Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import cv2
+from tensorflow.keras.preprocessing.image import img_to_array
+from tensorflow.keras.models import load_model
+import tensorflow as tf
+
+# Load your our trained Model
+model = load_model('Crack-segmentation.h5')
+
+
+def preprocess_image(image, target_size=(128, 128)):
+    # Convert the PIL image to a NumPy array
+    img_array = np.array(image)
+    # Resize the image using OpenCV
+    img_resized = cv2.resize(img_array, target_size)
+    # Normalize the image
+    img_resized = img_resized.astype('float32') / 255.0
+    # Expand dimensions to match the input shape for the model
+    img_resized = np.expand_dims(img_resized, axis=0)
+    return img_resized
+
+def predict_mask(image):
+    preprocessed_image = preprocess_image(image)
+    # Make predictions
+    predictions = model.predict(preprocessed_image)
+    # Squeeze the prediction to remove the batch dimension
+    predicted_mask = predictions.squeeze()
+    # Normalize the mask to [0, 255] and convert to uint8
+    predicted_mask = (predicted_mask * 255).astype(np.uint8)
+    return predicted_mask
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=predict_mask,
+    inputs=gr.Image(),
+    outputs=gr.Image(type="numpy",label='Segmented Image 🚀'),
+    title="Crack Segmentation",
+    description="Upload an Image 📥"
+)
+
+# Launch the Gradio interface
+iface.launch()