Flask Backend Code

app.py

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+import gradio as gr
+from huggingface_hub import from_pretrained_keras
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import io
+
+# Load pre-trained deblurring model
+model = from_pretrained_keras("google/maxim-s3-deblurring-reds")
+
+def deblur_image(input_image):
+    # Preprocess the input image
+    image = np.array(input_image)
+    image = tf.convert_to_tensor(image)
+    image = tf.image.resize(image, (256, 256))
+    
+    # Predict with the model
+    predictions = model.predict(tf.expand_dims(image, 0))
+    
+    # Convert back to image
+    output_image = predictions[0].numpy().astype(np.uint8)
+    output_image = Image.fromarray(output_image)
+    
+    # Save the result in memory as a byte array
+    byte_arr = io.BytesIO()
+    output_image.save(byte_arr, format='PNG')
+    byte_arr.seek(0)
+    
+    return byte_arr
+
+# Set up the Gradio interface
+iface = gr.Interface(fn=deblur_image,
+                     inputs=gr.inputs.Image(type="pil"),
+                     outputs=gr.outputs.Image(type="file"),
+                     live=True)
+
+# Launch the Gradio interface
+iface.launch()