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app.py

@@ -1,30 +1,46 @@
 import gradio as gr
 import onnxruntime as ort
+import numpy as np
+from PIL import Image
 
 # Load the ONNX model
-ort_session = ort.InferenceSession("superresolution.onnx")
-
-# Define the prediction function
-def predict(image):
-  # Preprocess image (resize, normalization)
-  # ... (Implement your pre-processing logic here)
-  
-  # Convert image to input format for ONNX model
-  # ... (Convert image to tensor or format expected by the model)
-  
+model_path = "modelSR.onnx"  # Replace with your model path
+ort_session = ort.InferenceSession(model_path)
+
+# Define the superresolution function
+def superresolve(image):
+  # Preprocess the image
+  image = np.array(image.resize((256, 256)))  # Resize to expected input size
+  image = image[:, :, ::-1]  # Convert to BGR (some models expect this)
+  image = np.transpose(image, (2, 0, 1))  # Transpose to NHWC format
+  image = image[np.newaxis, ...]  # Add batch dimension
+
   # Run inference
-  outputs = ort_session.run(None, {"input": input_data})
-  
-  # Postprocess output (denormalization, resizing)
-  # ... (Implement your post-processing logic here)
-  
-  # Return the enhanced image
-  return enhanced_image
+  ort_inputs = {ort_session.get_inputs()[0].name: image.astype(np.float32)}
+  ort_outs = ort_session.run(None, ort_inputs)
+  output = ort_outs[0][0]
+
+  # Postprocess the output
+  output = output.transpose(1, 2, 0)  # Transpose back to HWC
+  output = np.clip(output, 0, 1)  # Clip values to [0, 1]
+  output = (output * 255).astype(np.uint8)  # Convert to uint8 for PIL
+  output = Image.fromarray(output[:, :, ::-1])  # Convert back to RGB
+
+  return output
 
 # Define the Gradio interface
-inputs = gr.Image(label="Input Image")
-outputs = gr.Image(label="Enhanced Image")
-interface = gr.Interface(fn=predict, inputs=inputs, outputs=outputs)
+interface = gr.Interface(
+  fn=superresolve,
+  inputs="image",
+  outputs="image",
+  title="Super Resolution",
+  description="Upload an image to upscale its resolution.",
+  allow_flagging=False,
+  thumbnail="https://i.imgur.com/nXhVq3y.png",  # Optional thumbnail image
+  theme="huggingface",  # Choose a theme (optional)
+)
 
 # Launch the Gradio app
-launch(interface, debug=True)
+interface.launch(server_port=12345)  # Change port if needed
+
+print("App launched! Visit http://localhost:12345 to use it.")

requirements.txt.bak

@@ -0,0 +1 @@
+onnxruntime