Create app.py

app.py

@@ -0,0 +1,57 @@
+import numpy as np
+import tensorflow as tf
+import onnxruntime as ort
+import gradio as gr
+import time
+import logging
+from PIL import Image
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+model_path = "model.onnx"
+
+try:
+    start_time = time.perf_counter()
+    model = ort.InferenceSession(model_path, providers=["CUDAExecutionProvider", "CPUExecutionProvider"])
+    logger.info(f"Model successfully loaded from {model_path} in {time.perf_counter()-start_time:.2f} sec")
+
+except Exception as e:
+    logger.error(f"Failed to load model from {model_path}: {e}")
+
+def preprocess_image(image):
+    "Convert to rgb, normalize and add batch dimension"
+    img = Image.open(image).convert("RGB")
+    img_arr = np.array(img, dtype=np.float32) / 255.0
+    img_arr = np.expand_dims(img_arr, axis=0)
+
+    return img_arr
+
+def super_resolution_image(image):
+    try:
+        model_inputs = model.get_inputs()[0].name
+        model_outputs = model.get_outputs()[0].name
+        # Inference
+        sr_img = model.run([model_outputs], {model_inputs: image})[0]
+        # Postprocess
+        sr_img = (np.clip(sr_img, 0, 1)*255).astype(np.uint8)
+        return Image.fromarray(sr_img[0])
+    
+    except Exception as e:
+        raise RuntimeError(f"Model inference failed, {str(e)}")
+    
+def gradio_inference(image):
+    img_arr = preprocess_image(image)
+    sr_img = super_resolution_image(img_arr)
+    return sr_img
+
+demo = gr.Interface(
+    fn=gradio_inference,
+    inputs=gr.Image(type="filepath"),  
+    outputs="image",
+    title="EDSR 4x Super-Resolution",
+    description="Upscale your images 4× using an onnx edsr model"
+)
+
+if __name__ == "__main__":
+    demo.launch()