("model100.h5")
        
    
    # Normalize the test image
    test_img = X_test[0]
    test_img_norm = test_img[:, :, 0][:, :, None]
    test_img_input = np.expand_dims(test_img_norm, 0)

    # Get the prediction
    prediction = model.predict(test_img_input)
    predicted_img = np.argmax(prediction, axis=3)[0, :, :]

    # Create an RGB image with a transparent background
    rgba_img = np.zeros((predicted_img.shape[0], predicted_img.shape[1], 4))

    # Define the color for the segmented area (e.g., red)
    segmented_color = [1, 0, 0]  # Red color in RGB

    # Set the segmented area to the desired color
    for i in range(3):
        rgba_img[:, :, i] = np.where(predicted_img > 0, segmented_color[i], 0)

    # Create an alpha channel: 1 where there is segmentation, 0 otherwise
    alpha_channel = np.where(predicted_img > 0, 1, 0)
    rgba_img[:, :, 3] = alpha_channel

    return rgba_img


# Gradio Interface
iface = gr.Interface(
    fn=predict_segmentation,
    inputs="image",
    outputs="image",
    live=False
)

iface.launch()