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| import requests | |
| import os | |
| import re | |
| HF_API_KEY = os.getenv("HF_API_KEY") # Hugging Face free account | |
| HF_HEADERS = {"Authorization": f"Bearer {HF_API_KEY}"} | |
| MODELS = { | |
| "ai_detector": "roberta-base-openai-detector", | |
| "sentiment": "finiteautomata/bertweet-base-sentiment-analysis", | |
| "spam": "mrm8488/bert-tiny-finetuned-sms-spam-detection", | |
| } | |
| # Suspicious patterns to look for | |
| SUSPICIOUS_PATTERNS = [ | |
| r"verify your account", | |
| r"urgent action", | |
| r"click here", | |
| r"reset (your )?password", | |
| r"confirm (your )?identity", | |
| r"bank account", | |
| r"invoice", | |
| r"payment (required|overdue|failed|method expired)", | |
| r"unauthorized login", | |
| r"compromised", | |
| r"final reminder", | |
| r"account (suspended|deactivated|locked)", | |
| r"update your (information|details|billing)", | |
| r"legal action", | |
| ] | |
| def query_hf(model, text): | |
| url = f"https://api-inference.huggingface.co/models/{model}" | |
| try: | |
| res = requests.post(url, headers=HF_HEADERS, json={"inputs": text[:1000]}) | |
| return res.json() | |
| except Exception: | |
| return None | |
| def analyze_body(text): | |
| findings = [] | |
| score = 0 | |
| body_lower = text.lower() | |
| # --- 1. Suspicious keyword detection --- | |
| for pattern in SUSPICIOUS_PATTERNS: | |
| matches = re.findall(pattern, body_lower) | |
| for match in matches: | |
| findings.append(f"Suspicious phrase detected: \"{match}\"") | |
| score += 20 # weight for suspicious phrase | |
| # --- 2. AI-generated text detection --- | |
| result = query_hf(MODELS["ai_detector"], text) | |
| if result and isinstance(result, list) and len(result) > 0: | |
| label = result[0]["label"] | |
| confidence = result[0]["score"] | |
| findings.append(f"Body: AI Detector → {label} (confidence {confidence:.2f})") | |
| # No score impact yet (just informational) | |
| # --- 3. Sentiment analysis --- | |
| result = query_hf(MODELS["sentiment"], text) | |
| if result and isinstance(result, list) and len(result) > 0: | |
| label = result[0]["label"] | |
| confidence = result[0]["score"] | |
| findings.append(f"Body: Sentiment → {label} (confidence {confidence:.2f})") | |
| if label.lower() in ["negative"]: | |
| score += 10 # negative/urgent tone adds risk | |
| # --- 4. Spam vs Ham detection --- | |
| result = query_hf(MODELS["spam"], text) | |
| if result and isinstance(result, list) and len(result) > 0: | |
| label = result[0]["label"] | |
| confidence = result[0]["score"] | |
| findings.append(f"Body: Spam Detector → {label} (confidence {confidence:.2f})") | |
| if label.lower() == "spam": | |
| score += 20 # spam classification increases risk | |
| if not findings: | |
| return ["No suspicious content detected in body."], 0 | |
| return findings, score | |