Initial commit

app.py

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+import gradio as gr
+import tensorflow as tf 
+import numpy as np
+from PIL import Image
+
+interpreter = tf.lite.Interpreter(model_path = "cnn.tflite")
+interpreter.allocate_tensors()
+input_details  = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+CLASSES = ["Glioma", "Meningioma", "No Tumor", "Pituitary"]
+IMG_SIZE = (224, 224)
+
+def preprocess(img: Image.Image) -> np.ndarray:
+    img = img.resize(IMG_SIZE)
+    arr = np.asarray(img, dtype=np.float32) / 255.0
+    return np.expand_dims(arr, 0)      
+
+def predict(image):
+    x = preprocess(image)
+    interpreter.set_tensor(input_details[0]["index"], x)
+    interpreter.invoke()
+    probs = interpreter.get_tensor(output_details[0]["index"])
+    return CLASSES[int(np.argmax(probs, axis=1)[0])]
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Brain MRI"),
+    outputs=gr.Label(num_top_classes=4),
+    title="Brain‑Tumor Classifier (.tflite)",
+    description="Returns: Glioma, Meningioma, No Tumor, Pituitary"
+)
+
+if __name__ == "__main__":
+    demo.launch()

cnn.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d621e23d2e705ed9a44696e02be7d93d312ba6ed3e21360e3795973cde31fb49
+size 51427444

requirements.txt

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+tensorflow==2.15.0    
+gradio>=4.31,<5
+pillow
+numpy