working good1

app.py

@@ -5,22 +5,6 @@ from transformers import pipeline
 import re
 from langdetect import detect
 from deep_translator import GoogleTranslator
-import shap
-import requests
-import json
-import os
-import numpy as np
-from shap.maskers import Text
-
-# Patch SHAP to replace np.bool with np.bool_ dynamically
-if hasattr(shap.maskers._text.Text, "invariants"):
-    original_invariants = shap.maskers._text.Text.invariants
-
-    def patched_invariants(self, *args):
-        # Use np.bool_ instead of the deprecated np.bool
-        return np.zeros(len(self._tokenized_s), dtype=np.bool_)
-
-    shap.maskers._text.Text.invariants = patched_invariants
 
 # Translator instance
 translator = GoogleTranslator(source="auto", target="es")
@@ -37,49 +21,6 @@ model_name = "joeddav/xlm-roberta-large-xnli"
 classifier = pipeline("zero-shot-classification", model=model_name)
 CANDIDATE_LABELS = ["SMiShing", "Other Scam", "Legitimate"]
 
-# 3. SHAP Explainer Setup
-explainer = shap.Explainer(classifier, masker=Text(tokenizer=classifier.tokenizer))
-
-# Retrieve the Google Safe Browsing API key from the environment
-SAFE_BROWSING_API_KEY = os.getenv("SAFE_BROWSING_API_KEY")
-
-if not SAFE_BROWSING_API_KEY:
-    raise ValueError("Google Safe Browsing API key not found. Please set it as an environment variable in your Hugging Face Space.")
-
-SAFE_BROWSING_URL = "https://safebrowsing.googleapis.com/v4/threatMatches:find"
-
-def check_url_with_google_safebrowsing(url):
-    """
-    Check a URL against Google's Safe Browsing API.
-    """
-    payload = {
-        "client": {
-            "clientId": "your-client-id",
-            "clientVersion": "1.0"
-        },
-        "threatInfo": {
-            "threatTypes": ["MALWARE", "SOCIAL_ENGINEERING", "UNWANTED_SOFTWARE", "POTENTIALLY_HARMFUL_APPLICATION"],
-            "platformTypes": ["ANY_PLATFORM"],
-            "threatEntryTypes": ["URL"],
-            "threatEntries": [
-                {"url": url}
-            ]
-        }
-    }
-    try:
-        response = requests.post(
-            SAFE_BROWSING_URL,
-            params={"key": SAFE_BROWSING_API_KEY},
-            json=payload
-        )
-        response_data = response.json()
-        if "matches" in response_data:
-            return True  # URL is flagged as malicious
-        return False  # URL is safe
-    except Exception as e:
-        print(f"Error checking URL with Safe Browsing API: {e}")
-        return False
-
 def get_keywords_by_language(text: str):
     """
     Detect language using `langdetect` and translate keywords if needed.
@@ -142,21 +83,9 @@ def boost_probabilities(probabilities: dict, text: str):
         "SMiShing": p_smishing,
         "Other Scam": p_other_scam,
         "Legitimate": p_legit,
-        "detected_lang": detected_lang,
+        "detected_lang": detected_lang
     }
 
-def explain_classification(text):
-    """
-    Generate SHAP explanations for the classification.
-    """
-    if not text.strip():
-        raise ValueError("Cannot generate SHAP explanations for empty text.")
-
-    shap_values = explainer([text])
-    shap.force_plot(
-        explainer.expected_value[0], shap_values[0].values[0], shap_values[0].data
-    )
-
 def smishing_detector(text, image):
     """
     Main detection function combining text and OCR.
@@ -173,8 +102,7 @@ def smishing_detector(text, image):
             "label": "No text provided",
             "confidence": 0.0,
             "keywords_found": [],
-            "urls_found": [],
-            "threat_analysis": "No URLs to analyze",
+            "urls_found": []
         }
 
     result = classifier(
@@ -197,14 +125,6 @@ def smishing_detector(text, image):
     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]
 
-    # Analyze URLs using Google's Safe Browsing API
-    threat_analysis = {
-        url: check_url_with_google_safebrowsing(url) for url in found_urls
-    }
-
-    # SHAP Explanation (optional for user insights)
-    explain_classification(combined_text)
-
     return {
         "detected_language": detected_lang,
         "text_used_for_classification": combined_text,
@@ -215,7 +135,6 @@ def smishing_detector(text, image):
         "smishing_keywords_found": found_smishing,
         "other_scam_keywords_found": found_other_scam,
         "urls_found": found_urls,
-        "threat_analysis": threat_analysis,
     }
 
 demo = gr.Interface(
@@ -232,14 +151,15 @@ demo = gr.Interface(
         )
     ],
     outputs="json",
-    title="SMiShing & Scam Detector with Safe Browsing",
+    title="SMiShing & Scam Detector (Language Detection + Keyword Translation)",
     description="""
 This tool classifies messages as SMiShing, Other Scam, or Legitimate using a zero-shot model
 (joeddav/xlm-roberta-large-xnli). It automatically detects if the text is Spanish or English.
-It uses SHAP for explainability and checks URLs against Google's Safe Browsing API for enhanced analysis.
-    """,
-    flagging_mode="never"
+If Spanish, it translates the English-based keyword lists to Spanish before boosting the scores.
+Any URL found further boosts SMiShing specifically.
+""",
+    allow_flagging="never"
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()

app.py.jan27

@@ -0,0 +1,245 @@
+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
+import shap
+import requests
+import json
+import os
+import numpy as np
+from shap.maskers import Text
+
+# Patch SHAP to replace np.bool with np.bool_ dynamically
+if hasattr(shap.maskers._text.Text, "invariants"):
+    original_invariants = shap.maskers._text.Text.invariants
+
+    def patched_invariants(self, *args):
+        # Use np.bool_ instead of the deprecated np.bool
+        return np.zeros(len(self._tokenized_s), dtype=np.bool_)
+
+    shap.maskers._text.Text.invariants = patched_invariants
+
+# 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"]
+
+# 3. SHAP Explainer Setup
+explainer = shap.Explainer(classifier, masker=Text(tokenizer=classifier.tokenizer))
+
+# Retrieve the Google Safe Browsing API key from the environment
+SAFE_BROWSING_API_KEY = os.getenv("SAFE_BROWSING_API_KEY")
+
+if not SAFE_BROWSING_API_KEY:
+    raise ValueError("Google Safe Browsing API key not found. Please set it as an environment variable in your Hugging Face Space.")
+
+SAFE_BROWSING_URL = "https://safebrowsing.googleapis.com/v4/threatMatches:find"
+
+def check_url_with_google_safebrowsing(url):
+    """
+    Check a URL against Google's Safe Browsing API.
+    """
+    payload = {
+        "client": {
+            "clientId": "your-client-id",
+            "clientVersion": "1.0"
+        },
+        "threatInfo": {
+            "threatTypes": ["MALWARE", "SOCIAL_ENGINEERING", "UNWANTED_SOFTWARE", "POTENTIALLY_HARMFUL_APPLICATION"],
+            "platformTypes": ["ANY_PLATFORM"],
+            "threatEntryTypes": ["URL"],
+            "threatEntries": [
+                {"url": url}
+            ]
+        }
+    }
+    try:
+        response = requests.post(
+            SAFE_BROWSING_URL,
+            params={"key": SAFE_BROWSING_API_KEY},
+            json=payload
+        )
+        response_data = response.json()
+        if "matches" in response_data:
+            return True  # URL is flagged as malicious
+        return False  # URL is safe
+    except Exception as e:
+        print(f"Error checking URL with Safe Browsing API: {e}")
+        return False
+
+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)
+
+    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:
+        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)
+
+    p_smishing = max(p_smishing, 0.0)
+    p_other_scam = max(p_other_scam, 0.0)
+    p_legit = max(p_legit, 0.0)
+
+    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 explain_classification(text):
+    """
+    Generate SHAP explanations for the classification.
+    """
+    if not text.strip():
+        raise ValueError("Cannot generate SHAP explanations for empty text.")
+
+    shap_values = explainer([text])
+    shap.force_plot(
+        explainer.expected_value[0], shap_values[0].values[0], shap_values[0].data
+    )
+
+def smishing_detector(text, image):
+    """
+    Main detection function combining text and OCR.
+    """
+    combined_text = text or ""
+    if image is not None:
+        ocr_text = pytesseract.image_to_string(image, lang="spa+eng")
+        combined_text += " " + ocr_text
+    combined_text = combined_text.strip()
+
+    if not combined_text:
+        return {
+            "text_used_for_classification": "(none)",
+            "label": "No text provided",
+            "confidence": 0.0,
+            "keywords_found": [],
+            "urls_found": [],
+            "threat_analysis": "No URLs to analyze",
+        }
+
+    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"])}
+    boosted = boost_probabilities(original_probs, combined_text)
+
+    boosted = {k: float(v) for k, v in boosted.items() if isinstance(v, (int, float))}
+    detected_lang = boosted.pop("detected_lang", "en")
+    final_label = max(boosted, key=boosted.get)
+    final_confidence = round(boosted[final_label], 3)
+
+    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]
+
+    # Analyze URLs using Google's Safe Browsing API
+    threat_analysis = {
+        url: check_url_with_google_safebrowsing(url) for url in found_urls
+    }
+
+    # SHAP Explanation (optional for user insights)
+    explain_classification(combined_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,
+        "threat_analysis": threat_analysis,
+    }
+
+demo = gr.Interface(
+    fn=smishing_detector,
+    inputs=[
+        gr.Textbox(
+            lines=3,
+            label="Paste Suspicious SMS Text (English/Spanish)",
+            placeholder="Type or paste the message here..."
+        ),
+        gr.Image(
+            type="pil",
+            label="Or Upload a Screenshot (Optional)"
+        )
+    ],
+    outputs="json",
+    title="SMiShing & Scam Detector with Safe Browsing",
+    description="""
+This tool classifies messages as SMiShing, Other Scam, or Legitimate using a zero-shot model
+(joeddav/xlm-roberta-large-xnli). It automatically detects if the text is Spanish or English.
+It uses SHAP for explainability and checks URLs against Google's Safe Browsing API for enhanced analysis.
+    """,
+    flagging_mode="never"
+)
+
+if __name__ == "__main__":
+    demo.launch()