Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import tensorflow as tf
+import cv2
+import os
+
+# Suppress TensorFlow logging
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+
+# Constants
+IMAGE_H = 512
+IMAGE_W = 512
+NUM_CLASSES = 11
+
+# RGB color codes for each class
+RGB_CODES = [
+    [0, 0, 0], [0, 153, 255], [102, 255, 153], [0, 204, 153],
+    [255, 255, 102], [255, 255, 204], [255, 153, 0], [255, 102, 255],
+    [102, 0, 51], [255, 204, 255], [255, 0, 102]
+]
+
+# Load the trained model
+model = tf.keras.models.load_model("ten_epoch_model.h5")
+
+# Function to convert grayscale mask to RGB mask
+def grayscale_to_rgb(mask, rgb_codes):
+    h, w = mask.shape[0], mask.shape[1]
+    mask = mask.astype(np.int32)
+    output = [rgb_codes[pixel] for pixel in mask.flatten()]
+    return np.reshape(output, (h, w, 3)).astype(np.uint8)
+
+# Gradio inference function
+def segment_face(image):
+    # Resize and normalize input image
+    image_resized = cv2.resize(image, (IMAGE_W, IMAGE_H))
+    image_input = image_resized / 255.0
+    image_input = np.expand_dims(image_input, axis=0).astype(np.float32)
+
+    # Predict the mask
+    pred = model.predict(image_input, verbose=0)[0]
+    pred_mask = np.argmax(pred, axis=-1).astype(np.uint8)
+
+    # Convert predicted mask to RGB
+    rgb_mask = grayscale_to_rgb(pred_mask, RGB_CODES)
+
+    return rgb_mask
+
+# Launch Gradio app
+iface = gr.Interface(
+    fn=segment_face,
+    inputs=gr.Image(type="numpy", label="Upload Face Image"),
+    outputs=gr.Image(type="numpy", label="Segmentation Mask"),
+    title="Face Segmentation using Deep Learning",
+    description="Upload a face image to get a segmentation mask with different facial components marked in color."
+)
+
+if __name__ == "__main__":
+    iface.launch()