import gradio as gr
import requests
import os
import base64
from io import BytesIO
from PIL import Image
import json

# Hugging Face API configuration
HF_TOKEN = os.environ.get("HF_TOKEN", "")
API_URL = "https://api-inference.huggingface.co/models/tencent/HunyuanImage-3.0"

headers = {"Authorization": f"Bearer {HF_TOKEN}"}

def generate_image_api(prompt, seed=42, num_inference_steps=50):
    """
    Generate image using Hugging Face Inference API
    Uses paid API from your HF account balance
    """
    try:
        payload = {
            "inputs": prompt,
            "parameters": {
                "seed": int(seed),
                "num_inference_steps": int(num_inference_steps)
            }
        }
        
        response = requests.post(API_URL, headers=headers, json=payload, timeout=60)
        
        if response.status_code == 200:
            image = Image.open(BytesIO(response.content))
            return image, seed, "Success!"
        else:
            error_msg = f"API Error: {response.status_code} - {response.text}"
            print(error_msg)
            placeholder = Image.new('RGB', (1024, 1024), color=(240, 240, 245))
            return placeholder, seed, error_msg
            
    except Exception as e:
        error_msg = f"Error: {str(e)}"
        print(error_msg)
        placeholder = Image.new('RGB', (1024, 1024), color=(240, 240, 245))
        return placeholder, seed, error_msg

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):
    """
    API endpoint for external integrations like n8n
    Returns base64 encoded image
    """
    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 with Inference API")
        gr.Markdown(
            """### Tencent HunyuanImage-3.0 - Using Paid Hugging Face Inference API
            
            ✅ This Space now uses the Hugging Face Inference API (paid from your account balance)
            - Real image generation with HunyuanImage-3.0
            - API endpoint available for n8n integration
            - Set your HF_TOKEN in Space secrets
            
            🔗 For n8n integration: Use the API endpoint at /gradio_api/ with the api_generate function
            """,
            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="Diffusion inference steps",
                minimum=10,
                maximum=100,
                step=10,
                value=50,
            )
            
            image_size = gr.Radio(
                label="Image Size",
                choices=["auto", "1024x1024", "1280x768", "768x1280"],
                value="auto",
            )
        
        gr.Examples(examples=examples, inputs=[prompt])
        
        run_button.click(
            fn=infer,
            inputs=[prompt, seed, randomize_seed, diff_infer_steps, image_size],
            outputs=[result, seed, status_text],
        )

api_demo = gr.Interface(
    fn=api_generate,
    inputs=[
        gr.Text(label="Prompt"),
        gr.Number(label="Seed", value=42),
        gr.Number(label="Inference Steps", value=50)
    ],
    outputs=gr.JSON(label="Response"),
    title="HunyuanImage-3.0 API Endpoint",
    description="API endpoint for n8n and other integrations. Returns base64 encoded image."
)

app = gr.TabbedInterface(
    [demo, api_demo],
    ["Interface", "API Endpoint"],
    title="HunyuanImage-3.0 Generator"
)

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