app.py

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()