Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+CLASS_NAMES = [
+    "MildDemented",
+    "Moderate Dementia",
+    "NonDemented",
+    "Very mild Dementia",
+    "glioma",
+    "meningioma",
+    "notumor",
+    "pituitary"
+]
+
+# Load model locally in the Space
+model = tf.keras.models.load_model("alz_classifier.keras")
+
+def predict_mri(img):
+    img = img.resize((128,128))
+    x = np.array(img)/255.0
+    x = np.expand_dims(x, axis=0)
+    
+    preds = model.predict(x)
+    pred_class = CLASS_NAMES[np.argmax(preds)]
+    return {pred_class: float(np.max(preds))}
+
+demo = gr.Interface(
+    fn=predict_mri,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=1),
+    title="Alzheimer & Brain Tumor 8-Class MRI Classifier",
+    description="Upload an MRI image to classify into 8 classes."
+)
+
+demo.launch()