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import requests
from typing import List, Dict, Optional
class ShopSmartAgent:
def __init__(self):
self.serpapi_key = os.getenv("SERPAPI_API_KEY")
def search_products(self, query: str, max_price: Optional[float] = None) -> List[Dict]:
fallback_products = [
{
"name": "Samsung Galaxy S23",
"price": 699,
"rating": 4.5,
"reviews_count": 2847,
"image_url": "",
"url": "https://www.samsung.com/us/smartphones/galaxy-s23/",
"seller": "Samsung",
"seller_rating": 4.8
},
{
"name": "iPhone 15",
"price": 799,
"rating": 4.6,
"reviews_count": 3254,
"image_url": "",
"url": "https://www.apple.com/iphone-15/",
"seller": "Apple Store",
"seller_rating": 4.9
},
{
"name": "Google Pixel 8",
"price": 599,
"rating": 4.3,
"reviews_count": 1892,
"image_url": "",
"url": "https://store.google.com/",
"seller": "Google Store",
"seller_rating": 4.7
}
]
if not self.serpapi_key:
print("SERPAPI_API_KEY not set. Using fallback products.")
return [p for p in fallback_products if max_price is None or p["price"] <= max_price]
params = {
"engine": "google_shopping",
"q": query,
"api_key": self.serpapi_key,
"gl": "us",
"hl": "en",
"num": 20,
"no_cache": "true",
}
try:
response = requests.get("https://serpapi.com/search", params=params, timeout=20)
response.raise_for_status()
data = response.json()
shopping_results = data.get("shopping_results", [])
normalized_products = []
for item in shopping_results:
price = item.get("extracted_price")
if price is None:
raw_price = item.get("price", "")
price = self._extract_price_number(raw_price)
if price is None:
continue
if max_price is not None and price > max_price:
continue
normalized_products.append({
"name": item.get("title", "Unknown Product"),
"price": price,
"rating": float(item.get("rating", 0) or 0),
"reviews_count": int(item.get("reviews", 0) or 0),
"image_url": item.get("thumbnail", ""),
"url": item.get("product_link") or item.get("link") or "#",
"seller": item.get("source", "Unknown Seller"),
"seller_rating": 4.5
})
normalized_products = self._dedupe_products(normalized_products)
if not normalized_products:
print("No SerpApi products found. Using fallback products.")
return [p for p in fallback_products if max_price is None or p["price"] <= max_price]
return normalized_products
except Exception as e:
print(f"SerpApi search failed: {e}. Using fallback products.")
return [p for p in fallback_products if max_price is None or p["price"] <= max_price]
def _extract_price_number(self, raw_price: str):
if not raw_price:
return None
cleaned = raw_price.replace("$", "").replace(",", "").strip()
parts = cleaned.split()
try:
return float(parts[0])
except Exception:
return None
def _dedupe_products(self, products: List[Dict]) -> List[Dict]:
seen = set()
deduped = []
for product in products:
name = str(product.get("name", "")).strip().lower()
seller = str(product.get("seller", "")).strip().lower()
key = (name, seller)
if key not in seen:
seen.add(key)
deduped.append(product)
return deduped
def analyze_reviews(self, product_name: str) -> Dict:
name = product_name.lower()
if any(k in name for k in ["iphone", "galaxy", "pixel", "oneplus"]):
return {
"sentiment": "positive",
"pros": ["Strong performance", "Good camera quality", "Reliable everyday use"],
"cons": ["Can be expensive", "Battery life varies by model"],
"summary": "Generally strong smartphone pick with solid mainstream appeal."
}
if any(k in name for k in ["headphone", "earbud", "airpods", "bose", "sony"]):
return {
"sentiment": "positive",
"pros": ["Strong audio quality", "Comfortable design", "Useful everyday features"],
"cons": ["Price may be high", "Battery life varies"],
"summary": "Well-reviewed audio option with strong consumer appeal."
}
if any(k in name for k in ["mask", "serum", "cleanser", "tonic", "moisturizer"]):
return {
"sentiment": "mixed",
"pros": ["Popular product type", "Affordable options available", "Easy to compare"],
"cons": ["Results vary by skin type", "Some products may be overhyped"],
"summary": "Promising beauty option, but personal fit matters more than ratings alone."
}
return {
"sentiment": "mixed",
"pros": ["Popular option", "Reasonable value", "Accessible price range"],
"cons": ["Not perfect for every user", "Feature tradeoffs may apply"],
"summary": "Solid option overall with a few tradeoffs depending on user needs."
}
def assess_risk(self, product: Dict) -> Dict:
risk_score = 0
risk_factors = []
if product["price"] < 50:
risk_score += 2
risk_factors.append("Unusually low price")
if product["rating"] and product["rating"] < 3.5:
risk_score += 3
risk_factors.append("Low customer rating")
if product["reviews_count"] < 50:
risk_score += 2
risk_factors.append("Limited reviews available")
if product.get("seller_rating", 5) < 4.0:
risk_score += 2
risk_factors.append("Low seller rating")
risk_level = "Low" if risk_score <= 2 else "Medium" if risk_score <= 5 else "High"
return {
"level": risk_level,
"score": risk_score,
"factors": risk_factors
}
def rank_products(self, products: List[Dict], user_query: str) -> List[Dict]:
if not products:
return []
query_lower = user_query.lower()
for product in products:
product["risk"] = self.assess_risk(product)
review_weight = max(product["reviews_count"], 1)
rating_weight = max(product["rating"], 0.1)
price_weight = max(product["price"], 1)
base_score = (rating_weight * review_weight) / price_weight
name_lower = product["name"].lower()
keyword_bonus = 0
for token in query_lower.split():
if token in name_lower:
keyword_bonus += 20
risk_penalty = product["risk"]["score"] * 10
product["value_score"] = base_score + keyword_bonus - risk_penalty
ranked = sorted(products, key=lambda x: (x["value_score"], x["rating"]), reverse=True)
if ranked:
ranked[0]["is_best"] = True
for product in ranked[1:]:
product["is_best"] = False
return ranked
def generate_recommendation(self, product: Dict, is_best: bool = False) -> str:
if is_best:
return "Best overall value"
if product["rating"] >= 4.5:
return "Premium option with strong reviews"
elif product["price"] < 600:
return "Budget-friendly choice with good value"
else:
return "Solid mid-range option"
def process_query(self, query: str, max_price: float = None) -> Dict:
products = self.search_products(query, max_price)
ranked_products = self.rank_products(products, query)
for product in ranked_products[:3]:
product["review_analysis"] = self.analyze_reviews(product["name"])
product["recommendation"] = self.generate_recommendation(
product,
product.get("is_best", False)
)
return {
"query": query,
"total_found": len(ranked_products),
"products": ranked_products
} |