patched lang detect bug

app.py

@@ -90,13 +90,11 @@ def boost_probabilities(probabilities: dict, text: str):
 
 def smishing_detector(input_type, text, image):
     """
-    Only use the textbox if input_type == "Text",
-    otherwise perform OCR on the image if input_type == "Screenshot".
+    Main detection function combining text (if 'Text') and OCR (if 'Screenshot').
     """
     if input_type == "Text":
         combined_text = text.strip() if text else ""
     else:
-        # input_type == "Screenshot"
         combined_text = ""
         if image is not None:
             combined_text = pytesseract.image_to_string(image, lang="spa+eng").strip()
@@ -110,7 +108,6 @@ def smishing_detector(input_type, text, image):
             "urls_found": []
         }
 
-    # Zero-shot classification
     result = classifier(
         sequences=combined_text,
         candidate_labels=CANDIDATE_LABELS,
@@ -118,11 +115,18 @@ def smishing_detector(input_type, text, image):
     )
     original_probs = {k: float(v) for k, v in zip(result["labels"], result["scores"])}
 
-    # Boost logic
     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")
+    # 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)
 
@@ -136,12 +140,8 @@ def smishing_detector(input_type, text, image):
     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,

app.py.working_ocr_selection_prepatch

@@ -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):
+    """
+    Only use the textbox if input_type == "Text",
+    otherwise perform OCR on the image if input_type == "Screenshot".
+    """
+    if input_type == "Text":
+        combined_text = text.strip() if text else ""
+    else:
+        # input_type == "Screenshot"
+        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": []
+        }
+
+    # 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)
+    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]
+
+    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()