import gradio as gr
from gradio import inputs
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
from keras.applications.imagenet_utils import preprocess_input


# Load the model
model = load_model('best_model.h5')

def classify_image(inp):
    inp = inp.reshape((-1, 224, 224, 3))
    inp = preprocess_input(inp)
    prediction = model.predict(inp).flatten()
    return {"Brain Tumor not Detected": float(prediction[0]), "Brain Tumor Detected": float(prediction[1])}

image = gr.inputs.Image(shape=(224, 224))

gr.Interface(fn=classify_image, inputs=image, outputs="label").launch()