Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+import matplotlib.pyplot as plt
+
+def iou(y_true, y_pred):
+    def f(y_true, y_pred):
+        intersection = (y_true * y_pred).sum()
+        union = y_true.sum() + y_pred.sum() - intersection
+        x = (intersection + 1e-15) / (union + 1e-15)
+        x = x.astype(np.float32)
+        return x
+    return tf.numpy_function(f, [y_true, y_pred], tf.float32)
+
+def dice_coef(y_true, y_pred):
+    y_true = tf.keras.layers.Flatten()(y_true)
+    y_pred = tf.keras.layers.Flatten()(y_pred)
+    intersection = tf.reduce_sum(y_true * y_pred)
+    return (2. * intersection + 1e-15) / (tf.reduce_sum(y_true) + tf.reduce_sum(y_pred))
+
+def dice_loss(y_true, y_pred):
+    return 1.0 - dice_coef(y_true, y_pred)
+
+def read_image(file, target_size=(256, 256)):
+    img = Image.open(file).convert('RGB')
+    img = img.resize(target_size)
+    x = np.array(img, dtype=np.float32)
+    x = x / 255.0
+    return x
+
+def preprocess_image(img):
+    if img.shape[-1] == 4:
+        img = img[..., :3]
+    img_expanded = np.expand_dims(img, axis=0)
+    return img_expanded
+
+def predict_image(model, img):
+    pred = model.predict(img)
+    return pred
+
+def visualize_prediction(img, pred):
+    fig, axs = plt.subplots(1, 2, figsize=(10, 5))
+    axs[0].imshow(img)
+    axs[0].set_title('Original Image')
+    axs[1].imshow(pred[0, ...], cmap='gray')  # Assuming the prediction is a mask or similar
+    axs[1].set_title('Predicted Image')
+    plt.close(fig)
+    return fig
+
+# Load the model with custom loss and metric
+model = tf.keras.models.load_model("oryx_road_segmentation_model.h5", custom_objects={'dice_coef': dice_coef, 'iou': iou})
+
+def process_image(image):
+    img = read_image(image)
+    img_preprocessed = preprocess_image(img)
+    pred = predict_image(model, img_preprocessed)
+    return visualize_prediction(img, pred)
+
+iface = gr.Interface(fn=process_image, inputs="file", outputs="plot", title="orYx Models - Road Segmentation")
+iface.launch()