init brain model

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+
+# Load the model
+model = tf.keras.models.load_model('model.h5')
+
+# Define the class names
+class_names = {
+    0: 'Glioma',
+    1: 'Menin',
+    2: 'Tumor'
+}
+
+
+{'brain_glioma': 0, 'brain_menin': 1, 'brain_tumor': 2}
+
+def classify_image(image):
+    # Preprocess the image
+    img_array = tf.image.resize(image, 200, 200])
+    img_array = tf.expand_dims(img_array, 0) / 255.0
+
+    # Make a prediction
+    prediction = model.predict(img_array)
+    predicted_class = tf.argmax(prediction[0], axis=-1)
+    confidence = np.max(prediction[0])
+
+    return class_names[predicted_class.numpy()], confidence
+
+
+iface = gr.Interface(
+    fn=classify_image,
+    inputs="image",
+    outputs=["text", "number"],
+    interpretation="default",
+    examples=[
+        ['examples/0.jpg'],
+        ['examples/1.jpg'],
+	['examples/2.jpg'],
+    ])
+iface.launch()

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4a27c67a7b3ad82c32f9a4025e9743f37e2ed8bc9bdc674873db2cd0e73e562a
+size 2915984

requirements.txt

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+tensorflow
+numpy
+pillow
+tensorflow_hub