Add app and pth

1024_MLP_best-MSE4.1636_ep75.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:617a1c3fe8cfdcfb79fa0df3d46d4673497ca47a8ee43ddd6d6a5027a478ec64
+size 3716120

app.py

@@ -0,0 +1,36 @@
+import gradio as gr
+import torch
+from PIL import Image
+from torchvision.transforms import functional as F
+from typing import List
+from transformers import CLIPModel, CLIPProcessor
+
+# Load the pre-trained model
+model_path = "1024_MLP_best-MSE4.1636_ep75.pth"
+model = torch.load(model_path)
+model.eval()
+
+# Load the CLIP model and processor
+clip_model = CLIPModel.from_pretrained("ViT-L/14")
+clip_processor = CLIPProcessor.from_pretrained("ViT-L/14")
+
+# Define the prediction function
+def predict(images: List[Image.Image]) -> float:
+    image_tensors = [F.to_tensor(img) for img in images]
+    inputs = clip_processor(images=image_tensors, return_tensors="pt", padding=True)
+    with torch.no_grad():
+        outputs = model(inputs.pixel_values)
+    scores = outputs.clamp(0, 10).cpu().numpy().reshape(-1).tolist()
+    return scores
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs="image",
+    outputs="number",
+    title="Kemono Aesthetic Scorer",
+    description="Predict the score of a kemono based on aesthetic features.",
+)
+
+# Run the Gradio interface
+iface.launch()