gradio-interface.py

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

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Animator2D().to(device)
model.load_state_dict(torch.load("animator2D-model.pth", map_location=device))
model.eval()

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

def generate_sprite(num_frames, description, action, direction):
    text = f"{num_frames}-frame sprite animation of: {description}, that: {action}, facing: {direction}"
    encoded_text = tokenizer(
        text, padding="max_length", max_length=128, truncation=True, return_tensors="pt"
    )
    
    with torch.no_grad():
        text_ids = encoded_text['input_ids'].to(device)
        text_mask = encoded_text['attention_mask'].to(device)
        generated_sprite = model(text_ids, text_mask).cpu().squeeze(0)
    
    generated_sprite = (generated_sprite + 1) / 2  # Denormalizzazione
    generated_sprite = transforms.ToPILImage()(generated_sprite)
    return generated_sprite

iface = gr.Interface(
    fn=generate_sprite,
    inputs=[
        gr.Number(label="Numero di Frame", value=17),
        gr.Textbox(label="Descrizione dello Sprite"),
        gr.Dropdown(["cammina", "corre", "salta", "attacca"], label="Azione"),
        gr.Dropdown(["Nord", "Sud", "Est", "Ovest"], label="Direzione")
    ],
    outputs=gr.Image(type="pil"),
    title="Animator2D Generator",
    description="Genera animazioni di sprite basate su descrizioni testuali."
)

iface.launch(share=False)  # Disabilita la condivisione pubblica