gradio wrapper around predictions

vggtunedgr.py

import tensorflow as tf
from tensorflow.keras.models import load_model
import gradio as gr
import numpy as np
from PIL import Image

# Load the saved model
model = load_model("modelVGG16.h5")

# Define the prediction function
def predict_bone_fracture(img):
    img = img.resize((256, 256))
    img = np.array(img) / 255.0
    img = np.expand_dims(img, axis=0)
    prediction = model.predict(img)[0][0]
    return "Broken" if prediction > 0.5 else "Not Broken"

# Create Gradio interface
interface = gr.Interface(
    fn=predict_bone_fracture,
    inputs=gr.inputs.Image(type="pil"),
    outputs="text",
    title="Bone Fracture Detection",
    description="Upload an X-ray image to check if there is a bone fracture."
)

# Launch the Gradio app
interface.launch()