app.py

import gradio as gr
import timm
import torch
from transformers import RobertaForSequenceClassification, RobertaTokenizer

"Vision"
vit_model = timm.create_model("hf_hub:Marqo/nsfw-image-detection-384", pretrained=True)
vit_model = vit_model.eval()

data_config = timm.data.resolve_model_data_config(vit_model)
transforms = timm.data.create_transform(**data_config, is_training=False)

"NLP"
tokenizer = RobertaTokenizer.from_pretrained("s-nlp/roberta_toxicity_classifier")
model = RobertaForSequenceClassification.from_pretrained(
    "s-nlp/roberta_toxicity_classifier"
)


def moderate_image(img):
    # Load your model
    with torch.no_grad():
        output = vit_model(transforms(img).unsqueeze(0)).softmax(dim=-1).cpu()

    class_names = vit_model.pretrained_cfg["label_names"]
    probabilities = output[0].tolist()
    if probabilities[0] >= 0.3:
        return class_names[0]
    else:
        return class_names[1]


def classify_toxic(text):
    with torch.no_grad():
        batch = tokenizer.encode(text, return_tensors="pt")
        output = model(batch).logits
    probabilities = torch.nn.functional.softmax(output, dim=-1)
    preds = probabilities.tolist()
    return "Toxic" if preds[0][0] <= 0.55 else "Safe"


# -----------------------
# Apple-Minimal Styling
# -----------------------
custom_css = """
/* Center container and control width */
.gradio-container {
    max-width: 900px !important;
    margin: 0 auto !important;
    padding: 20px 10px !important;
}

/* Header styling */
.clean-title {
    font-size: 1.9rem;
    font-weight: 600;
    text-align: center;
    margin-bottom: 1.2rem;
    letter-spacing: -0.4px;
}

/* Apple-like card sections */
.apple-card {
    padding: 18px;
    border-radius: 12px;
    border: 1px solid rgba(var(--block-border-color-rgb), 0.14);
    background: var(--block-background-fill);
    box-shadow: 0 1px 3px rgba(0,0,0,0.04);
    margin-bottom: 18px;
}

/* Button styling: clean, flat, subtle */
.gr-button {
    border-radius: 8px !important;
    background: var(--button-secondary-background-fill) !important;
    border: 1px solid rgba(var(--block-border-color-rgb), 0.22) !important;
    transition: 0.2s ease !important;
}

.gr-button:hover {
    background: var(--button-secondary-background-fill-hover) !important;
    border-color: rgba(var(--block-border-color-rgb), 0.34) !important;
}

.gr-button:active {
    background: var(--button-secondary-background-fill-pressed) !important;
}

/* Reduce blank space between elements */
.gr-block {
    margin: 6px 0 !important;
}

/* Label style */
label {
    font-weight: 500 !important;
}

/* Make body fill full height so footer can stick */
body, .gradio-container {
    min-height: 100vh !important;
    display: flex;
    flex-direction: column;
}

/* Main content should expand, footer sits at bottom */
.main-content {
    flex: 1 0 auto;
}

.footer-custom {
    flex-shrink: 0;
    text-align: center;
    font-size: 0.80rem;
    opacity: 0.6;
    padding: 14px 0;
    border-top: 1px solid rgba(var(--block-border-color-rgb), 0.12);
    margin-top: 25px;
}

footer {display: none !important}
"""


# -----------------------
# UI Layout
# -----------------------
with gr.Blocks(
    theme=gr.themes.Soft(primary_hue="violet", secondary_hue="slate"), css=custom_css
) as demo:
    with gr.Column(elem_classes="main-content"):
        gr.Markdown("<div class='clean-title'>Content Safety Demo</div>")

        with gr.Tabs():
            # ---- NSFW Image Classification ---- #
            with gr.Tab("NSFW Image Detection"):
                with gr.Row():
                    with gr.Column(scale=3):
                        with gr.Group(elem_classes="apple-card"):
                            img_in = gr.Image(type="pil", label="Upload Image")
                            classify_img_btn = gr.Button("Classify")
                            img_clear_btn = gr.ClearButton(components=img_in)

                    with gr.Column(scale=2):
                        with gr.Group(elem_classes="apple-card"):
                            img_out = gr.Label(label="Prediction")

                classify_img_btn.click(
                    fn=moderate_image, inputs=img_in, outputs=img_out
                )

            # ---- Toxic Text Classification ---- #
            with gr.Tab("Toxic Text Detection"):
                with gr.Row():
                    with gr.Column(scale=3):
                        with gr.Group(elem_classes="apple-card"):
                            txt_in = gr.Textbox(lines=4, label="Enter Text")
                            classify_txt_btn = gr.Button("Analyze")
                            text_clear_btn = gr.ClearButton(components=txt_in)

                    with gr.Column(scale=2):
                        with gr.Group(elem_classes="apple-card"):
                            txt_out = gr.Label(label="Prediction")

                classify_txt_btn.click(classify_toxic, inputs=txt_in, outputs=txt_out)

    gr.Markdown(
        "<div class='footer-custom'>Demo by 7th • Powered by Transformers</div>"
    )

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