import os
import gradio as gr
from dotenv import load_dotenv
from utils import analyze, prepare_examples

load_dotenv()
TOKEN = os.getenv("HF_TOKEN")

EXAMPLES = prepare_examples()

CSS = """
.header {
    text-align: center;
    padding: 2rem 1rem;
    background: linear-gradient(135deg, #14b8a6 0%, #0d9488 100%);
    color: white;
    border-radius: 8px;
    margin-bottom: 2rem;
}
.header-logo {
    max-width: 400px;
    height: auto;
    margin: 0 auto;
    display: block;
}
.header p { margin: 0.5rem 0; opacity: 0.9; }
.header a { color: white; text-decoration: underline; }

/* Hide built-in Gradio footer */
footer {
    display: none !important;
}

/* Custom footer styling */
.custom-footer {
    text-align: center;
    padding: 1rem;
    color: #888;
    margin-top: 2rem;
}
"""

with gr.Blocks(
    title="Visual Content Moderation",
    css=CSS,
    theme=gr.themes.Default(primary_hue=gr.themes.colors.teal),
    analytics_enabled=False,
) as demo:
    gr.HTML("""
    <div class="header">
        <img src="https://github.com/viddexa/moderators/releases/download/v0.1.1/logo-v2.jpeg" alt="Visual Content Moderation" class="header-logo">
        <p>Official demo for the <a href="https://github.com/viddexa/moderators" target="_blank">moderators</a> package</p>
        <p>
            <a href="https://huggingface.co/viddexa/nsfw-detection-2-mini" target="_blank">Model: nsfw-detection-2-mini</a> | 
            <a href="https://huggingface.co/viddexa/nsfw-detection-2-nano" target="_blank">Model: nsfw-detection-2-nano</a> | 
            <a href="https://arxiv.org/abs/2312.16338" target="_blank">Arxiv</a> | 
            <a href="https://github.com/viddexa/moderators" target="_blank">GitHub</a> | 
            <a href="https://pypi.org/project/moderators/" target="_blank">PyPI</a>
        </p>
    </div>
    """)
    
    with gr.Accordion("📄 BibTeX entry for citation", open=False):
        gr.Textbox(
            value="""@article{akyon2023nudity,
    title={State-of-the-art in nudity classification: A comparative analysis},
    author={Akyon, Fatih Cagatay and Temizel, Alptekin},
    booktitle={2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)},
    pages={1--5},
    year={2023},
    organization={IEEE}
}""",
            show_label=False,
            lines=9,
            max_lines=9,
        )
    
    with gr.Row():
        with gr.Column(scale=1):
            model_choice = gr.Dropdown(
                choices=["viddexa/nsfw-detection-2-mini", "viddexa/nsfw-detection-2-nano"],
                value="viddexa/nsfw-detection-2-mini",
                label="Model",
            )
            
            with gr.Tabs():
                with gr.TabItem("Upload"):
                    image_input = gr.Image(type="filepath", label="Image")
                with gr.TabItem("URL"):
                    url_input = gr.Textbox(label="Image URL", placeholder="https://...")
            
            analyze_btn = gr.Button("Analyze", variant="primary")
        
        with gr.Column(scale=1):
            output = gr.Label(label="Classification Scores", num_top_classes=5)
    
    gr.Markdown("### 🎯 Try an Example")
    gr.Examples(
        examples=EXAMPLES,
        inputs=[image_input, model_choice],
        outputs=output,
        fn=lambda img, model: analyze(img, None, model, TOKEN),
        cache_examples=True,
        run_on_click=True,
    )
    
    analyze_btn.click(
        fn=lambda img, url, model: analyze(img, url, model, TOKEN),
        inputs=[image_input, url_input, model_choice],
        outputs=output,
    )
    
    gr.HTML('<div class="custom-footer">Developed by Viddexa</div>')

demo.launch()