Create app.py

app.py

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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
+
+# 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})"
+
+# Preloaded images for testing
+sample_images = [
+    ("fracture1.jpg", "Fractured Example"),
+    ("fracture2.jpg", "Fractured Example"),
+    ("normal1.jpg", "Normal Example"),
+    ("normal2.jpg", "Normal Example"),
+]
+
+# 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="""
+    <h1 style='color: blue;'>Bone Fracture Detection Model</h1>
+    <p>This AI model predicts whether a given X-ray image shows a fracture or not.</p>
+    <p><b>Upload an image</b> or select from the provided samples to get a prediction.</p>
+    """,
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch()