Create app.py

app.py

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+import gradio as gr
+from openvino.inference_engine import IECore
+import cv2
+import numpy as np
+from PIL import Image
+
+# Load the OpenVINO model
+model_xml = 'model.xml'
+model_bin = 'model.bin'
+
+ie = IECore()
+net = ie.read_network(model=model_xml, weights=model_bin)
+exec_net = ie.load_network(network=net, device_name="CPU")
+
+# Define the function for image processing
+def upscale_image(input_image):
+    # Convert Gradio PIL Image to numpy array
+    input_image = np.array(input_image)
+
+    # Preprocess the input image
+    image = cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR)
+    image = cv2.resize(image, (224, 224))
+    image = image / 255.0
+    image = np.transpose(image, (2, 0, 1))
+    image = image.reshape(1, 3, 224, 224)
+
+    # Run inference
+    outputs = exec_net.infer(inputs={'input': image})
+
+    # Post-process the output
+    output_image = outputs['output'][0]
+    output_image = np.transpose(output_image, (1, 2, 0))
+    output_image = np.clip(output_image, 0, 1) * 255
+    output_image = output_image.astype(np.uint8)
+    output_image = cv2.cvtColor(output_image, cv2.COLOR_RGB2BGR)
+    return output_image
+
+# Create the Gradio interface
+inputs = gr.inputs.Image(type="pil", label="Input Image")  # Use 'pil' type for uploaded images
+outputs = gr.outputs.Image(type="pil", label="Upscaled Image")
+
+title = "Image Upscaling App"
+description = "Upload an image and see the upscaled result."
+iface = gr.Interface(fn=upscale_image, inputs=inputs, outputs=outputs, title=title, description=description)
+
+# Launch the Gradio interface
+iface.launch()