import gradio as gr
from huggingface_hub import InferenceClient
import os
import base64
from io import BytesIO
from PIL import Image

HF_TOKEN = os.environ.get("HF_TOKEN", "")
client = InferenceClient(provider="fal-ai", api_key=HF_TOKEN)

def generate_image_api(prompt, seed=42, num_inference_steps=50):
    try:
        image = client.text_to_image(
            prompt,
            model="tencent/HunyuanImage-3.0",
            seed=int(seed),
            num_inference_steps=int(num_inference_steps)
        )
        return image, seed, "Success!"
    except Exception as e:
        placeholder = Image.new('RGB', (1024, 1024), color=(240, 240, 245))
        return placeholder, seed, f"Error: {str(e)}"

def infer(prompt, seed, randomize_seed, diff_infer_steps, image_size):
    import random
    if randomize_seed:
        seed = random.randint(0, 2 ** 32 - 1)
    image, used_seed, status = generate_image_api(prompt, seed, diff_infer_steps)
    return image, used_seed, status

def api_generate(prompt: str, seed: int = 42, num_inference_steps: int = 50):
    try:
        image, used_seed, status = generate_image_api(prompt, seed, num_inference_steps)
        buffered = BytesIO()
        image.save(buffered, format="PNG")
        img_str = base64.b64encode(buffered.getvalue()).decode()
        return {
            "success": True,
            "image_base64": img_str,
            "seed": used_seed,
            "status": status,
            "prompt": prompt
        }
    except Exception as e:
        return {
            "success": False,
            "error": str(e),
            "seed": seed,
            "prompt": prompt
        }

examples = [
    "A brown and white dog is running on the grass",
    "A futuristic city at sunset with flying cars",
    "A serene mountain landscape with a crystal clear lake",
]

css = """
#col-container {
  margin: 0 auto;
  max-width: 800px;
}
.note {
  background: #fff3cd;
  padding: 15px;
  border-radius: 8px;
  margin: 10px 0;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("# 🎨 HunyuanImage-3.0 Text-to-Image using fal.ai Provider")
        gr.Markdown(
            """
### Tencent HunyuanImage-3.0 - Using fal.ai Inference Provider
✅ هذه المساحة الآن تولّد صور حقيقية فعلياً عبر مزود fal.ai  
🔗 كل شيء يعمل تلقائياً باستخدام التوكن HF_TOKEN من أسرارك.
""",
            elem_classes="note"
        )
        
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=True,
                max_lines=3,
                placeholder="Enter your prompt for image generation...",
                value="A serene mountain landscape with a crystal clear lake"
            )
            run_button = gr.Button("🎨 Generate Image", variant="primary")
        
        result = gr.Image(label="Generated Image", show_label=True)
        status_text = gr.Textbox(label="Status", interactive=False)
        
        with gr.Accordion("Advanced Settings", open=False):
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=2**32 - 1,
                step=1,
                value=42,
            )
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
            diff_infer_steps = gr.Slider(
                label="Inference Steps",
                minimum=1,
                maximum=100,
                step=1,
                value=50,
            )
            image_size = gr.Radio(
                choices=["1024x1024"],
                value="1024x1024",
                label="Image Size"
            )
        
        gr.Examples(
            examples=examples,
            inputs=[prompt],
            outputs=[result, seed, status_text],
            fn=infer,
            cache_examples=False,
        )
    
    run_button.click(
        fn=infer,
        inputs=[prompt, seed, randomize_seed, diff_infer_steps, image_size],
        outputs=[result, seed, status_text],
    )

if __name__ == "__main__":
    demo.launch()