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Ensure high accuracy in scraped product information and pricing.
Access supermarket data from multiple countries effortlessly and efficiently.
Retrieve detailed grocery product listings, including descriptions and images.
Monitor price changes and promotional offers across various stores.
Tailor data scraping to focus on specific product categories.
Simple integration process with existing systems for seamless use.
Set automated schedules for regular data collection without manual intervention.
Consistent and dependable data extraction with minimal downtime or errors.
Utilize Milk Basket Product Scraping to analyze competitor pricing strategies and adjust marketing plans accordingly for better market positioning.
Integrate the Milk Basket Order Data API to streamline inventory management, ensuring stock levels meet customer demands effectively and efficiently.
Leverage Milk Basket Customer Reviews Scraping to gather feedback and improve product offerings based on customer preferences and satisfaction levels.
Employ the Milk Basket Product Data API to integrate product information into e-commerce platforms, enhancing the online shopping experience for customers.
Implement Automated Milk Basket Data Extraction to save time and resources by regularly collecting data without manual effort, ensuring data freshness.
Description: Fetch detailed product information based on various criteria.
keyword (string): Search term to find products.
category (string): Filter by specific product categories.
sort (string): Sorting options (e.g., price, popularity).
page (int): Pagination for large datasets.
Response: JSON object containing product name, ID, price, rating, and description.
Description: Retrieve detailed information for a specific product using its unique ID.
product_id (string): Unique ID for the product.
Response: JSON object with product details including title, price, features, and customer reviews.
Description: Fetch customer reviews and ratings for a specific product.
product_id (string): Unique ID for the product.
Response: JSON object containing reviewer names, ratings, review texts, and helpfulness votes.
Description: Retrieve current offers, discounts, and availability for a specific product.
product_id (string): Unique ID for the product.
Response: JSON object with offer details, including price, discount percentages, and stock availability.
Description: Get recommendations for related products based on a specific product.
product_id (string): Unique ID for the product.
Response: JSON object containing a list of related product IDs and names.
Description: Retrieve a list of available product categories on Milk Basket.
Response: JSON object containing category names and IDs for filtering products.
Description: Search for products based on various criteria like keywords and filters.
query (string): Search term to find products.
sort (string): Sorting options (e.g., price, rating).
page (int): Pagination for large datasets.
Response: JSON object with a list of products matching the search criteria.
Description: Retrieve historical pricing data for a specific product.
product_id (string): Unique ID for the product.
Response: JSON object containing price changes over time, including dates and corresponding prices.
Description: Fetch store locations that offer delivery or pickup options.
zipcode (string): ZIP code for location-based searching.
Response: JSON object containing store names, addresses, and operating hours.
Description: Manage the shopping cart for a user session.
user_id (string): Unique identifier for the user.
action (string): Action to perform (add, remove, update).
product_id (string): Unique ID of the product.
Response: JSON object confirming cart status and updated totals.
Description: Process the checkout for a user’s cart.
user_id (string): Unique identifier for the user.
payment_info (object): Payment details for transaction processing.
Response: JSON object containing order confirmation details and estimated delivery time.
All responses are returned in JSON format for easy integration into your application.
from flask import Flask, jsonify, request
app = Flask(__name__)
# Sample data for demonstration purposes
products = [
{"id": "1", "name": "Apple", "price": 1.00, "rating": 4.5, "description": "Fresh apples"},
{"id": "2", "name": "Banana", "price": 0.50, "rating": 4.0, "description": "Ripe bananas"},
# Add more products as needed
]
# Sample data for reviews
reviews = {
"1": [
{"reviewer": "Alice", "rating": 5, "text": "Great apples!", "helpfulness": 10},
{"reviewer": "Bob", "rating": 4, "text": "Very fresh.", "helpfulness": 5}
],
"2": [
{"reviewer": "Charlie", "rating": 4, "text": "Tasty bananas.", "helpfulness": 3}
]
}
@app.route('/products', methods=['GET'])
def get_products():
keyword = request.args.get('keyword', '')
category = request.args.get('category', '')
sort = request.args.get('sort', 'name')
page = int(request.args.get('page', 1))
# Filter and sort products (basic implementation)
filtered_products = [p for p in products if keyword.lower() in p['name'].lower()]
sorted_products = sorted(filtered_products, key=lambda x: x[sort])
return jsonify(sorted_products)
@app.route('/product/', methods=['GET'])
def get_product(product_id):
product = next((p for p in products if p['id'] == product_id), None)
if product:
return jsonify(product)
return jsonify({"error": "Product not found"}), 404
@app.route('/reviews', methods=['GET'])
def get_reviews():
product_id = request.args.get('product_id')
product_reviews = reviews.get(product_id, [])
return jsonify(product_reviews)
@app.route('/offers', methods=['GET'])
def get_offers():
product_id = request.args.get('product_id')
# This is a placeholder; implement offer logic as needed
offers = {"product_id": product_id, "offers": [{"discount": "10%", "availability": "In Stock"}]}
return jsonify(offers)
@app.route('/related', methods=['GET'])
def get_related():
product_id = request.args.get('product_id')
# This is a placeholder; implement related product logic as needed
related_products = [{"id": "3", "name": "Orange"}, {"id": "4", "name": "Grapes"}]
return jsonify(related_products)
@app.route('/categories', methods=['GET'])
def get_categories():
categories = [{"id": "1", "name": "Fruits"}, {"id": "2", "name": "Vegetables"}]
return jsonify(categories)
@app.route('/search', methods=['GET'])
def search_products():
query = request.args.get('query', '')
sort = request.args.get('sort', 'name')
page = int(request.args.get('page', 1))
# Implement search logic (basic implementation)
searched_products = [p for p in products if query.lower() in p['name'].lower()]
sorted_products = sorted(searched_products, key=lambda x: x[sort])
return jsonify(sorted_products)
@app.route('/price-history', methods=['GET'])
def get_price_history():
product_id = request.args.get('product_id')
# This is a placeholder; implement price history logic as needed
price_history = [{"date": "2023-10-01", "price": 1.00}, {"date": "2023-10-10", "price": 1.20}]
return jsonify(price_history)
@app.route('/store-locations', methods=['GET'])
def get_store_locations():
zipcode = request.args.get('zipcode')
# This is a placeholder; implement location logic as needed
locations = [{"name": "Store A", "address": "123 Main St", "hours": "9am-9pm"}]
return jsonify(locations)
@app.route('/cart', methods=['POST'])
def manage_cart():
user_id = request.json.get('user_id')
action = request.json.get('action')
product_id = request.json.get('product_id')
# Implement cart management logic here
return jsonify({"status": "success", "message": f"Product {action} to cart for user {user_id}."})
@app.route('/checkout', methods=['POST'])
def checkout():
user_id = request.json.get('user_id')
payment_info = request.json.get('payment_info')
# Implement checkout logic here
return jsonify({"status": "success", "message": "Checkout completed successfully."})
if __name__ == '__main__':
app.run(debug=True)
Optimize your product data with our Milk Basket Grocery Data Scraping API. Our powerful Milk Basket Scraping API allows businesses to efficiently scrape Milk Basket product data from various supermarkets, ensuring you stay updated with the latest product information. With our comprehensive Grocery Data Scraping Services, you can access critical insights into pricing, availability, and promotions across multiple regions. This enables informed decision-making and enhances competitive advantage in the ever-evolving market. Trust our reliable solutions to streamline your data extraction processes, ensuring you have the most accurate and timely data at your fingertips. Experience the difference with our Milk Basket data solutions today!
Whatever your project size is, we will handle it well with all the standards fulfilled! We are here to give 100% satisfaction.