app.py

"""OpenTextShield demo Space.

Loads the OpenTextShield mBERT model from the Hub and classifies an SMS as
ham (legitimate), spam or phishing, applying the same text normalisation as
the production API so obfuscated messages are handled identically. Styled to
match the OpenTextShield / TelecomsXChange (TCXC) brand.
"""

import gradio as gr
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from normalizer import normalize_unicode

MODEL_ID = "telecomsxchange/OpenTextShield"
MAX_TOKENS = 96  # matches the production API's truncation length

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
model.eval()

DISPLAY = {
    "ham": ("Legitimate", "ham", "reads like a normal message"),
    "spam": ("Spam", "spam", "unwanted promotional or bulk content"),
    "phishing": ("Phishing", "phishing", "an attempt to steal credentials, money or personal data"),
}

EXAMPLES = [
    "Running about 15 min late, order me the usual? I'll grab the bill.",
    "USPS: Your parcel could not be delivered because of an unpaid customs fee. Settle it within 24h to avoid return: http://usps-redelivery.top/pay",
    "BBVA: Hemos detectado un acceso inusual a su cuenta. Verifique su identidad ahora para evitar el bloqueo: http://bbva-seguridad.info/verificar",
    "CONGRATULATIONS! Your number was picked for a $1,000 gift card. Reply YES to claim before midnight!",
    "Ｐａｙｐａｌ: unusual sign-in detected. Confirm your identity: http://рayрal-id.com/verify",
]


def classify(message: str):
    message = (message or "").strip()
    if not message:
        return "", {}, ""

    normalized = normalize_unicode(message)
    inputs = tokenizer(
        normalized,
        return_tensors="pt",
        truncation=True,
        max_length=MAX_TOKENS,
    )
    with torch.inference_mode():
        probs = torch.softmax(model(**inputs).logits, dim=-1)[0]

    top = int(probs.argmax())
    raw = model.config.id2label[top]
    word, css_class, meaning = DISPLAY[raw]
    verdict_html = (
        f'<div class="ots-verdict"><span class="ots-word ots-{css_class}">{word}</span>'
        f'<span class="ots-conf">{probs[top]:.1%}</span>'
        f'<span class="ots-note">&mdash; {meaning}</span></div>'
    )

    scores = {DISPLAY[model.config.id2label[i]][0]: float(p) for i, p in enumerate(probs)}
    note = (
        ""
        if normalized == message
        else f"**Obfuscation detected** — classified as the text it imitates:\n\n> {normalized}"
    )
    return verdict_html, scores, note


MARK_SVG = """<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 100 112" class="ots-mark" aria-hidden="true"><path d="M14 0H86Q100 0 100 14V62Q100 78 88 86L56 108Q50 112 44 108L12 86Q0 78 0 62V14Q0 0 14 0Z" class="ots-mark-shield"/><path d="M30 56 L44 70 L72 40" fill="none" class="ots-mark-check" stroke-width="10" stroke-linecap="round" stroke-linejoin="round"/></svg>"""

HEADER = f"""
<div class="ots-header">
  <div class="ots-brand">{MARK_SVG}
    <div>
      <div class="ots-title">OpenTextShield</div>
      <div class="ots-sub">Open-source SMS spam &amp; phishing detection, in many languages</div>
    </div>
  </div>
  <div class="ots-links">
    <a href="https://github.com/TelecomsXChangeAPi/OpenTextShield" target="_blank" rel="noopener">GitHub</a>
    <a href="https://huggingface.co/telecomsxchange/OpenTextShield" target="_blank" rel="noopener">Model</a>
    <a href="https://hub.docker.com/r/telecomsxchange/opentextshield" target="_blank" rel="noopener">Docker</a>
    <a href="https://ots.telecomsxchange.com" target="_blank" rel="noopener">Live API</a>
  </div>
</div>
"""

ARTICLE = """
### About

OpenTextShield is an open-source model that detects SMS spam and phishing
(smishing) in many languages. It is used by telecom carriers to screen live
SMS traffic and protect subscribers in real networks, and it runs entirely on
your own servers — as a REST API, an SMPP proxy in front of your SMSC, or
both. No third-party AI service is involved. This demo applies the same
Unicode normalisation as the production API, so zero-width, full-width,
homoglyph and leetspeak disguises are classified as the text they imitate.

### Run it yourself

```bash
docker pull telecomsxchange/opentextshield:latest
docker run -d -p 8002:8002 -p 8080:8080 telecomsxchange/opentextshield:latest

curl -X POST "http://localhost:8002/predict/" \\
  -H "Content-Type: application/json" \\
  -d '{"text":"Your account has been suspended. Verify now at http://secure-login-check.xyz","model":"ots-mbert"}'
```

Or load the model directly:

```python
from transformers import pipeline
classifier = pipeline("text-classification", model="telecomsxchange/OpenTextShield")
classifier("Your package is held. Pay the fee: http://usps-redelivery.top/pay")
```

Built by [TelecomsXChange (TCXC)](https://www.telecomsxchange.com) · MIT licensed
"""

CSS = """
:root {
  --ots-paper:#F2F3F1; --ots-surface:#FAFAF9; --ots-ink:#16181A; --ots-muted:#6B7076;
  --ots-hair:#D9DCD8; --ots-ham:#1E6E50; --ots-spam:#A6621A; --ots-phishing:#B42318;
}
.dark {
  --ots-paper:#15171A; --ots-surface:#1C1F23; --ots-ink:#E9EBEC; --ots-muted:#9AA0A6;
  --ots-hair:#2C3035; --ots-ham:#4CC38A; --ots-spam:#E5A04D; --ots-phishing:#F0655A;
}
.gradio-container { max-width: min(780px, 100%) !important; margin: 0 auto !important; }
.ots-header { display:flex; align-items:flex-end; justify-content:space-between;
  flex-wrap:wrap; gap:12px; padding:8px 0 4px; border-bottom:1px solid var(--ots-hair); }
.ots-brand { display:flex; align-items:center; gap:14px; }
.ots-mark { width:40px; height:45px; flex:none; }
.ots-mark-shield { fill: var(--ots-ink); }
.ots-mark-check { stroke: var(--ots-paper); }
.ots-title { font-size:22px; font-weight:700; letter-spacing:-0.02em; color:var(--ots-ink); }
.ots-sub { font-size:13.5px; color:var(--ots-muted); }
.ots-links { display:flex; gap:14px; font-size:13.5px; padding-bottom:4px; }
.ots-links a { color:var(--ots-muted) !important; text-decoration:none !important;
  border-bottom:1px solid var(--ots-hair); }
.ots-links a:hover { color:var(--ots-ink) !important; border-color:var(--ots-ink); }
.ots-verdict { display:flex; align-items:baseline; gap:10px; flex-wrap:wrap;
  padding:6px 2px 2px; min-height:40px; }
.ots-word { font-size:30px; font-weight:700; letter-spacing:-0.02em; line-height:1.1; }
.ots-ham { color:var(--ots-ham); } .ots-spam { color:var(--ots-spam); }
.ots-phishing { color:var(--ots-phishing); }
.ots-conf { font-size:17px; color:var(--ots-ink); font-variant-numeric:tabular-nums; }
.ots-note { font-size:13.5px; color:var(--ots-muted); }
@media (max-width: 640px) {
  .gradio-container { padding-left:16px !important; padding-right:16px !important; }
  .ots-header { flex-direction:column; align-items:flex-start; gap:8px; }
  .ots-brand { gap:10px; }
  .ots-mark { width:32px; height:36px; }
  .ots-title { font-size:19px; }
  .ots-sub { font-size:12.5px; }
  .ots-links { flex-wrap:wrap; gap:10px; }
  .ots-word { font-size:24px; }
  .ots-conf { font-size:15px; }
}
"""

theme = gr.themes.Soft(
    primary_hue=gr.themes.colors.stone,
    neutral_hue=gr.themes.colors.stone,
    font=[gr.themes.GoogleFont("Schibsted Grotesk"), "system-ui", "sans-serif"],
    font_mono=[gr.themes.GoogleFont("JetBrains Mono"), "ui-monospace", "monospace"],
).set(
    body_background_fill="#F2F3F1",
    body_background_fill_dark="#15171A",
    block_background_fill="#FAFAF9",
    block_background_fill_dark="#1C1F23",
    button_primary_background_fill="#16181A",
    button_primary_background_fill_hover="#2C3035",
    button_primary_text_color="#FAFAF9",
    button_primary_background_fill_dark="#E9EBEC",
    button_primary_background_fill_hover_dark="#FFFFFF",
    button_primary_text_color_dark="#15171A",
    block_title_text_color="#6B7076",
    block_title_text_color_dark="#9AA0A6",
)

with gr.Blocks(theme=theme, css=CSS, title="OpenTextShield — SMS Spam & Phishing Detection") as demo:
    gr.HTML(HEADER)
    msg = gr.Textbox(
        lines=3,
        max_lines=8,
        label="Text message",
        placeholder="Paste an SMS to check…",
    )
    check = gr.Button("Check message", variant="primary")
    verdict = gr.HTML(label="Verdict")
    scores = gr.Label(num_top_classes=3, label="Confidence", show_label=True)
    note = gr.Markdown()
    gr.Examples(
        examples=[[e] for e in EXAMPLES],
        inputs=msg,
        outputs=[verdict, scores, note],
        fn=classify,
        run_on_click=True,
        cache_examples=False,
        label="Try a sample",
    )
    gr.Markdown(ARTICLE)

    check.click(classify, inputs=msg, outputs=[verdict, scores, note])
    msg.submit(classify, inputs=msg, outputs=[verdict, scores, note])

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