import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image

model = tf.keras.models.load_model("brain_mri_model.keras")

class_names = [
"MildDemented",
"ModerateDementia",
"NonDemented",
"VeryMildDementia",
"Glioma",
"Meningioma",
"NoTumor",
"Pituitary"
]

IMG_SIZE = (224,224)

def predict(image):

    img = image.resize(IMG_SIZE)

    img = np.array(img)/255.0
    img = np.expand_dims(img,0)

    pred = model.predict(img)

    idx = np.argmax(pred)
    conf = np.max(pred)

    return f"{class_names[idx]} | Confidence: {conf:.2f}"

demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs="text",
    title="Brain MRI Disease Detection"
)

demo.launch()