return data The indexing engine will be implemented using Elasticsearch and will be responsible for creating and maintaining the index of Megamind-related content.
def collect_data(): # Collect data from APIs and web scraping sources = [ "https://example.com/megamind-api", "https://example.com/megamind-web-page" ]
data = [] for source in sources: response = requests.get(source) soup = BeautifulSoup(response.content, 'html.parser') # Extract relevant data data.append({ "title": soup.find("title").text, "description": soup.find("description").text }) index of megamind updated
class TestSearchInterface(unittest.TestCase): def test_search(self): tester = app.test_client() response = tester.get("/search?query=Test") self.assertEqual(response.status_code, 200)
import unittest from data_collector import collect_data from indexing_engine import create_index, update_index return data The indexing engine will be implemented
def test_update_index(self): data = [{"title": "Test", "description": "Test"}] update_index(data) self.assertTrue(True)
def create_index(): es = Elasticsearch() es.indices.create(index="megamind-index", body={ "mappings": { "properties": { "title": {"type": "text"}, "description": {"type": "text"} } } }) index of megamind updated
def update_index(data): es = Elasticsearch() for item in data: es.index(index="megamind-index", body=item) The search interface will be implemented using a web application framework (e.g., Flask) and will provide a simple search form for users to find Megamind-related content.