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app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import os
+
+# Configuration
+MODEL_PATH = "best_model.keras"
+IMG_SIZE = (299, 299)
+CLASSES = ['glioma', 'meningioma', 'notumor', 'pituitary']
+
+# Load model
+print(f"Loading model from {MODEL_PATH}...")
+try:
+    if os.path.exists(MODEL_PATH):
+        model = tf.keras.models.load_model(MODEL_PATH)
+        print("Model loaded successfully.")
+    else:
+        print("Error: Model file not found.")
+        model = None
+except Exception as e:
+    print(f"Failed to load model: {e}")
+    model = None
+
+def predict(image):
+    if model is None:
+        return "Model not loaded. Please check the logs."
+
+    try:
+        # Preprocess image
+        image = image.resize(IMG_SIZE)
+        img_array = np.array(image)
+        img_array = img_array.astype(np.float32) / 255.0
+        img_batch = np.expand_dims(img_array, axis=0)
+
+        # Predict
+        predictions = model.predict(img_batch, verbose=0)
+        probs = predictions[0]
+
+        # Format results
+        results = {CLASSES[i]: float(probs[i]) for i in range(len(CLASSES))}
+        return results
+    except Exception as e:
+        return f"Error: {e}"
+
+# Build Gradio Interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=4),
+    title="MRI Brain Tumor Classification",
+    description="Upload an MRI scan to classify it into one of four categories: Glioma, Meningioma, No Tumor, or Pituitary.",
+    theme="soft"
+)
+
+if __name__ == "__main__":
+    interface.launch()