update model

app.py

@@ -7,7 +7,7 @@ from huggingface_hub import hf_hub_download
 
 def predict(img):
     img = img.astype(np.float32) / 255
-    s = 640
+    s = 768
     h, w = img.shape[:-1]
     h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
     ph, pw = s - h, s - w
@@ -15,14 +15,14 @@ def predict(img):
     img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h))
     img_input = np.transpose(img_input, (2, 0, 1))
     img_input = img_input[np.newaxis, :]
-    pred = model.run(None, {"img": img_input})[0][0]
-    return {"not best": pred[0].item(), "best": pred[1].item()}
+    pred = model.run(None, {"img": img_input})[0].item()
+    return f"{pred}"
 
 
 if __name__ == "__main__":
-    model_path = hf_hub_download(repo_id="skytnt/anime_quality", filename="classifier.onnx")
+    model_path = hf_hub_download(repo_id="skytnt/anime-aesthetic", filename="model.onnx")
     model = rt.InferenceSession(model_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
     examples = [[f"examples/{x:02d}.jpg"] for x in range(0, 2)]
-    app = gr.Interface(predict, gr.Image(label="input image"), gr.Label(label="result"),title="Best Anime or Not",
+    app = gr.Interface(predict, gr.Image(label="input image"), gr.Textbox(label="score"),title="Anime Aesthetic Predict",
                        allow_flagging="never", examples=examples)
     app.launch()