Add app.py

app.py

@@ -0,0 +1,36 @@
+import onnxruntime
+import gradio as gr
+import numpy as np
+from PIL import Image
+
+# Load the ONNX model
+onnx_model_path = "sar2rgb.onnx"
+sess = onnxruntime.InferenceSession(onnx_model_path)
+
+# Function to process the input and make predictions
+def predict(input_image):
+    # Preprocess the input image (e.g., resize, normalize)
+    input_image = input_image.resize((256, 256))  # Adjust size as needed
+    input_image = np.array(input_image).transpose(2, 0, 1)  # HWC to CHW
+    input_image = input_image.astype(np.float32) / 255.0  # [0,1]
+    input_image = (input_image - 0.5) / 0.5               # [-1,1] 
+    input_image = np.expand_dims(input_image, axis=0)  # Add batch dimension
+    
+    # Run the model
+    inputs = {sess.get_inputs()[0].name: input_image}
+    output = sess.run(None, inputs)
+    
+    # Post-process the output image (if necessary)
+    output_image = output[0].squeeze().transpose(1, 2, 0)  # CHW to HWC
+    output_image = (output_image + 1) / 2 # [0,1]
+    output_image = (output_image * 255).astype(np.uint8)  # Denormalize [0,255]
+    
+    return Image.fromarray(output_image)
+
+# Create Gradio interface
+iface = gr.Interface(fn=predict, 
+                     inputs=gr.Image(type="pil"), 
+                     outputs=gr.Image(type="pil"))
+
+# Launch the interface
+iface.launch()

requirements.txt

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+gradio==5.5.0
+numpy==2.1.3
+onnxruntime==1.16.3
+Pillow==10.1.0
+Pillow==11.0.0