app.py

import gradio as gr
import torch
from huggingface_hub import hf_hub_download
import json
from omegaconf import OmegaConf
import sys
import os
from PIL import Image
import torchvision.transforms as transforms

photos_folder = "Photos"

# Pobierz model i config
repo_id = "Kiwinicki/sat2map-generator"
generator_path = hf_hub_download(repo_id=repo_id, filename="generator.pth")
config_path = hf_hub_download(repo_id=repo_id, filename="config.json")
model_path = hf_hub_download(repo_id=repo_id, filename="model.py")

# Dodaj ścieżkę do modelu
sys.path.append(os.path.dirname(model_path))
from model import Generator

# Załaduj konfigurację
with open(config_path, "r") as f:
    config_dict = json.load(f)
cfg = OmegaConf.create(config_dict)

# Inicjalizacja modelu
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
generator = Generator(cfg).to(device)
generator.load_state_dict(torch.load(generator_path, map_location=device))
generator.eval()

# Transformacje
transform = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])

def process_image(image):
    # Konwersja do tensora
    image_tensor = transform(image).unsqueeze(0).to(device)
    
    # Inferencja
    with torch.no_grad():
        output_tensor = generator(image_tensor)
    
    # Przygotowanie wyjścia
    output_image = output_tensor.squeeze(0).cpu()
    output_image = output_image * 0.5 + 0.5  # Denormalizacja
    output_image = transforms.ToPILImage()(output_image)
    
    return output_image


def load_images_from_folder(folder):
    images = []
    for filename in os.listdir(folder):
        if filename.lower().endswith(('.png', '.jpg', '.jpeg')):
            img_path = os.path.join(folder, filename)
            img = Image.open(img_path)
            images.append((img, filename))
    return images



def load_image_from_gallery(images, index):
    if images and 0 <= index < len(images):
        image = images[index]
        if isinstance(image, tuple):
            image = image[0]
        return image
    return None


def gallery_click_event(images, evt: gr.SelectData):
    index = evt.index
    selected_img = load_image_from_gallery(images, index)
    return selected_img


def clear_image():
    return None


def app():
    images = load_images_from_folder(photos_folder)

    with gr.Blocks(css=".container { background-color: white; }") as demo:
        with gr.Row():
            with gr.Column():
                selected_image = gr.Image(label="Input Image", type="pil")
                clear_button = gr.Button("Clear")

            with gr.Column():
                image_gallery = gr.Gallery(label="Image Gallery", elem_id="gallery", type="pil", value=[img for img, _ in images])

            with gr.Column():
                result_image = gr.Image(label="Result Image", type="pil")

        image_gallery.select(
            fn=gallery_click_event,
            inputs=image_gallery,
            outputs=selected_image
        )

        selected_image.change(
            fn=process_image,
            inputs=selected_image,
            outputs=result_image
        )

        clear_button.click(
            fn=clear_image,
            inputs=None,
            outputs=selected_image
        )

    demo.launch()


if __name__ == "__main__":
    app()