update

app.py

@@ -1,7 +1,10 @@
 import gradio as gr
+import spaces
+from core import IQA
+metric = IQA("nima-vgg16-ava")
 
-def greet(name):
-    return "Hello " + name + "!!"
+def greet(image):
+    return metric(image).item()
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+demo = gr.Interface(fn=greet, inputs=gr.Image(format="png", image_mode="RGB", type="pil"), outputs=gr.Textbox())
 demo.launch()

core.py

@@ -0,0 +1,23 @@
+import spaces
+import pyiqa
+import torch
+
+class IQA:
+    def __init__(self, model_name="nima"):
+        device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+        self.model = pyiqa.create_metric(model_name, device=device)
+        print(self.model)
+    def __call__(self, image_path):
+        return self.model(image_path)
+
+if __name__ == "__main__":
+    import requests
+    from PIL import Image
+    import glob
+    image_files = glob.glob("samples/*")
+    iqa_metric = IQA(model_name="nima-vgg16-ava")
+    for image_file in image_files:
+        print(image_file)
+        image = Image.open(image_file)
+        score = iqa_metric(image)
+        print(score)

requirements.txt

@@ -0,0 +1 @@
+pyiqa