app.py

import gradio as gr
import torch
from torchvision import transforms
from PIL import Image

# Load class names
with open("classes.txt", "r", encoding="utf-8") as f:
    CLASSES = [line.strip() for line in f if line.strip()]

device = torch.device("cpu")

# Load trained model
model_path = "best_model.pth"
model = torch.load(model_path, map_location=device)
model.eval()

# Preprocessing for input images
preprocess = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]
    ),
])

def predict(image: Image.Image):
    if image is None:
        return {"error": 1.0}

    image = image.convert("RGB")
    x = preprocess(image).unsqueeze(0).to(device)

    with torch.no_grad():
        logits = model(x)
        probs = torch.softmax(logits, dim=1)[0]

    # Build {label: prob} dict for Gradio Label output
    return {
        CLASSES[i]: float(probs[i])
        for i in range(len(CLASSES))
    }

demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil", label="Upload a game cover"),
    outputs=gr.Label(num_top_classes=3, label="Predicted genre"),
    title="Video Game Genre Predictor",
    description="Upload a video game cover to predict its genre."
)

if __name__ == "__main__":
    demo.launch()