Create app.py

app.py

@@ -0,0 +1,56 @@
+import gradio as gr
+from tensorflow.keras.models import load_model
+from tensorflow.keras.layers import DepthwiseConv2D
+from PIL import Image, ImageOps
+import numpy as np
+
+# Disable scientific notation for clarity
+np.set_printoptions(suppress=True)
+
+# Custom object for DepthwiseConv2D
+custom_objects = {'DepthwiseConv2D': DepthwiseConv2D}
+
+# Load the model with custom objects
+model = load_model("model/pleasuredomes_image_model.h5", custom_objects=custom_objects, compile=False)
+
+# Load the labels
+class_names = open("model/labels.txt", "r").readlines()
+
+def predict_image(image):
+    """
+    Function to process the image and make a prediction using the loaded model.
+    """
+    # Resize the image to be at least 224x224 and then crop from the center
+    size = (224, 224)
+    image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)
+
+    # Turn the image into a numpy array
+    image_array = np.asarray(image)
+
+    # Normalize the image
+    normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
+
+    # Create the array of the right shape to feed into the keras model
+    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+    data[0] = normalized_image_array
+
+    # Predict the model
+    prediction = model.predict(data)
+    index = np.argmax(prediction)
+    class_name = class_names[index].strip()
+    confidence_score = prediction[0][index]
+
+    return f"Class: {class_name}, Confidence Score: {confidence_score:.2f}"
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.inputs.Image(type="pil"),
+    outputs="text",
+    title="Image Classification",
+    description="Upload an image to classify it using the pre-trained model."
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    interface.launch()