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"initial"

.gitignore

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+venv
+model

app.py

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+import gradio as gr
+from predict import makepredictions
+
+with gr.Blocks() as demo:
+    gr.Markdown("Brain Tumor Image classification")
+    gr.Markdown("We detect 4 Types of Tumor with 99.12% Accuracy:")
+    gr.Markdown("Glioma  |   Meningioma  |   No Tumor  |   Pituitary")
+    image_input = gr.Image(label='Tumor Image', type='filepath', height=480)
+    with gr.Row():
+        image_button = gr.Button("Predict")
+        y_pred = output = gr.Textbox(label="Tumor Type:")
+        image_button.click(makepredictions, inputs=image_input, outputs=y_pred)
+    gr.Markdown(
+        """
+           This model is trained on the Kaggle dataset:
+                  - Only for the educational purpose
+                  - Play with that and Have fun.
+        """)
+
+if __name__ == "__main__":
+    demo.launch()

predict.py

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+import tensorflow as tf
+import tf_keras
+model_mri = tf_keras.models.load_model('model')
+
+def load_image_with_path(path):
+    img = tf.io.read_file(path)
+    img = tf.image.decode_image(img, channels=3)
+    img = tf.image.resize(img, size=[256, 256])
+    img = img / 255.
+    return img
+
+def makepredictions(path):
+    print(path)
+    img = load_image_with_path(path)
+    predictions = model_mri.predict(tf.expand_dims(img, axis=0))
+    a = int(tf.argmax(tf.squeeze(predictions)))
+    if a == 0:
+        a = "Result : Glioma Tumor"
+    elif a == 1:
+        a = "Result : Meningioma Tumor"
+    elif a == 2:
+        a = "Result : No Tumor"
+    else:
+        a = "Result : Pituitary Tumor"
+    return a
+    # {'glioma': 0, 'meningioma': 1, 'notumor': 2, 'pituitary': 3}