import os
import requests
import subprocess
import gradio as gr

# Token Hugging Face từ biến môi trường
hf_token = os.getenv("HF_TOKEN")

# URLs cần tải
app_url = "https://huggingface.co/datasets/ArrcttacsrjksX/Deffusion/resolve/main/RunModelAppp/App/sdRundeffusiononhuggingfacemaster-ac54e00"
model_url = "https://huggingface.co/datasets/ArrcttacsrjksX/Deffusion/resolve/main/Model/realisticVisionV60B1_v51HyperVAE.safetensors"

# Đường dẫn lưu file
app_path = "sdRundeffusiononhuggingfacemaster-ac54e00"
model_path = "realisticVisionV60B1_v51HyperVAE.safetensors"

# Hàm tải file từ Hugging Face
def download_file(url, output_path, token):
    headers = {"Authorization": f"Bearer {token}"}
    response = requests.get(url, headers=headers, stream=True)
    response.raise_for_status()  # Kiểm tra lỗi
    with open(output_path, "wb") as f:
        for chunk in response.iter_content(chunk_size=8192):
            f.write(chunk)
    print(f"Downloaded: {output_path}")

# Tải các file nếu chưa tồn tại
if not os.path.exists(app_path):
    download_file(app_url, app_path, hf_token)
    subprocess.run(["chmod", "+x", app_path])  # Thay đổi quyền thực thi

if not os.path.exists(model_path):
    download_file(model_url, model_path, hf_token)

# Hàm xử lý chạy ứng dụng
def run_command(prompt, mode, height, width, steps, seed, init_image=None):
    try:
        # Lưu ảnh đầu vào nếu được cung cấp
        init_image_path = None
        if init_image:
            init_image_path = "input_image.png"
            init_image.save(init_image_path)

        # Chạy lệnh
        command = [
            f"./{app_path}",
            "-M", mode,
            "-m", model_path,
            "-p", prompt,
            "-H", str(height),
            "-W", str(width),
            "--steps", str(steps),
            "-s", str(seed),
        ]
        
        # Thêm ảnh đầu vào nếu có
        if mode == "img2img" and init_image_path:
            command.extend(["-i", init_image_path])
        
        result = subprocess.run(command, capture_output=True, text=True)

        # Kiểm tra kết quả và trả về
        if result.returncode == 0:
            output_path = "./output.png"  # Đường dẫn ảnh đầu ra mặc định
            return output_path if os.path.exists(output_path) else "Output image not found."
        else:
            return f"Error: {result.stderr}"
    except Exception as e:
        return str(e)

# Giao diện Gradio
with gr.Blocks() as demo:
    gr.Markdown("# Stable Diffusion Interface")
    
    # Cấu hình tham số
    with gr.Row():
        prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here", lines=2)
        mode = gr.Radio(choices=["txt2img", "img2img"], value="txt2img", label="Mode")
    
    with gr.Row():
        height = gr.Slider(128, 1024, value=512, step=64, label="Height (px)")
        width = gr.Slider(128, 1024, value=512, step=64, label="Width (px)")
    
    with gr.Row():
        steps = gr.Slider(1, 100, value=20, step=1, label="Steps")
        seed = gr.Slider(-1, 10000, value=42, step=1, label="Seed (-1 for random)")
    
    init_image = gr.Image(label="Initial Image (for img2img mode, optional)", type="pil", optional=True)
    output_path = gr.Textbox(label="Output Image Path or Logs", interactive=False)
    run_button = gr.Button("Run")

    # Kết nối hàm xử lý
    run_button.click(
        run_command,
        inputs=[prompt, mode, height, width, steps, seed, init_image],
        outputs=output_path,
    )

demo.launch()