# { "Depends": "py-genlayer:1jb45aa8ynh2a9c9xn3b7qqh8sm5q93hwfp7jqmwsfhh8jpz09h6" } from genlayer import * import json WEB_TRUNCATE = 8000 class PriceGuard(gl.Contract): """ PriceGuard — AI-Powered Price Verification on GenLayer. """ total_checks: u64 checks: TreeMap[u64, str] user_checks: TreeMap[Address, u64] total_great_deals: u64 total_fair: u64 total_overpriced: u64 total_scams: u64 def __init__(self): self.total_checks = 0 self.total_great_deals = 0 self.total_fair = 0 self.total_overpriced = 0 self.total_scams = 0 # ═══════════════════════════════════════ # HELPERS # ═══════════════════════════════════════ def _normalize_ai_result(self, raw_result: dict, product_name_fallback: str, listed_price_fallback: str) -> str: normalized = { "verdict": str(raw_result.get("verdict", "FAIR_PRICE")).strip().upper().replace(" ", "_"), "product_name": str(raw_result.get("product_name", product_name_fallback))[:200], "listed_price": str(raw_result.get("listed_price", listed_price_fallback))[:50], "estimated_fair_price": str(raw_result.get("estimated_fair_price", "N/A"))[:50], "savings_percent": int(raw_result.get("savings_percent", 0)), "reasoning": str(raw_result.get("reasoning", ""))[:500], "price_factors": sorted([str(f).strip().lower() for f in raw_result.get("price_factors", [])][:5]), } return json.dumps(normalized, sort_keys=True) def _record_check(self, check_id: u64, result: dict, url: str, store_name: str): verdict = result.get("verdict", "FAIR_PRICE") submitter = gl.message.sender_address record = { "id": int(check_id), "url": url, "category": result.get("_category", ""), "submitter": str(submitter), "verdict": verdict, "product_name": result.get("product_name", "Unknown"), "listed_price": result.get("listed_price", "N/A"), "estimated_fair_price": result.get("estimated_fair_price", "N/A"), "savings_percent": result.get("savings_percent", 0), "reasoning": result.get("reasoning", ""), "price_factors": result.get("price_factors", []), "store_name": store_name, } self.checks[check_id] = json.dumps(record) self.total_checks += 1 current_user_checks = int(self.user_checks.get(submitter, 0)) self.user_checks[submitter] = current_user_checks + 1 if verdict == "GREAT_DEAL": self.total_great_deals += 1 elif verdict == "FAIR_PRICE": self.total_fair += 1 elif verdict == "OVERPRICED": self.total_overpriced += 1 elif verdict == "SCAM_ALERT": self.total_scams += 1 # ═══════════════════════════════════════ # CHECK PRICE BY URL # ═══════════════════════════════════════ @gl.public.write def check_price(self, product_url: str, category: str) -> dict: if not product_url: raise Rollback("Product URL cannot be empty") if not product_url.startswith(("http://", "https://")): raise Rollback("Product URL must start with http:// or https://") VALID_CATEGORIES = ["electronics", "clothing", "home", "beauty", "sports", "food", "other"] if category not in VALID_CATEGORIES: raise Rollback(f"Invalid category. Choose from: {', '.join(VALID_CATEGORIES)}") check_id = self.total_checks def nondet_block(): web_data = str(gl.get_webpage(product_url))[:WEB_TRUNCATE] task = f"""You are PriceGuard, an expert AI price analyst. TASK: Analyze the product page below. Identify the product and price. Determine if the price is fair. CATEGORY: {category} URL: {product_url} PAGE CONTENT: <<>> {web_data} <<>> STEPS: 1. Identify product name, brand, model 2. Extract listed price (include currency) 3. Compare to typical market price for this product 4. Check for scam indicators VERDICT: - "GREAT_DEAL": 20%+ below market, listing seems legit - "FAIR_PRICE": Within ±15% of market - "OVERPRICED": 20%+ above market - "SCAM_ALERT": Suspiciously low or scam indicators Return ONLY JSON: {{"verdict": "GREAT_DEAL"|"FAIR_PRICE"|"OVERPRICED"|"SCAM_ALERT", "product_name": "", "listed_price": "", "estimated_fair_price": "", "savings_percent": , "reasoning": "<2-3 sentences>", "price_factors": ["", ""]}}""" result = gl.exec_prompt(task) try: parsed = json.loads(result) except Exception: parsed = {} return self._normalize_ai_result(parsed, "Unknown", "N/A") result_str = gl.eq_principle_strict_eq(nondet_block) result = json.loads(result_str) result["_category"] = category self._record_check(check_id, result, product_url, "") return result # ═══════════════════════════════════════ # MANUAL PRICE CHECK # ═══════════════════════════════════════ @gl.public.write def check_price_manual(self, product_name: str, listed_price: str, store_name: str, category: str) -> dict: if not product_name or not listed_price: raise Rollback("Product name and price are required") VALID_CATEGORIES = ["electronics", "clothing", "home", "beauty", "sports", "food", "other"] if category not in VALID_CATEGORIES: raise Rollback(f"Invalid category. Choose from: {', '.join(VALID_CATEGORIES)}") check_id = self.total_checks def nondet_block(): task = f"""You are PriceGuard, an expert AI price analyst. TASK: A user found a product and wants to know if the price is fair. PRODUCT: {product_name} LISTED PRICE: {listed_price} STORE: {store_name} CATEGORY: {category} STEPS: 1. What is typical retail price for "{product_name}"? 2. How does {listed_price} compare? 3. Any suspicious indicators? VERDICT: - "GREAT_DEAL": 20%+ below market - "FAIR_PRICE": Within ±15% of market - "OVERPRICED": 20%+ above market - "SCAM_ALERT": Suspiciously low or scam indicators Return ONLY JSON: {{"verdict": "GREAT_DEAL"|"FAIR_PRICE"|"OVERPRICED"|"SCAM_ALERT", "product_name": "{product_name}", "listed_price": "{listed_price}", "estimated_fair_price": "", "savings_percent": , "reasoning": "<2-3 sentences>", "price_factors": ["", ""]}}""" result = gl.exec_prompt(task) try: parsed = json.loads(result) except Exception: parsed = {} return self._normalize_ai_result(parsed, product_name, listed_price) result_str = gl.eq_principle_strict_eq(nondet_block) result = json.loads(result_str) result["_category"] = category self._record_check(check_id, result, "", store_name) return result # ═══════════════════════════════════════ # READ METHODS # ═══════════════════════════════════════ @gl.public.view def get_check(self, check_id: int) -> dict: try: return json.loads(self.checks[check_id]) except: return {} @gl.public.view def get_recent_checks(self, count: int) -> list: results = [] start = max(0, self.total_checks - count) for i in range(start, self.total_checks): try: results.append(json.loads(self.checks[i])) except: pass return results @gl.public.view def get_stats(self) -> dict: total = max(1, self.total_checks) return { "total_checks": int(self.total_checks), "total_great_deals": int(self.total_great_deals), "total_fair": int(self.total_fair), "total_overpriced": int(self.total_overpriced), "total_scams": int(self.total_scams), "scam_rate": round((int(self.total_scams) / total) * 100, 1), } @gl.public.view def get_user_stats(self, user: Address) -> dict: try: checks = int(self.user_checks[user]) except: checks = 0 return {"checks": checks}

Hash	Type	Status		From	To	Method	GenVM Result	Consensus Result	Time
0x2f0a21f6...9cb4c882	Call	FINALIZED	IN	0xAf1D8d...a1DD27	0x16dBE3...c4a335	check_price	ERROR	Accepted	5 months ago
0x80809baf...fd0c26cf	Deploy	FINALIZED	IN	0xAf1D8d...a1DD27	0x16dBE3...c4a335	(constructor)	SUCCESS	Accepted	5 months ago