app.py

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

# Load the trained model
model = tf.keras.models.load_model("brain_tumor_model.h5")

# Prediction function
def predict_tumor(image):
    image = image.resize((150, 150))
    image = np.expand_dims(np.array(image) / 255.0, axis=0)
    prediction = model.predict(image)[0][0]
    return "🧠 Tumor Detected" if prediction > 0.5 else "✅ No Tumor Detected"

# Gradio Interface
gr.Interface(
    fn=predict_tumor,
    inputs=gr.Image(type="pil"),
    outputs="text",
    title="🧠 Brain Tumor MRI Classifier",
    description="Upload a brain MRI scan to detect tumor presence.",
    allow_flagging="never"
).launch()