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
+from tensorflow.keras.models import load_model
+from tensorflow.keras import backend as K
+
+# ---- Custom metrics/loss required to load your model ---- #
+def dice_coef(y_true, y_pred):
+    y_true = K.cast(y_true, 'float32')
+    y_pred = K.cast(y_pred, 'float32')
+    y_true_f = K.flatten(y_true)
+    y_pred_f = K.flatten(y_pred)
+    intersect = K.sum(y_true_f * y_pred_f)
+    return (2. * intersect + K.epsilon()) / (K.sum(y_true_f) + K.sum(y_pred_f) + K.epsilon())
+
+# ---- Load model from Hugging Face model repo ---- #
+model_path = "yaraa11/brain-tumor-segmentation"   # <-- Replace with your HF model ID
+model = load_model(model_path, custom_objects={'dice_coef': dice_coef}, compile=False)
+
+IMG_SIZE = (224, 224)
+
+# ---- Prediction function for Gradio ---- #
+def predict(img):
+    # Convert to grayscale if not already
+    img = img.convert("L")
+
+    # Resize
+    img_resized = img.resize(IMG_SIZE)
+    x = np.array(img_resized) / 255.0
+    x = np.expand_dims(x, axis=(0, -1))  # shape: (1, 224, 224, 1)
+
+    # Predict mask
+    pred = model.predict(x)[0]
+    mask = (pred > 0.5).astype(np.uint8) * 255  # Convert to binary mask
+
+    # Convert numpy mask to image
+    mask_img = Image.fromarray(mask.squeeze(), mode="L")
+    return mask_img
+
+# ---- Gradio Interface ---- #
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload Brain MRI Image"),
+    outputs=gr.Image(type="pil", label="Predicted Tumor Mask"),
+    title="Brain Tumor Segmentation",
+    description="Upload a brain MRI image to generate a tumor segmentation mask."
+)
+
+interface.launch()