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import gradio as gr
import pytesseract
from PIL import Image
from transformers import pipeline
import re
from langdetect import detect
from deep_translator import GoogleTranslator
# Translator instance
translator = GoogleTranslator(source="auto", target="es")
# 1. Load separate keywords for SMiShing and Other Scam (assumed in English)
with open("smishing_keywords.txt", "r", encoding="utf-8") as f:
SMISHING_KEYWORDS = [line.strip().lower() for line in f if line.strip()]
with open("other_scam_keywords.txt", "r", encoding="utf-8") as f:
OTHER_SCAM_KEYWORDS = [line.strip().lower() for line in f if line.strip()]
# 2. Zero-Shot Classification Pipeline
model_name = "joeddav/xlm-roberta-large-xnli"
classifier = pipeline("zero-shot-classification", model=model_name)
CANDIDATE_LABELS = ["SMiShing", "Other Scam", "Legitimate"]
def get_keywords_by_language(text: str):
"""
Detect language using `langdetect` and translate keywords if needed.
"""
snippet = text[:200] # Use a snippet for detection
try:
detected_lang = detect(snippet)
except Exception:
detected_lang = "en" # Default to English if detection fails
if detected_lang == "es":
smishing_in_spanish = [
translator.translate(kw).lower() for kw in SMISHING_KEYWORDS
]
other_scam_in_spanish = [
translator.translate(kw).lower() for kw in OTHER_SCAM_KEYWORDS
]
return smishing_in_spanish, other_scam_in_spanish, "es"
else:
return SMISHING_KEYWORDS, OTHER_SCAM_KEYWORDS, "en"
def boost_probabilities(probabilities: dict, text: str):
"""
Boost probabilities based on keyword matches and presence of URLs.
"""
lower_text = text.lower()
smishing_keywords, other_scam_keywords, detected_lang = get_keywords_by_language(text)
smishing_count = sum(1 for kw in smishing_keywords if kw in lower_text)
other_scam_count = sum(1 for kw in other_scam_keywords if kw in lower_text)
# Example: 30% per found keyword
smishing_boost = 0.30 * smishing_count
other_scam_boost = 0.30 * other_scam_count
found_urls = re.findall(r"(https?://[^\s]+)", lower_text)
if found_urls:
# 35% boost for Smishing if there's a URL
smishing_boost += 0.35
p_smishing = probabilities.get("SMiShing", 0.0)
p_other_scam = probabilities.get("Other Scam", 0.0)
p_legit = probabilities.get("Legitimate", 1.0)
p_smishing += smishing_boost
p_other_scam += other_scam_boost
p_legit -= (smishing_boost + other_scam_boost)
# Clamp to 0
p_smishing = max(p_smishing, 0.0)
p_other_scam = max(p_other_scam, 0.0)
p_legit = max(p_legit, 0.0)
# Re-normalize
total = p_smishing + p_other_scam + p_legit
if total > 0:
p_smishing /= total
p_other_scam /= total
p_legit /= total
else:
p_smishing, p_other_scam, p_legit = 0.0, 0.0, 1.0
return {
"SMiShing": p_smishing,
"Other Scam": p_other_scam,
"Legitimate": p_legit,
"detected_lang": detected_lang
}
def smishing_detector(input_type, text, image):
"""
Main detection function:
- If input_type == "Text": use `text` as the message
- If input_type == "Screenshot": use OCR on `image` to get text
"""
if input_type == "Text":
# Use the pasted text
combined_text = text.strip() if text else ""
else:
# input_type == "Screenshot"
if image is not None:
ocr_text = pytesseract.image_to_string(image, lang="spa+eng")
combined_text = ocr_text.strip()
else:
combined_text = ""
if not combined_text:
return {
"text_used_for_classification": "(none)",
"label": "No text provided",
"confidence": 0.0,
"keywords_found": [],
"urls_found": []
}
# Zero-shot classification
result = classifier(
sequences=combined_text,
candidate_labels=CANDIDATE_LABELS,
hypothesis_template="This message is {}."
)
original_probs = {k: float(v) for k, v in zip(result["labels"], result["scores"])}
# Boost logic
boosted = boost_probabilities(original_probs, combined_text)
# Convert to float
boosted = {k: float(v) for k, v in boosted.items() if isinstance(v, (int, float))}
detected_lang = boosted.pop("detected_lang", "en")
# Final classification
final_label = max(boosted, key=boosted.get)
final_confidence = round(boosted[final_label], 3)
# For display
lower_text = combined_text.lower()
smishing_keys, scam_keys, _ = get_keywords_by_language(combined_text)
found_urls = re.findall(r"(https?://[^\s]+)", lower_text)
found_smishing = [kw for kw in smishing_keys if kw in lower_text]
found_other_scam = [kw for kw in scam_keys if kw in lower_text]
return {
"detected_language": detected_lang,
"text_used_for_classification": combined_text,
"original_probabilities": {k: round(v, 3) for k, v in original_probs.items()},
"boosted_probabilities": {k: round(v, 3) for k, v in boosted.items()},
"label": final_label,
"confidence": final_confidence,
"smishing_keywords_found": found_smishing,
"other_scam_keywords_found": found_other_scam,
"urls_found": found_urls,
}
# Create a Radio for user choice + text input + image input
demo = gr.Interface(
fn=smishing_detector,
inputs=[
gr.Radio(
choices=["Text", "Screenshot"],
label="Choose input type",
value="Text", # default
info="Select 'Text' to paste a message, or 'Screenshot' to upload an image."
),
gr.Textbox(
lines=3,
label="Paste Suspicious SMS Text",
placeholder="Type or paste the message here..."
),
gr.Image(
type="pil",
label="Upload a Screenshot",
)
],
outputs="json",
title="SMiShing & Scam Detector",
description="""
Select "Text" or "Screenshot" above.
- If "Text", only use the textbox.
- If "Screenshot", only upload an image.
The app will classify the message as SMiShing, Other Scam, or Legitimate.
""",
allow_flagging="never"
)
if __name__ == "__main__":
demo.launch() |