import os
import sys
import json
import requests
from typing import Optional
from pydantic import BaseModel
from dotenv import load_dotenv
# Ensure repo root path for Upsonic imports
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
from upsonic import Agent, Task
from examples.find_company_website.find_company_website import find_company_website
# --- Config ---
load_dotenv()
SERPER_API_KEY = os.getenv("SERPER_API_KEY")
if not SERPER_API_KEY:
raise ValueError("SERPER_API_KEY missing in .env file.")
# --- Response Model ---
class ProductInfo(BaseModel):
product_name: str
product_price: Optional[str]
product_brand: Optional[str]
availability: Optional[str]
url: Optional[str]
# --- Website Scraping Tool using Serper ---
def website_scraping(url: str) -> dict:
"""
Use Serper API to fetch website content.
Returns a dict with {url, content}.
"""
endpoint = "https://google.serper.dev/scrape"
headers = {"X-API-KEY": SERPER_API_KEY, "Content-Type": "application/json"}
payload = {"url": url}
try:
resp = requests.post(endpoint, headers=headers, json=payload, timeout=30)
resp.raise_for_status()
data = resp.json()
return {"url": url, "content": data.get("text", "")}
except Exception as e:
print(f"Serper scraping failed for {url}: {e}")
return {"url": url, "content": ""}
# --- Agent Setup ---
example_product_agent = Agent(name="example_product_agent")
# --- CLI + Task definition ---
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Find an example product from a company's website using Serper & LLM.")
parser.add_argument("--company", required=True, help="Company name, e.g. 'Nike', 'Adidas', 'Mavi'")
args = parser.parse_args()
# Define Task prompt
task_prompt = f"""
You are an intelligent agent tasked with finding an example product from {args.company}'s website.
Steps:
1. Use the `find_company_website` tool to get the official company website.
2. Then use `website_scraping` to read the website content.
3. Identify relevant sublinks or sections that likely contain product information (e.g., products, shop, catalog, collections, items).
4. Use `website_scraping` again to fetch those subpages as needed.
5. If you find a valid product page, extract:
- product_name
- product_price
- product_brand
- availability
- url (the product page link)
6. Return the structured data as `ProductInfo`.
If you cannot find a product, retry with different relevant sublinks before giving up.
"""
task = Task(
description=task_prompt.strip(),
tools=[website_scraping, find_company_website],
response_format=ProductInfo,
)
result = example_product_agent.do(task)
print("\n" + "="*60)
print("📦 FINAL RESULT")
print("="*60)
print(result.model_dump_json(indent=2))
print("="*60)
