safebrowsing debugging

app.py

@@ -1,46 +1,19 @@
-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 openai
-import os
 import requests
 import json
+import os
 
-# Set your OpenAI API key
-openai.api_key = os.getenv("OPENAI_API_KEY")
-
-# Retrieve Google Safe Browsing API key from environment
 SAFE_BROWSING_API_KEY = os.getenv("GOOGLE_SAFE_BROWSING_API_KEY")
 SAFE_BROWSING_URL = "https://safebrowsing.googleapis.com/v4/threatMatches:find"
 
-# 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 check_urls_with_google_safebrowsing(urls):
     """
-    Queries the Google Safe Browsing API to see if any URLs are malicious.
+    Debug-enabled version:
+    - Prints payload and raw response to help troubleshoot Safe Browsing issues.
     Returns a dict {url: bool is_malicious}.
-    If the API key is missing or an error occurs, returns {url: False} for all.
     """
     result = {}
     if not SAFE_BROWSING_API_KEY:
-        # No key: we can't check. Return all as safe.
+        print("No GOOGLE_SAFE_BROWSING_API_KEY found. Returning all URLs as safe.")
         for u in urls:
             result[u] = False
         return result
@@ -66,6 +39,12 @@ def check_urls_with_google_safebrowsing(urls):
         }
     }
 
+    print("---- Safe Browsing Debug ----")
+    print("REQUEST Payload (JSON):")
+    print(json.dumps(payload, indent=2))
+    print("REQUEST Endpoint:", SAFE_BROWSING_URL, "Key:", SAFE_BROWSING_API_KEY)
+    print("URLs being checked:", urls)
+
     try:
         resp = requests.post(
             SAFE_BROWSING_URL,
@@ -73,9 +52,17 @@ def check_urls_with_google_safebrowsing(urls):
             json=payload,
             timeout=10
         )
-        data = resp.json()
+
+        print("RESPONSE Status Code:", resp.status_code)
+        try:
+            data = resp.json()
+            print("RESPONSE JSON:")
+            print(json.dumps(data, indent=2))
+        except Exception as parse_error:
+            print("Error parsing response as JSON:", parse_error)
+            data = {}
+
         # If "matches" is present, some URL is flagged
-        # Each match has "threat": {"url": "..."}
         malicious_urls = set()
         if "matches" in data:
             for match in data["matches"]:
@@ -92,369 +79,7 @@ def check_urls_with_google_safebrowsing(urls):
         for u in urls:
             result[u] = False
 
-    return result
-
-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, presence of URLs,
-    and Google Safe Browsing checks.
-    """
-    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
-
-    # More robust URL pattern
-    found_urls = re.findall(
-        r"(https?://[^\s]+|\b[a-zA-Z0-9.-]+\.(?:com|net|org|edu|gov|mil|io|ai|co|info|biz|us|uk|de|fr|es|ru|jp|cn|in|au|ca|br|mx|it|nl|se|no|fi|ch|pl|kr|vn|id|tw|sg|hk)\b)",
-        lower_text
-    )
-    # If any URL is found, add 0.35 to smishing
-    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)
-
-    # Preliminary clamp & normalization
-    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
-
-    # **Now** check Safe Browsing
-    sb_results = {}
-    if found_urls:
-        sb_results = check_urls_with_google_safebrowsing(found_urls)
-        # If any malicious => set p_smishing=1.0
-        if any(sb_results[u] for u in sb_results):
-            p_smishing = 1.0
-            p_other_scam = 0.0
-            p_legit = 0.0
-
-    return {
-        "SMiShing": p_smishing,
-        "Other Scam": p_other_scam,
-        "Legitimate": p_legit,
-        "detected_lang": detected_lang,
-        "found_urls": found_urls,
-        "safe_browsing_results": sb_results  # <--- show which are malicious
-    }
-
-def query_llm_for_classification(raw_message: str) -> dict:
-    """
-    First LLM call: asks for a classification (SMiShing, Other Scam, or Legitimate)
-    acting as a cybersecurity expert. Returns label and short reason.
-    """
-    if not raw_message.strip():
-        return {"label": "Unknown", "reason": "No message provided to the LLM."}
-
-    system_prompt = (
-        "You are a cybersecurity expert. You will classify the user's message "
-        "as one of: SMiShing, Other Scam, or Legitimate. Provide a short reason. "
-        "Return only JSON with keys: label, reason."
-    )
-    user_prompt = f"Message: {raw_message}\nClassify it as SMiShing, Other Scam, or Legitimate."
-
-    try:
-        response = openai.ChatCompletion.create(
-            model="gpt-3.5-turbo",
-            messages=[
-                {"role": "system", "content": system_prompt},
-                {"role": "user", "content": user_prompt}
-            ],
-            temperature=0.2
-        )
-        raw_reply = response["choices"][0]["message"]["content"].strip()
-
-        llm_result = json.loads(raw_reply)
-        if "label" not in llm_result or "reason" not in llm_result:
-            return {"label": "Unknown", "reason": f"Unexpected format: {raw_reply}"}
-
-        return llm_result
-
-    except Exception as e:
-        return {"label": "Unknown", "reason": f"LLM error: {e}"}
-
-def incorporate_llm_label(boosted: dict, llm_label: str) -> dict:
-    """
-    Adjust the final probabilities based on the LLM's classification.
-    If LLM says SMiShing, add +0.2 to SMiShing, etc. Then clamp & re-normalize.
-    """
-    if llm_label == "SMiShing":
-        boosted["SMiShing"] += 0.2
-    elif llm_label == "Other Scam":
-        boosted["Other Scam"] += 0.2
-    elif llm_label == "Legitimate":
-        boosted["Legitimate"] += 0.2
-    # else "Unknown" => do nothing
-
-    # clamp
-    for k in boosted:
-        if boosted[k] < 0:
-            boosted[k] = 0.0
+    print("RESULTS (url -> malicious):", result)
+    print("---- End Debug ----\n")
 
-    total = sum(boosted.values())
-    if total > 0:
-        for k in boosted:
-            boosted[k] /= total
-    else:
-        # fallback
-        boosted["Legitimate"] = 1.0
-        boosted["SMiShing"] = 0.0
-        boosted["Other Scam"] = 0.0
-
-    return boosted
-
-def query_llm_for_explanation(
-    text: str,
-    final_label: str,
-    final_conf: float,
-    local_label: str,
-    local_conf: float,
-    llm_label: str,
-    llm_reason: str,
-    found_smishing: list,
-    found_other_scam: list,
-    found_urls: list,
-    detected_lang: str
-) -> str:
-    """
-    Second LLM call: provides a holistic explanation of the final classification
-    in the same language as detected_lang (English or Spanish).
-    """
-    if detected_lang == "es":
-        system_prompt = (
-            "Eres un experto en ciberseguridad. Proporciona una explicación final al usuario en español. "
-            "Combina la clasificación local, la clasificación LLM y la etiqueta final en una sola explicación breve. "
-            "No reveles el código interno ni el JSON bruto; simplemente da una breve explicación fácil de entender. "
-            "Termina con la etiqueta final."
-        )
-    else:
-        system_prompt = (
-            "You are a cybersecurity expert providing a final explanation to the user in English. "
-            "Combine the local classification, the LLM classification, and the final label "
-            "into one concise explanation. Do not reveal internal code or raw JSON. "
-            "End with a final statement of the final label."
-        )
-
-    user_context = f"""
-User Message:
-{text}
-
-Local Classification => Label: {local_label}, Confidence: {local_conf}
-LLM Classification => Label: {llm_label}, Reason: {llm_reason}
-Final Overall Label => {final_label} (confidence {final_conf})
-
-Suspicious SMiShing Keywords => {found_smishing}
-Suspicious Other Scam Keywords => {found_other_scam}
-URLs => {found_urls}
-"""
-
-    try:
-        response = openai.ChatCompletion.create(
-            model="gpt-3.5-turbo",
-            messages=[
-                {"role": "system", "content": system_prompt},
-                {"role": "user", "content": user_context}
-            ],
-            temperature=0.2
-        )
-        final_explanation = response["choices"][0]["message"]["content"].strip()
-        return final_explanation
-    except Exception as e:
-        return f"Could not generate final explanation due to error: {e}"
-
-def smishing_detector(input_type, text, image):
-    """
-    Main detection function combining text (if 'Text') & OCR (if 'Screenshot'),
-    plus two LLM calls: 
-      1) classification to adjust final probabilities,
-      2) a final explanation summarizing the outcome in the detected language.
-    """
-    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": [],
-            "llm_label": "Unknown",
-            "llm_reason": "No text to analyze",
-            "final_explanation": "No text provided"
-        }
-
-    # 1. Local zero-shot classification
-    local_result = classifier(
-        sequences=combined_text,
-        candidate_labels=CANDIDATE_LABELS,
-        hypothesis_template="This message is {}."
-    )
-    original_probs = {k: float(v) for k, v in zip(local_result["labels"], local_result["scores"])}
-
-    # 2. Basic boosting from keywords & URLs (plus Safe Browsing)
-    boosted_dict = boost_probabilities(original_probs, combined_text)
-    detected_lang = boosted_dict.pop("detected_lang", "en")
-
-    # Also retrieve the safe_browsing_results
-    sb_results = boosted_dict.pop("safe_browsing_results", {})
-    found_urls = boosted_dict.pop("found_urls", [])
-
-    for k in boosted_dict:
-        boosted_dict[k] = float(boosted_dict[k])
-
-    local_label = max(boosted_dict, key=boosted_dict.get)
-    local_conf = round(boosted_dict[local_label], 3)
-
-    # 3. LLM Classification
-    llm_classification = query_llm_for_classification(combined_text)
-    llm_label = llm_classification.get("label", "Unknown")
-    llm_reason = llm_classification.get("reason", "No reason provided")
-
-    # 4. Incorporate LLM’s label into final probabilities
-    boosted_dict = incorporate_llm_label(boosted_dict, llm_label)
-
-    final_label = max(boosted_dict, key=boosted_dict.get)
-    final_confidence = round(boosted_dict[final_label], 3)
-
-    # 5. Gather found keywords & URLs for the final explanation
-    lower_text = combined_text.lower()
-    smishing_keys, scam_keys, _ = get_keywords_by_language(combined_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]
-
-    # 6. Final explanation in user's language
-    final_explanation = query_llm_for_explanation(
-        text=combined_text,
-        final_label=final_label,
-        final_conf=final_confidence,
-        local_label=local_label,
-        local_conf=local_conf,
-        llm_label=llm_label,
-        llm_reason=llm_reason,
-        found_smishing=found_smishing,
-        found_other_scam=found_other_scam,
-        found_urls=found_urls,
-        detected_lang=detected_lang
-    )
-
-    # Combine results in final output
-    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_before_llm": {local_label: local_conf},
-        "llm_label": llm_label,
-        "llm_reason": llm_reason,
-        "boosted_probabilities_after_llm": {k: round(v, 3) for k, v in boosted_dict.items()},
-        "label": final_label,
-        "confidence": final_confidence,
-        "smishing_keywords_found": found_smishing,
-        "other_scam_keywords_found": found_other_scam,
-        "urls_found": found_urls,
-        "safe_browsing_results": sb_results,  # <--- show if malicious
-        "final_explanation": final_explanation,
-    }
-
-#
-# Gradio interface with dynamic visibility
-#
-def toggle_inputs(choice):
-    """
-    Return updates for (text_input, image_input) based on the radio selection.
-    """
-    if choice == "Text":
-        return gr.update(visible=True), gr.update(visible=False)
-    else:
-        return gr.update(visible=False), gr.update(visible=True)
-
-with gr.Blocks() as demo:
-    gr.Markdown("## SMiShing & Scam Detector with Google Safe Browsing + LLM")
-    
-    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
-    )
-
-    image_input = gr.Image(
-        type="pil",
-        label="Upload Screenshot",
-        visible=False
-    )
-
-    input_type.change(
-        fn=toggle_inputs,
-        inputs=input_type,
-        outputs=[text_input, image_input],
-        queue=False
-    )
-
-    analyze_btn = gr.Button("Classify")
-    output_json = gr.JSON(label="Result")
-
-    analyze_btn.click(
-        fn=smishing_detector,
-        inputs=[input_type, text_input, image_input],
-        outputs=output_json
-    )
-
-if __name__ == "__main__":
-    if not openai.api_key:
-        print("WARNING: OPENAI_API_KEY not set. LLM calls will fail or be skipped.")
-    if not SAFE_BROWSING_API_KEY:
-        print("WARNING: GOOGLE_SAFE_BROWSING_API_KEY not set. URL checks will be skipped.")
-    demo.launch()
+    return result