Today, everybody is familiar with food delivery apps like FoodPanda and Deliveroo. They conveniently allow you to order food from the local restaurants and get it delivered right to your doorstep with your mobile phone. Today, we will understand how you can create a food delivery application with ReactJS. But before you start hire Reactjs developer, there are some essentials you need to have by your side to develop a food delivery app with React successfully. Let’s get started.
Things You Will Need To Develop Food Delivery Application With ReactJS
First of all, you will be required to have a basic knowledge of React Native and Node.js. Following are the packages that we will be using.
React Native 0.61.1
React Native CLI 2.0.1
Node 11.10.1
Yarn 1.17.3
Moreover, you will also need an ngrok and Pusher channels account, as the channels will be used to establish a connection between the driver and the customer, and ngrok will expose the server to the internet.
Creating New Channels
Now is the time to set up new channels app instances if you have not. After that, you should go to the app settings and check the ‘enable client events’ options. This will allow us to trigger events right from the application.
Google Map Platform
To use React Native Maps, we need to set up the Google Maps platform. If you have not used the Google Cloud Console before, you can use Google Map Platform’s Quickstart guide for new users.
It is the fastest way to get going as it will configure everything for you automatically. You can choose which Map products you want to use, and it will be enabled for Android, iOS, and Web API. In this case, we will be selecting Maps and Places under the product selection.
Then, we need to select the project. If this is your first time using Google API, there will be a pre-created project ready. You can select that project or create a new one.
The last step is to set up the billing. For that, you may have to create a billing account if you don’t already have one.
Bootstrapping
Now, it’s time to bootstrap the app. Prepare a starter branch with the code for setting up the navigation and the code for the styles and components. Clone the switch and the repo to the starter branch.
Install the dependencies with ‘yarn install’. Here are the packages that we will use and why we will be using them.
git clone https://github.com/kumarRam/React-Native-Food-Delivery.git FoodDelivery
cd FoodDelivery
git checkout starter
- react-navigation: This will help in implementing navigation in our food delivery application. It will depend on react-native-gesture-handler, react-navigation-stack, react-native-reanimated, and react-native-gesture-handler.
- react-native-simple-stepper: It will render a stepper component for choosing the quantity for a specific order item.
- react-native-permissions: This will be used to request permission to access the data for geolocation.
- React-native-maps-directions: Draws path between two coordinates.
- react-native-maps: Renders maps and markers
- react-native-google-places: This will render Google Maps Places’s place picker modal.
- react-native-geolocation-service: To get the geolocation data
- react-native-geocoding: It converts coordinates into an actual place name.
- react-native-config: It helps in reading config in .env files.
- pusher-js: It will be used as a real time communication channel between the driver and the customer.
- axios: It will be used to make requests for the food lists to the server.
Now, we need to update the .env file at the roof of the project directory with channels and Google Maps API credentials.
CHANNELS_APP_KEY="YOUR CHANNELS APP KEY"
CHANNELS_APP_CLUSTER="YOUR CHANNELS APP CLUSTER"
GOOGLE_API_KEY="YOUR GOOGLE API KEY"
NGROK_HTTPS_URL="YOUR NGROK HTTPS URL"
After that, we need to update android/settings.gradle to include the native files we will be using for the packages. Most of the packages do not have native code, and others support auto-linking, so it would not require including all the native files.
rootProject.name = 'FoodDelivery'
include ':react-native-permissions'
project(':react-native-permissions').projectDir = new File(rootProject.projectDir, '../node_modules/react-native-permissions/android')
include ':react-native-config'
project(':react-native-config').projectDir = new File(rootProject.projectDir, '../node_modules/react-native-config/android')
include ':react-native-google-places'
project(':react-native-google-places').projectDir = new File(rootProject.projectDir, '../node_modules/react-native-google-places/android')
Now, it’s time to update the android/app/build.gradle file.
apply plugin: "com.android.application"
apply from: project(':react-native-config').projectDir.getPath() + "/dotenv.gradle"
Read More Here: https://www.infiraise.com/how-to-develop-food-delivery-application-with-reactjs/
Top comments (1)
Thank you for sharing this insightful information! The food industry continues to innovate in exciting ways, and it’s always a pleasure to be part of that transformation. At our company, we’ve been fortunate to contribute to the development of several restaurant menus, including the Chick-fil-A menu, where we worked on adding new items that catered to emerging customer preferences and seasonal trends. We also had the opportunity to work on the Firehouse Subs menu, where we helped integrate bold flavors and creative options while maintaining the brand's commitment to hearty, satisfying meals.
Menu development today goes beyond just culinary creativity—it also involves technology and data-driven decisions. Here’s how we approach the process:
Consumer Data Analysis: The foundation of developing a new menu item starts with understanding what customers are looking for. Using advanced coding techniques, we can analyze customer preferences through social media trends, customer feedback, and sales data. For instance, by utilizing machine learning algorithms, we can predict flavor profiles that are likely to resonate with target demographics. This data helps us make informed decisions before the first bite of a new dish is even prepared.
Recipe Optimization with Software: Once we have an idea for a new menu item, we use software tools to calculate nutritional values, ingredient sourcing, and pricing. By inputting various ingredients and their costs into a coding platform, we can quickly evaluate the profitability of new items and ensure they align with the restaurant’s pricing strategy.
Operational Efficiency through Automation: We’ve implemented coding solutions that allow for greater efficiency in the kitchen. For example, we create automated systems for inventory management to track ingredient usage, preventing waste and ensuring we always have fresh products on hand. These systems help streamline the process of scaling new menu items across multiple locations.
Consumer Feedback via Digital Platforms: To test new menu items, we leverage digital platforms where customers can provide real-time feedback. By analyzing this data, we can quickly adjust recipes or presentation styles to improve customer satisfaction before full-scale rollouts.
Incorporating technology, from data analysis to automation, helps us craft menus that are not only delicious but also efficient and scalable. It’s exciting to be a part of this innovative process, especially when working with brands like Chick-fil-A and Firehouse Subs. The future of menu development is certainly evolving through the combination of culinary creativity and cutting-edge coding solutions!
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
Sample customer feedback data (Reviews and Ratings)
data = {
'Item': ['Chick-fil-A Chicken Sandwich', 'Firehouse Subs Hook & Ladder', 'Chick-fil-A Spicy Deluxe', 'Firehouse Subs Meatball'],
'Review': [
'Love the crispy chicken, very tasty and fresh',
'Great sub with delicious roast beef, I love it!',
'Spicy and crispy, a little too spicy for me',
'The meatballs were not as tasty as I expected, a bit dry'
],
'Rating': [5, 4, 5, 2]
}
Convert data into a DataFrame
df = pd.DataFrame(data)
Function to compute similarity between reviews using cosine similarity
def get_similarity(review_texts):
vectorizer = CountVectorizer(stop_words='english')
X = vectorizer.fit_transform(review_texts)
similarity_matrix = cosine_similarity(X)
return similarity_matrix
Analyzing the similarity between reviews
similarity_matrix = get_similarity(df['Review'])
Plot the similarity matrix
plt.imshow(similarity_matrix, cmap='Blues', interpolation='nearest')
plt.colorbar()
plt.xticks(range(len(df['Item'])), df['Item'], rotation=90)
plt.yticks(range(len(df['Item'])), df['Item'])
plt.title('Review Similarity Matrix')
plt.show()
Display items with the highest ratings (suggesting popular menu items)
high_rated_items = df[df['Rating'] >= 4]
print("High-rated items for potential new menu development:")
print(high_rated_items[['Item', 'Rating']])
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