rollback

app.py

@@ -7,12 +7,6 @@ from langdetect import detect
 from deep_translator import GoogleTranslator
 import openai
 import os
-import io
-import requests
-import json
-
-# For text-to-speech
-from gtts import gTTS
 
 # Set your OpenAI API key
 openai.api_key = os.getenv("OPENAI_API_KEY")
@@ -32,39 +26,10 @@ model_name = "joeddav/xlm-roberta-large-xnli"
 classifier = pipeline("zero-shot-classification", model=model_name)
 CANDIDATE_LABELS = ["SMiShing", "Other Scam", "Legitimate"]
 
-def tts_explanation(explanation: str, detected_lang: str):
+def get_keywords_by_language(text: str):
     """
-    Generate TTS audio from the final explanation text.
-    We'll choose English or Spanish voices in gTTS, but cannot guarantee 
-    a specific "female" voice. We'll do a best approximation.
-
-    - If text is Spanish: set lang="es"
-    - If text is English (or other): set lang="en"
-    - We'll set tld="co.uk" for a British accent that might sound female. 
-      Adjust if needed or switch to a more advanced TTS service.
+    Detect language using langdetect and translate keywords if needed.
     """
-    # Choose language for gTTS
-    if detected_lang == "es":
-        lang_code = "es"
-        tld = "com"
-    else:
-        lang_code = "en"
-        # Attempt a 'comforting female' accent:
-        # gTTS doesn't let you pick male/female directly, but you can pick a TLD for a different accent
-        tld = "co.uk"
-
-    try:
-        tts = gTTS(text=explanation, lang=lang_code, tld=tld, slow=False)
-        mp3_bytes = io.BytesIO()
-        tts.write_to_fp(mp3_bytes)
-        mp3_bytes.seek(0)
-        return mp3_bytes
-    except Exception as e:
-        print("TTS generation error:", e)
-        # If TTS fails, return an empty buffer
-        return io.BytesIO()
-
-def get_keywords_by_language(text: str):
     snippet = text[:200]
     try:
         detected_lang = detect(snippet)
@@ -83,6 +48,9 @@ def get_keywords_by_language(text: str):
         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)
 
@@ -92,10 +60,7 @@ def boost_probabilities(probabilities: dict, text: str):
     smishing_boost = 0.30 * smishing_count
     other_scam_boost = 0.30 * other_scam_count
 
-    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
-    )
+    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 found_urls:
         smishing_boost += 0.35
 
@@ -112,6 +77,7 @@ def boost_probabilities(probabilities: dict, text: str):
     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
@@ -128,6 +94,10 @@ def boost_probabilities(probabilities: dict, text: str):
     }
 
 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."}
 
@@ -149,6 +119,7 @@ def query_llm_for_classification(raw_message: str) -> dict:
         )
         raw_reply = response["choices"][0]["message"]["content"].strip()
 
+        import json
         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}"}
@@ -159,13 +130,19 @@ def query_llm_for_classification(raw_message: str) -> dict:
         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
@@ -175,6 +152,7 @@ def incorporate_llm_label(boosted: dict, llm_label: str) -> dict:
         for k in boosted:
             boosted[k] /= total
     else:
+        # fallback
         boosted["Legitimate"] = 1.0
         boosted["SMiShing"] = 0.0
         boosted["Other Scam"] = 0.0
@@ -194,14 +172,21 @@ def query_llm_for_explanation(
     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).
+    """
+    # Decide the language for final explanation
     if detected_lang == "es":
+        # Spanish
         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."
+            "Termina con la etiqueta final. "
         )
     else:
+        # Default to English
         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 "
@@ -237,6 +222,12 @@ URLs => {found_urls}
         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:
@@ -256,6 +247,7 @@ def smishing_detector(input_type, text, image):
             "final_explanation": "No text provided"
         }
 
+    # 1. Local zero-shot classification
     local_result = classifier(
         sequences=combined_text,
         candidate_labels=CANDIDATE_LABELS,
@@ -263,33 +255,38 @@ def smishing_detector(input_type, text, image):
     )
     original_probs = {k: float(v) for k, v in zip(local_result["labels"], local_result["scores"])}
 
+    # 2. Basic boosting from keywords & URLs
     boosted = boost_probabilities(original_probs, combined_text)
     detected_lang = boosted.pop("detected_lang", "en")
 
+    # Convert to float only
     for k in boosted:
         boosted[k] = float(boosted[k])
 
     local_label = max(boosted, key=boosted.get)
     local_conf = round(boosted[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 = incorporate_llm_label(boosted, llm_label)
 
+    # Now we have updated probabilities
     final_label = max(boosted, key=boosted.get)
     final_confidence = round(boosted[final_label], 3)
 
+    # 5. Gather found keywords & URLs
     lower_text = combined_text.lower()
     smishing_keys, scam_keys, _ = get_keywords_by_language(combined_text)
-    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
-    )
+
+    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)
     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 LLM explanation (in detected_lang)
     final_explanation = query_llm_for_explanation(
         text=combined_text,
         final_label=final_label,
@@ -320,35 +317,24 @@ def smishing_detector(input_type, text, image):
         "final_explanation": final_explanation,
     }
 
-###
-# Combined function to produce both text (JSON) and TTS audio
-###
-def classify_and_tts(input_type, text, image):
+#
+# Gradio interface with dynamic visibility
+#
+def toggle_inputs(choice):
     """
-    1. Perform the classification logic (smishing_detector).
-    2. Generate TTS audio from the final explanation in a comforting female voice.
-    3. Return both the JSON result & the audio bytes.
+    Return updates for (text_input, image_input) based on the radio selection.
     """
-    result = smishing_detector(input_type, text, image)
-    final_explanation = result["final_explanation"]
-    detected_lang = result.get("detected_language", "en")
-
-    # Generate TTS from final_explanation
-    audio_data = tts_explanation(final_explanation, detected_lang)
-    # Return both
-    return result, audio_data
-
-
-def toggle_inputs(choice):
     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 with LLM-Enhanced Logic + TTS Explanation")
-
+    gr.Markdown("## SMiShing & Scam Detector with LLM-Enhanced Logic (Multilingual Explanation)")
+    
     with gr.Row():
         input_type = gr.Radio(
             choices=["Text", "Screenshot"], 
@@ -360,14 +346,16 @@ with gr.Blocks() as demo:
         lines=3,
         label="Paste Suspicious SMS Text",
         placeholder="Type or paste the message here...",
-        visible=True
+        visible=True  # default
     )
+
     image_input = gr.Image(
         type="pil",
         label="Upload Screenshot",
-        visible=False
+        visible=False  # hidden by default
     )
 
+    # Whenever input_type changes, toggle which input is visible
     input_type.change(
         fn=toggle_inputs,
         inputs=input_type,
@@ -375,17 +363,15 @@ with gr.Blocks() as demo:
         queue=False
     )
 
+    # Button to run classification
     analyze_btn = gr.Button("Classify")
+    output_json = gr.JSON(label="Result")
 
-    # We'll show the classification JSON + TTS audio
-    output_json = gr.JSON(label="Classification Result")
-    audio_output = gr.Audio(label="TTS Explanation")
-
-    # We call classify_and_tts, which returns (dict_result, audio_data)
+    # On button click, call the smishing_detector
     analyze_btn.click(
-        fn=classify_and_tts,
+        fn=smishing_detector,
         inputs=[input_type, text_input, image_input],
-        outputs=[output_json, audio_output]
+        outputs=output_json
     )
 
 if __name__ == "__main__":

app.py.bestoftues

@@ -1,380 +0,0 @@
-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
-
-# Set your OpenAI API key
-openai.api_key = os.getenv("OPENAI_API_KEY")
-
-# 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]+|\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 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 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()
-
-        import json
-        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
-
-    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).
-    """
-    # Decide the language for final explanation
-    if detected_lang == "es":
-        # Spanish
-        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:
-        # Default to English
-        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
-    boosted = boost_probabilities(original_probs, combined_text)
-    detected_lang = boosted.pop("detected_lang", "en")
-
-    # Convert to float only
-    for k in boosted:
-        boosted[k] = float(boosted[k])
-
-    local_label = max(boosted, key=boosted.get)
-    local_conf = round(boosted[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 = incorporate_llm_label(boosted, llm_label)
-
-    # Now we have updated probabilities
-    final_label = max(boosted, key=boosted.get)
-    final_confidence = round(boosted[final_label], 3)
-
-    # 5. Gather found keywords & URLs
-    lower_text = combined_text.lower()
-    smishing_keys, scam_keys, _ = get_keywords_by_language(combined_text)
-
-    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)
-    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 LLM explanation (in detected_lang)
-    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
-    )
-
-    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.items()},
-        "label": final_label,
-        "confidence": final_confidence,
-        "smishing_keywords_found": found_smishing,
-        "other_scam_keywords_found": found_other_scam,
-        "urls_found": found_urls,
-        "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":
-        # 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 with LLM-Enhanced Logic (Multilingual Explanation)")
-    
-    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__":
-    if not openai.api_key:
-        print("WARNING: OPENAI_API_KEY not set. LLM calls will fail or be skipped.")
-    demo.launch()