Index Of Megamind Updated -

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 })

from elasticsearch import Elasticsearch

if __name__ == "__main__": unittest.main() Integration tests will be written to ensure that the entire system is functioning correctly.

return data The indexing engine will be implemented using Elasticsearch and will be responsible for creating and maintaining the index of Megamind-related content. index of megamind updated

import unittest from data_collector import collect_data from indexing_engine import create_index, update_index

@app.route("/search", methods=["GET"]) def search(): query = request.args.get("query") es = Elasticsearch() response = es.search(index="megamind-index", body={ "query": { "match": { "title": query } } })

class TestDataCollector(unittest.TestCase): def test_collect_data(self): data = collect_data() self.assertIsNotNone(data) data = [] for source in sources: response = requests

from flask import Flask, request, jsonify from elasticsearch import Elasticsearch

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.

app = Flask(__name__)

def collect_data(): # Collect data from APIs and web scraping sources = [ "https://example.com/megamind-api", "https://example.com/megamind-web-page" ]

import unittest from app import app