Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+from keras.models import load_model
+import tensorflow as tf
+
+# Load the trained model
+model = load_model('brain_tumor_model.h5')
+
+# Define the prediction function
+def predict_image(image):
+    # Resize the image to match training input
+    img = image.resize((128, 128))
+
+    # Convert the image to a NumPy array
+    img_array = np.array(img)
+
+    # Handle grayscale images
+    if img_array.ndim == 2:
+        img_array = np.stack((img_array,) * 3, axis=-1)
+    elif img_array.shape[2] == 4:
+        # Convert RGBA to RGB
+        img_array = img_array[..., :3]
+
+    # Normalize the image
+    img_array = img_array / 255.0
+
+    # Expand dimensions to match model input
+    img_array = np.expand_dims(img_array, axis=0)
+
+    # Make prediction
+    prediction = model.predict(img_array)
+
+    # Interpret the prediction
+    predicted_class = np.argmax(prediction, axis=1)[0]
+    confidence = prediction[0][predicted_class]
+
+    if predicted_class == 0:
+        result = f'No tumor detected with confidence {confidence:.2%}.'
+    else:
+        result = f'Tumor detected with confidence {confidence:.2%}.'
+
+    return result
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.inputs.Image(type="pil"),
+    outputs="text",
+    title="Brain Tumor Detection AI App",
+    description="Upload a brain MRI image to detect the presence of a tumor.",
+    examples=[
+        ["example_images/no_tumor_example.jpg"],
+        ["example_images/yes_tumor_example.jpg"],
+    ],
+)
+
+if __name__ == "__main__":
+    interface.launch()