app.py

import gradio as gr
import torch
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import requests
import os

# URLs dos arquivos do modelo
MODEL_URL = "https://huggingface.co/nasa-ibm-ai4science/Surya-1.0/resolve/main/surya.366m.v1.pt"

# Nome local do arquivo
MODEL_FILE = "surya.366m.v1.pt"

# Função para baixar o modelo se não existir
def download_model():
    if not os.path.exists(MODEL_FILE):
        print("Baixando pesos do Surya-1.0...")
        r = requests.get(MODEL_URL)
        with open(MODEL_FILE, "wb") as f:
            f.write(r.content)
        print("Download concluído!")

# Baixar modelo
download_model()

# Carregar modelo PyTorch
model = torch.load(MODEL_FILE, map_location=torch.device('cpu'))
model.eval()

# Função para gerar heatmap
def infer_solar_image_heatmap(img):
    # Pré-processamento: grayscale, resize 224x224
    img = img.convert("L").resize((224, 224))
    img_tensor = torch.tensor(np.array(img), dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.0

    with torch.no_grad():
        outputs = model(img_tensor)
    
    # Criar heatmap
    emb = outputs.squeeze().numpy()
    heatmap = emb - emb.min()
    heatmap /= heatmap.max() + 1e-8  # normalização 0-1

    plt.imshow(heatmap, cmap='hot')
    plt.axis('off')
    plt.tight_layout()
    return plt.gcf()

# Interface Gradio
interface = gr.Interface(
    fn=infer_solar_image_heatmap,
    inputs=gr.Image(type="pil"),
    outputs=gr.Plot(label="Heatmap do embedding Surya"),
    title="Playground Surya-1.0 com Heatmap",
    description="Upload de imagem solar → visualize heatmap gerado pelo Surya-1.0"
)

interface.launch()