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import gradio as gr
from pathlib import Path
import os
from PIL import Image
import torch
import torchvision.transforms as transforms
import requests
# Function to download the model from Google Drive
def download_file_from_google_drive(id, destination):
URL = "https://drive.google.com/uc?export=download"
session = requests.Session()
response = session.get(URL, params={'id': id}, stream=True)
token = get_confirm_token(response)
if token:
params = {'id': id, 'confirm': token}
response = session.get(URL, params=params, stream=True)
save_response_content(response, destination)
def get_confirm_token(response):
for key, value in response.cookies.items():
if key.startswith('download_warning'):
return value
return None
def save_response_content(response, destination):
CHUNK_SIZE = 32768
with open(destination, "wb") as f:
for chunk in response.iter_content(CHUNK_SIZE):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
# Replace 'YOUR_FILE_ID' with your actual file ID from Google Drive
file_id = '1WJ33nys02XpPDsMO5uIZFiLqTuAT_iuV'
destination = 'ema_ckpt_cond.pt'
download_file_from_google_drive(file_id, destination)
# Preprocessing
from modules import PaletteModelV2
from diffusion import Diffusion_cond
device = 'cuda'
model = PaletteModelV2(c_in=2, c_out=1, num_classes=5, image_size=256, true_img_size=64).to(device)
ckpt = torch.load(destination, map_location=device)
model.load_state_dict(ckpt)
diffusion = Diffusion_cond(noise_steps=1000, img_size=256, device=device)
model.eval()
transform_hmi = transforms.Compose([
transforms.ToTensor(),
transforms.Resize((256, 256)),
transforms.RandomVerticalFlip(p=1.0),
transforms.Normalize(mean=(0.5,), std=(0.5,))
])
def generate_image(seed_image):
seed_image_tensor = transform_hmi(Image.open(seed_image)).reshape(1, 1, 256, 256).to(device)
generated_image = diffusion.sample(model, y=seed_image_tensor, labels=None, n=1)
generated_image_pil = transforms.ToPILImage()(generated_image.squeeze().cpu())
return generated_image_pil
# Create Gradio interface
iface = gr.Interface(
fn=generate_image,
inputs="file",
outputs="image",
title="Magnetogram-to-Magnetogram: Generative Forecasting of Solar Evolution",
description="Upload a LoS magnetogram and predict how it is going to be in 24 hours."
)
iface.launch()
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