import gradio as gr
import numpy as np
import random
import os
from huggingface_hub import InferenceClient
 
# Load HF Inference Client
client = InferenceClient(api_key=os.environ["HF_API_KEY"])
 
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
 
def infer(
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
 
    # Call Hugging Face Inference API
    image = client.text_to_image(
        prompt=prompt,
        model="stabilityai/sdxl-turbo",  # or "Qwen/Qwen-Image"
        negative_prompt=negative_prompt if negative_prompt else None,
        size=f"{width}x{height}"
    )
    return image, seed
 
 
examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
    "A delicious ceviche cheesecake slice",
]
 
css = """
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
"""
 
with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("# Text-to-Image Gradio Template (Hugging Face Inference API)")
 
        with gr.Row():
            prompt = gr.Textbox(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )
 
            run_button = gr.Button("Run", scale=0, variant="primary")
 
        result = gr.Image(label="Result", show_label=False)
 
        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Textbox(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=True,
            )
 
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )
 
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
 
            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,
                )
 
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,
                )
 
            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=7.5,
                )
 
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=25,
                )
 
        gr.Examples(examples=examples, inputs=[prompt])
 
        # Correct event bindings
        run_button.click(
            fn=infer,
            inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
            outputs=[result, seed],
        )
        prompt.submit(
            fn=infer,
            inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
            outputs=[result, seed],
        )
 
if __name__ == "__main__":
    demo.launch(share=True)  # removed ssr_mode