one last try

app.py

@@ -5,6 +5,22 @@ 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")
@@ -21,15 +37,58 @@ 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]
+    snippet = text[:200]  # Use a snippet for detection
     try:
         detected_lang = detect(snippet)
     except Exception:
-        detected_lang = "en"
+        detected_lang = "en"  # Default to English if detection fails
 
     if detected_lang == "es":
         smishing_in_spanish = [
@@ -67,12 +126,10 @@ def boost_probabilities(probabilities: dict, text: str):
     p_other_scam += other_scam_boost
     p_legit -= (smishing_boost + other_scam_boost)
 
-    # Clamp
     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
@@ -85,19 +142,104 @@ 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 smishing_detector(input_type, text, image):
+def explain_classification(text):
     """
-    Main detection function combining text (if 'Text') and OCR (if 'Screenshot').
+    Generate SHAP explanations for the classification.
     """
-    if input_type == "Text":
-        combined_text = text.strip() if text else ""
-    else:
-        combined_text = ""
-        if image is not None:
-            combined_text = pytesseract.image_to_string(image, lang="spa+eng").strip()
+    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 generate_user_friendly_message(
+    final_label: str,
+    confidence: float,
+    found_smishing: list,
+    found_other_scam: list,
+    found_urls: list,
+    threat_analysis: dict
+) -> str:
+    """
+    Build a user-friendly explanation of the classification and provide
+    a brief reason why it is labeled as SMiShing, Other Scam, or Legitimate.
+    """
+    if final_label == "SMiShing":
+        msg = (
+            f"This message is classified as SMiShing (confidence {confidence}). "
+            "We found indications typical of phishing via SMS, such as "
+        )
+        reasons = []
+        if found_smishing:
+            reasons.append(f"the use of suspicious keywords: {', '.join(found_smishing)}")
+        if found_urls:
+            flagged_urls = [u for u in found_urls if threat_analysis.get(u)]
+            safe_urls = [u for u in found_urls if not threat_analysis.get(u)]
+            if flagged_urls:
+                reasons.append(f"at least one URL flagged as unsafe: {', '.join(flagged_urls)}")
+            if safe_urls:
+                reasons.append(f"other URLs may be suspicious: {', '.join(safe_urls)}")
+
+        if not reasons:
+            reasons.append("certain context or structure commonly used in SMiShing")
+
+        msg += " and ".join(reasons) + "."
+        return msg
+
+    elif final_label == "Other Scam":
+        msg = (
+            f"This message is classified as 'Other Scam' (confidence {confidence}). "
+            "It contains elements typically associated with scams. "
+        )
+        reasons = []
+        if found_other_scam:
+            reasons.append(f"keywords often linked to fraudulent activity: {', '.join(found_other_scam)}")
+        if found_urls:
+            flagged_urls = [u for u in found_urls if threat_analysis.get(u)]
+            safe_urls = [u for u in found_urls if not threat_analysis.get(u)]
+            if flagged_urls:
+                reasons.append(f"URLs flagged as unsafe: {', '.join(flagged_urls)}")
+            if safe_urls:
+                reasons.append(f"additional suspicious URLs: {', '.join(safe_urls)}")
+
+        if not reasons:
+            reasons.append("general content or structure known to be used in scams")
+
+        msg += " and ".join(reasons) + "."
+        return msg
+
+    else:  # Legitimate
+        msg = (
+            f"This message is classified as 'Legitimate' (confidence {confidence}). "
+            "We did not detect typical phishing or scam indicators. "
+        )
+        if found_urls:
+            # If there are URLs, mention if they're considered safe
+            flagged_urls = [u for u in found_urls if threat_analysis.get(u)]
+            if flagged_urls:
+                msg += f"However, note that at least one URL appears unsafe: {', '.join(flagged_urls)}."
+            else:
+                msg += "Although it contains URLs, none appear to be malicious."
+        else:
+            msg += "No suspicious keywords or URLs were detected."
+
+        return msg
+
+
+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 {
@@ -105,7 +247,9 @@ def smishing_detector(input_type, text, image):
             "label": "No text provided",
             "confidence": 0.0,
             "keywords_found": [],
-            "urls_found": []
+            "urls_found": [],
+            "threat_analysis": "No URLs to analyze",
+            "user_friendly_message": "No classification could be made since no text was provided.",
         }
 
     result = classifier(
@@ -114,18 +258,12 @@ def smishing_detector(input_type, text, image):
         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)
 
-    # Patched snippet begins
-    # 1. Extract language first, preserving it
-    detected_lang = boosted.get("detected_lang", "en")
-    # 2. Remove it so only numeric keys remain
-    boosted.pop("detected_lang", None)
-    # 3. Convert numeric values to float
+    # Extract language key first, then remove
+    detected_lang = boosted.pop("detected_lang", "en")
     for k, v in boosted.items():
         boosted[k] = float(v)
-    # Patched snippet ends
 
     final_label = max(boosted, key=boosted.get)
     final_confidence = round(boosted[final_label], 3)
@@ -137,6 +275,24 @@ def smishing_detector(input_type, 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
+    }
+
+    # Generate SHAP Explanation (optional for user insights)
+    explain_classification(combined_text)
+
+    # Build user-friendly message
+    user_friendly_msg = generate_user_friendly_message(
+        final_label,
+        final_confidence,
+        found_smishing,
+        found_other_scam,
+        found_urls,
+        threat_analysis
+    )
+
     return {
         "detected_language": detected_lang,
         "text_used_for_classification": combined_text,
@@ -147,64 +303,33 @@ def smishing_detector(input_type, text, image):
         "smishing_keywords_found": found_smishing,
         "other_scam_keywords_found": found_other_scam,
         "urls_found": found_urls,
+        "threat_analysis": threat_analysis,
+        # The new user-friendly explanation
+        "user_friendly_message": user_friendly_msg,
     }
 
-#
-# Gradio interface with dynamic visibility
-#
-def toggle_inputs(choice):
-    """
-    Return updates for (text_input, image_input) based on the radio selection.
-    """
-    if choice == "Text":
-        # Show text input, hide image
-        return gr.update(visible=True), gr.update(visible=False)
-    else:
-        # choice == "Screenshot"
-        # Hide text input, show image
-        return gr.update(visible=False), gr.update(visible=True)
-
-with gr.Blocks() as demo:
-    gr.Markdown("## SMiShing & Scam Detector (Choose Text or Screenshot)")
-    
-    with gr.Row():
-        input_type = gr.Radio(
-            choices=["Text", "Screenshot"], 
-            value="Text", 
-            label="Choose Input Type"
+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)"
         )
-
-    text_input = gr.Textbox(
-        lines=3,
-        label="Paste Suspicious SMS Text",
-        placeholder="Type or paste the message here...",
-        visible=True  # default
-    )
-
-    image_input = gr.Image(
-        type="pil",
-        label="Upload Screenshot",
-        visible=False  # hidden by default
-    )
-
-    # Whenever input_type changes, toggle which input is visible
-    input_type.change(
-        fn=toggle_inputs,
-        inputs=input_type,
-        outputs=[text_input, image_input],
-        queue=False
-    )
-
-    # Button to run classification
-    analyze_btn = gr.Button("Classify")
-    output_json = gr.JSON(label="Result")
-
-    # On button click, call the smishing_detector
-    analyze_btn.click(
-        fn=smishing_detector,
-        inputs=[input_type, text_input, image_input],
-        outputs=output_json
-    )
+    ],
+    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()

app.py.bestofmon

@@ -0,0 +1,210 @@
+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]
+    try:
+        detected_lang = detect(snippet)
+    except Exception:
+        detected_lang = "en"
+
+    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)
+
+    # Clamp
+    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 combining text (if 'Text') and OCR (if 'Screenshot').
+    """
+    if input_type == "Text":
+        combined_text = text.strip() if text else ""
+    else:
+        combined_text = ""
+        if image is not None:
+            combined_text = pytesseract.image_to_string(image, lang="spa+eng").strip()
+
+    if not combined_text:
+        return {
+            "text_used_for_classification": "(none)",
+            "label": "No text provided",
+            "confidence": 0.0,
+            "keywords_found": [],
+            "urls_found": []
+        }
+
+    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)
+
+    # Patched snippet begins
+    # 1. Extract language first, preserving it
+    detected_lang = boosted.get("detected_lang", "en")
+    # 2. Remove it so only numeric keys remain
+    boosted.pop("detected_lang", None)
+    # 3. Convert numeric values to float
+    for k, v in boosted.items():
+        boosted[k] = float(v)
+    # Patched snippet ends
+
+    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]
+
+    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,
+    }
+
+#
+# Gradio interface with dynamic visibility
+#
+def toggle_inputs(choice):
+    """
+    Return updates for (text_input, image_input) based on the radio selection.
+    """
+    if choice == "Text":
+        # Show text input, hide image
+        return gr.update(visible=True), gr.update(visible=False)
+    else:
+        # choice == "Screenshot"
+        # Hide text input, show image
+        return gr.update(visible=False), gr.update(visible=True)
+
+with gr.Blocks() as demo:
+    gr.Markdown("## SMiShing & Scam Detector (Choose Text or Screenshot)")
+    
+    with gr.Row():
+        input_type = gr.Radio(
+            choices=["Text", "Screenshot"], 
+            value="Text", 
+            label="Choose Input Type"
+        )
+
+    text_input = gr.Textbox(
+        lines=3,
+        label="Paste Suspicious SMS Text",
+        placeholder="Type or paste the message here...",
+        visible=True  # default
+    )
+
+    image_input = gr.Image(
+        type="pil",
+        label="Upload Screenshot",
+        visible=False  # hidden by default
+    )
+
+    # Whenever input_type changes, toggle which input is visible
+    input_type.change(
+        fn=toggle_inputs,
+        inputs=input_type,
+        outputs=[text_input, image_input],
+        queue=False
+    )
+
+    # Button to run classification
+    analyze_btn = gr.Button("Classify")
+    output_json = gr.JSON(label="Result")
+
+    # On button click, call the smishing_detector
+    analyze_btn.click(
+        fn=smishing_detector,
+        inputs=[input_type, text_input, image_input],
+        outputs=output_json
+    )
+
+if __name__ == "__main__":
+    demo.launch()