updated requirements

app.py

@@ -3,12 +3,11 @@ import pytesseract
 from PIL import Image
 from transformers import pipeline
 import re
-
-# Language detection & translation
 from langdetect import detect
-from googletrans import Translator
+from deep_translator import GoogleTranslator
 
-translator = Translator()
+# 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:
@@ -24,26 +23,23 @@ CANDIDATE_LABELS = ["SMiShing", "Other Scam", "Legitimate"]
 
 def get_keywords_by_language(text: str):
     """
-    1. Detect language (using `langdetect`).
-    2. If Spanish ('es'), translate each English-based keyword to Spanish using googletrans.
-    3. If English (or anything else), just use the original English lists.
+    1. Detect language using `langdetect`.
+    2. If Spanish ('es'), translate each English-based keyword to Spanish using `deep-translator`.
+    3. If English (or other languages), use the original English lists.
     """
-    # Attempt to detect language from a snippet (to reduce overhead on very large text)
-    snippet = text[:200]  # up to 200 chars for detection
+    snippet = text[:200]  # Use a snippet for detection
     try:
         detected_lang = detect(snippet)
-    except:
-        detected_lang = "en"  # fallback if detection fails
+    except Exception:
+        detected_lang = "en"  # Default to English if detection fails
 
     if detected_lang == "es":
         # Translate all SMiShing and Other Scam keywords to Spanish
         smishing_in_spanish = [
-            translator.translate(kw, src="en", dest="es").text.lower()
-            for kw in SMISHING_KEYWORDS
+            translator.translate(kw).lower() for kw in SMISHING_KEYWORDS
         ]
         other_scam_in_spanish = [
-            translator.translate(kw, src="en", dest="es").text.lower()
-            for kw in OTHER_SCAM_KEYWORDS
+            translator.translate(kw).lower() for kw in OTHER_SCAM_KEYWORDS
         ]
         return smishing_in_spanish, other_scam_in_spanish, "es"
     else:
@@ -52,45 +48,29 @@ def get_keywords_by_language(text: str):
 
 def boost_probabilities(probabilities: dict, text: str):
     """
-    1. Load the appropriate keyword lists (English or Spanish).
-    2. Count matches for SMiShing vs. Other Scam.
-    3. If a URL is found, add an extra boost only to SMiShing.
-    4. Subtract total boost from 'Legitimate'.
-    5. Clamp negative probabilities to 0, re-normalize.
+    Boost probabilities based on keyword matches and presence of URLs.
     """
     lower_text = text.lower()
-
-    # Grab the correct keyword lists based on language
     smishing_keywords, other_scam_keywords, detected_lang = get_keywords_by_language(text)
 
-    # Count SMiShing keyword matches
     smishing_count = sum(1 for kw in smishing_keywords if kw in lower_text)
-    # Count Other Scam keyword matches
     other_scam_count = sum(1 for kw in other_scam_keywords if kw in lower_text)
 
-    # Base boost amounts
     smishing_boost = 0.30 * smishing_count
     other_scam_boost = 0.30 * other_scam_count
 
-    # Check for URLs => +0.35 only to SMiShing
     found_urls = re.findall(r"(https?://[^\s]+)", lower_text)
     if found_urls:
         smishing_boost += 0.35
 
-    # Extract original probabilities
     p_smishing = probabilities["SMiShing"]
     p_other_scam = probabilities["Other Scam"]
     p_legit = probabilities["Legitimate"]
 
-    # Apply boosts
     p_smishing += smishing_boost
     p_other_scam += other_scam_boost
+    p_legit -= (smishing_boost + other_scam_boost)
 
-    # Subtract total boost from 'Legitimate'
-    total_boost = smishing_boost + other_scam_boost
-    p_legit -= total_boost
-
-    # Clamp negative probabilities
     if p_smishing < 0:
         p_smishing = 0.0
     if p_other_scam < 0:
@@ -98,14 +78,12 @@ def boost_probabilities(probabilities: dict, text: str):
     if p_legit < 0:
         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:
-        # fallback if everything is 0
         p_smishing, p_other_scam, p_legit = 0.0, 0.0, 1.0
 
     return {
@@ -116,13 +94,6 @@ def boost_probabilities(probabilities: dict, text: str):
     }
 
 def smishing_detector(text, image):
-    """
-    Main function called by Gradio.
-    1. Combine user text + OCR text (if an image is provided).
-    2. Zero-shot classify => base probabilities.
-    3. Apply language detection & translation if needed, then boost logic.
-    4. Return final classification.
-    """
     combined_text = text or ""
     if image is not None:
         ocr_text = pytesseract.image_to_string(image, lang="spa+eng")
@@ -138,51 +109,29 @@ def smishing_detector(text, image):
             "urls_found": []
         }
 
-    # 1. Zero-shot classification
     result = classifier(
         sequences=combined_text,
         candidate_labels=CANDIDATE_LABELS,
         hypothesis_template="This message is {}."
     )
     original_probs = dict(zip(result["labels"], result["scores"]))
-
-    # 2. Boost logic (including language detection + translation)
     boosted = boost_probabilities(original_probs, combined_text)
-    final_label = max(boosted, key=boosted.get) if not isinstance(boosted.get("detected_lang"), float) else "Legitimate"
-    # to avoid conflict, let's store the detected language separately:
-    detected_lang = boosted.pop("detected_lang", "en")
-
-    # We have p_smishing, p_other_scam, p_legit left in boosted
     final_label = max(boosted, key=boosted.get)
     final_confidence = round(boosted[final_label], 3)
+    detected_lang = boosted.pop("detected_lang", "en")
 
-    # 3. Identify which keywords & URLs we found
     lower_text = combined_text.lower()
-    # If we detected Spanish, we used the translated keywords to do matching. But let's also show them:
-    # For demonstration, let's just show the "English or Spanish" keywords. The code to show them in output
-    # can be the same as before, or you can do a second pass with the same logic from boost_probabilities.
-    found_urls = re.findall(r"(https?://[^\s]+)", lower_text)
-
-    # We'll do a quick second pass on actual matched keywords so user sees them
-    # - If language is es => we used translated Spanish keywords, let's do the same for display
-    # - If language is en => we used the original English lists
-    if detected_lang == "es":
-        smishing_keys, scam_keys, _ = get_keywords_by_language(combined_text)
-    else:
-        smishing_keys, scam_keys, _ = (SMISHING_KEYWORDS, OTHER_SCAM_KEYWORDS, "en")
+    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()
-        },
+        "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,