import base64, io, random, torch, gradio as gr
from diffusers import DiffusionPipeline
from PIL import Image

MODEL_ID = "JingyeChen22/textdiffuser2-full-ft"

device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if device == "cuda" else torch.float32

pipe = DiffusionPipeline.from_pretrained(MODEL_ID, torch_dtype=dtype).to(device)

def gen_image(prompt, main_text, sub_text, negative_prompt, width, height, steps, guidance, seed):
    # seed
    if seed is None or str(seed).strip() == "":
        seed = random.randint(1, 2**31 - 1)
    g = torch.Generator(device=device).manual_seed(int(seed))

    # prompt structuré pour TextDiffuser-2
    full_prompt = f"{prompt}\ntext: '{main_text}'\nsubtext: '{sub_text}'"

    img = pipe(
        prompt=full_prompt,
        negative_prompt=negative_prompt if negative_prompt else None,
        num_inference_steps=int(steps),
        guidance_scale=float(guidance),
        width=int(width),
        height=int(height),
        generator=g
    ).images[0]

    # retour base64 pour un usage direct via API
    buf = io.BytesIO()
    img.save(buf, format="PNG")
    b64 = base64.b64encode(buf.getvalue()).decode("utf-8")
    return img, str(seed), b64

with gr.Blocks() as demo:
    gr.Markdown("### TextDiffuser-2 — API pour visuels LinkedIn (HF Space)")
    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(label="Background prompt", value="Professional LinkedIn poster background, deep blue gradient, Quebec map in connected nodes, innovation & cybersecurity icons, clean corporate layout, high contrast")
            main_text = gr.Textbox(label="Main title", value="SOUVERAINETÉ NUMÉRIQUE AU QUÉBEC")
            sub_text = gr.Textbox(label="Subtitle", value="Un avantage compétitif réel ?")
            negative_prompt = gr.Textbox(label="Negative prompt (optional)", value="blurry, misspelled text, distorted letters, artifacts, cluttered layout")
            width  = gr.Number(label="Width", value=1200, precision=0)
            height = gr.Number(label="Height", value=627,  precision=0)
            steps = gr.Slider(10, 60, value=40, step=1, label="Steps")
            guidance = gr.Slider(3.0, 12.0, value=7.5, step=0.5, label="Guidance")
            seed = gr.Textbox(label="Seed (optional)")
            btn = gr.Button("Generate")
        with gr.Column():
            out_img = gr.Image(label="Result", type="pil")
            out_seed = gr.Textbox(label="Used seed")
            out_b64 = gr.Textbox(label="Base64 PNG (for API)")
    btn.click(gen_image, inputs=[prompt, main_text, sub_text, negative_prompt, width, height, steps, guidance, seed],
              outputs=[out_img, out_seed, out_b64])

demo.launch()