Create app.py

app.py

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+import numpy as np
+from tensorflow.keras.preprocessing import image
+import gradio as gr
+import numpy as np
+from tensorflow.keras.preprocessing import image
+import gradio as gr
+import keras
+
+model = keras.models.load_model('x_ray.keras')
+
+# Define the function for image classification
+def classify_image(img):
+    # Set the input image dimensions
+    img_width, img_height = 150, 150
+
+    # Resize the image to match the model's input shape
+    img = img.resize((img_width, img_height))
+
+    # Convert the image to a numpy array
+    img = np.array(img)
+    img = img.astype('float32') / 255.0
+    img = np.expand_dims(img, axis=0)
+
+    # Get the prediction
+    prediction = model.predict(img)
+
+    return "NOT fractured" if prediction > 0.5 else "fractured"
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.inputs.Image(type="pil", label="Upload an X-ray image"),
+    outputs="text",
+    title="Bone Fracture Classification",
+    description="Upload an X-ray image, and this model will classify it as fractured or not.",
+)
+
+# Start the Gradio interface
+iface.launch()