added initial app

app.py

@@ -1,8 +1,154 @@
 import gradio as gr
+import pytesseract
+from PIL import Image
+from transformers import pipeline
+import re
 
-def greet(name):
-    return "Hello " + name + "!!"
+# 1. Load scam keywords from file
+#    Each line in 'scam_keywords.txt' is treated as a separate keyword.
+with open("scam_keywords.txt", "r", encoding="utf-8") as f:
+    SCAM_KEYWORDS = [line.strip().lower() for line in f if line.strip()]
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# 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 keyword_and_url_boost(probabilities, text):
+    """
+    Adjust final probabilities if certain scam-related keywords or URLs appear.
+      - probabilities: dict, label -> original probability
+      - text: the combined text from user input + OCR
+
+    Returns an updated dict of probabilities that sum to 1.
+    """
+    lower_text = text.lower()
+
+    # 1. Check scam keywords
+    keyword_count = sum(1 for kw in SCAM_KEYWORDS if kw in lower_text)
+    keyword_boost = 0.05 * keyword_count  # 5% per found keyword
+    keyword_boost = min(keyword_boost, 0.30)  # cap at +30%
+
+    # 2. Check if there's any URL (simple regex for http/https)
+    found_urls = re.findall(r"(https?://[^\s]+)", lower_text)
+    url_boost = 0.0
+    if found_urls:
+        # For demonstration: a flat +10% if a URL is found
+        url_boost = 0.10
+
+    # 3. Combine total boost
+    total_boost = keyword_boost + url_boost
+    total_boost = min(total_boost, 0.40)  # cap at +40%
+
+    if total_boost <= 0:
+        return probabilities  # no change if no keywords/URLs found
+
+    smishing_prob = probabilities["SMiShing"]
+    other_scam_prob = probabilities["Other Scam"]
+    legit_prob = probabilities["Legitimate"]
+
+    # 4. Distribute the total boost equally to "SMiShing" and "Other Scam"
+    half_boost = total_boost / 2.0
+    smishing_boosted = smishing_prob + half_boost
+    other_scam_boosted = other_scam_prob + half_boost
+    legit_boosted = legit_prob
+
+    # 5. Re-normalize so they sum to 1
+    total = smishing_boosted + other_scam_boosted + legit_boosted
+    if total > 0:
+        smishing_final = smishing_boosted / total
+        other_scam_final = other_scam_boosted / total
+        legit_final = legit_boosted / total
+    else:
+        smishing_final = 0.0
+        other_scam_final = 0.0
+        legit_final = 1.0
+
+    return {
+        "SMiShing": smishing_final,
+        "Other Scam": other_scam_final,
+        "Legitimate": legit_final
+    }
+
+def smishing_detector(text, image):
+    """
+    1. Extract text from the image (OCR) if provided.
+    2. Combine with user-entered text.
+    3. Zero-shot classification -> base probabilities.
+    4. Keyword + URL boost -> adjusted probabilities.
+    5. Return final label, confidence, etc.
+    """
+    # Step 1: OCR if there's an image
+    combined_text = text if text else ""
+    if image is not None:
+        ocr_text = pytesseract.image_to_string(image, lang="spa+eng")
+        combined_text += " " + ocr_text
+
+    # Clean text
+    combined_text = combined_text.strip()
+    if not combined_text:
+        return {
+            "text_used_for_classification": "(none)",
+            "label": "No text provided",
+            "confidence": 0.0,
+            "keywords_found": [],
+            "urls_found": []
+        }
+
+    # Step 2: 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"]))
+
+    # Step 3: Keyword + URL boost
+    boosted_probs = keyword_and_url_boost(original_probs, combined_text)
+
+    # Step 4: Pick final label after boost
+    final_label = max(boosted_probs, key=boosted_probs.get)
+    final_confidence = round(boosted_probs[final_label], 3)
+
+    # Step 5: Identify which keywords and URLs were found
+    lower_text = combined_text.lower()
+    found_keywords = [kw for kw in SCAM_KEYWORDS if kw in lower_text]
+    found_urls = re.findall(r"(https?://[^\s]+)", lower_text)
+
+    return {
+        "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_probs.items()},
+        "label": final_label,
+        "confidence": final_confidence,
+        "keywords_found": found_keywords,
+        "urls_found": found_urls,
+    }
+
+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)"
+        )
+    ],
+    outputs="json",
+    title="SMiShing & Scam Detector (Keyword + URL Boost)",
+    description="""
+This tool classifies messages as SMiShing, Other Scam, or Legitimate using a zero-shot model
+(joeddav/xlm-roberta-large-xnli). It also checks for certain "scam keywords" (loaded from a file)
+and any URLs, boosting the probability of a scam label if found.
+Supports English & Spanish text (OCR included).
+""",
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    demo.launch()