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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()
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