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import gradio as gr
import tensorflow as tf
import numpy as np
from tensorflow.keras.preprocessing import image
from PIL import Image
import os
# Load the trained model
model = tf.keras.models.load_model("my_keras_model.h5")
# Define image size based on the model's input requirement
image_size = (224, 224)
# Function to make predictions
def predict_image(img):
img = img.resize(image_size) # Resize image to model's expected size
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0) / 255.0 # Normalize
prediction = model.predict(img_array)
# Assuming binary classification (fractured or normal)
class_names = ['Fractured', 'Normal']
predicted_class = class_names[int(prediction[0] > 0.5)] # Threshold at 0.5
return f"Prediction: {predicted_class} (Confidence: {prediction[0][0]:.2f})"
# Get image paths dynamically
sample_images_dir = "samples"
sample_images = [os.path.join(sample_images_dir, f) for f in os.listdir(sample_images_dir) if f.endswith(('.jpg', '.png'))]
# Define Gradio Interface
interface = gr.Interface(
fn=predict_image,
inputs=gr.Image(type="pil"),
outputs=gr.Textbox(),
examples=sample_images, # Preloaded images for testing
title="Bone Fracture Detection",
description="Upload an X-ray image or select a sample image to check for fractures."
)
# Launch the Gradio app
if __name__ == "__main__":
interface.launch() |