app.py

import torch
from torch import nn
from transformers import AutoImageProcessor, AutoModel
import gradio as gr
import numpy as np
from PIL import Image

# Nome do modelo no Hugging Face Hub
MODEL_NAME = "facebook/dinov2-small"

# Carregando processador e modelo
processor = AutoImageProcessor.from_pretrained(MODEL_NAME)
model = AutoModel.from_pretrained(MODEL_NAME)

# Projeção para 512D (caso a saída seja >512, reduzimos)
projection = nn.Linear(model.config.hidden_size, 512)

def get_embedding(image: Image.Image):
    # Preprocessamento
    inputs = processor(images=image, return_tensors="pt")
    
    with torch.no_grad():
        outputs = model(**inputs)
        # Usando o CLS token como embedding da imagem
        last_hidden_state = outputs.last_hidden_state  # (batch, seq_len, hidden)
        embedding = last_hidden_state[:, 0]  # pegando o [CLS] token

        # Projeta para 512D
        embedding_512 = projection(embedding)

    # Converte para lista Python
    return embedding_512.squeeze().tolist()

# Cria API com Gradio (sem interface visual, apenas endpoint)
iface = gr.Interface(
    fn=get_embedding,
    inputs=gr.Image(type="pil"),
    outputs=gr.JSON(),
    live=False,
    api_name="embed"  # endpoint em /embed
)

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