app.py

import gradio as gr
import tensorflow as tf
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input
import numpy as np

# Load the model
model_path = 'fine_tuned_resnet50_brain_tumor.h5'
model = tf.keras.models.load_model(model_path)

def predict_image(img):
    img = img.resize((224, 224))  # Resize the image to match the model input size
    img_array = np.array(img)  # Convert image to numpy array
    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
    img_array = preprocess_input(img_array)  # Preprocess the image
    prediction = model.predict(img_array)  # Predict
    predicted_class = np.round(prediction).astype(int)[0][0]
    return "Brain Tumor" if predicted_class == 0 else "Healthy"

# Create Gradio interface
iface = gr.Interface(
    fn=predict_image,
    inputs=gr.Image(type="pil"),
    outputs=gr.Text()
)

# Launch the Gradio app and create a public link
iface.launch(share=True)