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import gradio as gr
import torch
from torch import nn
from einops import rearrange
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import requests
import os
# ================================
# 1. Baixar pesos do Surya-1.0
# ================================
MODEL_URL = "https://huggingface.co/nasa-ibm-ai4science/Surya-1.0/resolve/main/surya.366m.v1.pt"
MODEL_FILE = "surya.366m.v1.pt"
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!")
download_model()
# ================================
# 2. Colar aqui a classe HelioSpectFormer
# ================================
# Copie todo o conteúdo que você me enviou da HelioSpectFormer aqui
# ⚠️ Substitua a seção abaixo pelo código real do repo
from surya.helio_spectformer import HelioSpectFormer # se você tiver a pasta surya local
# ================================
# 3. Instanciar o modelo com parâmetros padrão
# ================================
model = HelioSpectFormer(
img_size=224,
patch_size=16,
in_chans=1,
embed_dim=366,
time_embedding={"type": "linear", "time_dim": 1},
depth=8,
n_spectral_blocks=4,
num_heads=8,
mlp_ratio=4.0,
drop_rate=0.0,
window_size=7,
dp_rank=1,
learned_flow=False,
finetune=True
)
# Carregar pesos
state_dict = torch.load(MODEL_FILE, map_location=torch.device('cpu'))
model.load_state_dict(state_dict)
model.eval()
# ================================
# 4. Função de inferência para heatmap
# ================================
def infer_solar_image_heatmap(img):
# Pré-processamento da imagem
img_gray = img.convert("L").resize((224, 224))
ts_tensor = torch.tensor(np.array(img_gray), dtype=torch.float32).unsqueeze(0).unsqueeze(0).unsqueeze(2) / 255.0
batch = {"ts": ts_tensor, "time_delta_input": torch.zeros((1,1))}
# Inferência
with torch.no_grad():
outputs = model(batch)
# Pegar o embedding da saída
emb = outputs.squeeze().numpy()
heatmap = emb - emb.min()
heatmap /= heatmap.max() + 1e-8
# Criar figura do heatmap
plt.imshow(heatmap, cmap='hot')
plt.axis('off')
plt.tight_layout()
return plt.gcf()
# ================================
# 5. 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()
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