Upload 3 files

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.applications.inception_v3 import preprocess_input
+from tensorflow.keras.preprocessing.image import img_to_array
+
+# Load the trained model
+model = tf.keras.models.load_model("best_model.h5")
+
+# Original class names from directory
+class_names = ["no", "yes"]
+
+# Mapping to user-friendly labels
+label_mapping = {
+    "no": "No Tumor",
+    "yes": "Yes, that's a Brain Tumor"
+}
+
+# Prediction function
+def predict(image):
+    image = image.resize((224, 224))
+    image = img_to_array(image)
+    image = np.expand_dims(image, axis=0)
+    image = preprocess_input(image)
+
+    preds = model.predict(image)[0]
+    label_idx = np.argmax(preds)
+    raw_label = class_names[label_idx]
+    readable_label = label_mapping[raw_label]
+    confidence = float(preds[label_idx])
+
+    return {readable_label: confidence}
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=2),
+    title="🧠 Brain Tumor Detection",
+    description="Upload an MRI image to detect if it has a brain tumor using InceptionV3."
+)
+
+if __name__ == "__main__":
+    interface.launch()

best_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ca23e7da4b6b7c526471438a046444712c22488eb11ddcb29763280d3af7a308
+size 142858160

reqruirements.txt

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+tensorflow
+gradio
+numpy
+pillow