Build a Microservices app using Python, Rails and Angular

To build a Rails API and connect it to RabbitMQ, you'll need to follow these steps:

1. Set Up Your Rails API

1. Install Rails (if not already installed): Ensure you have Rails installed on your system. You can install it using:

   gem install rails

2. Create a new Rails API application:

   rails new my_api --api
   cd my_api

   The --api flag generates a Rails application that is optimized for API-only applications.

3. Generate a model (example):

   rails generate model Message content:string
   rails db:migrate

4. Create a controller:

   rails generate controller Messages

   Edit the app/controllers/messages_controller.rb to include actions such as create and index:

   class MessagesController < ApplicationController
     def index
       @messages = Message.all
       render json: @messages
     end
   
     def create
       @message = Message.new(message_params)
       if @message.save
         render json: @message, status: :created
       else
         render json: @message.errors, status: :unprocessable_entity
       end
     end
   
     private
   
     def message_params
       params.require(:message).permit(:content)
     end
   end

5. Set up routes in config/routes.rb:

   Rails.application.routes.draw do
     resources :messages, only: [:index, :create]
   end

2. Set Up RabbitMQ

1. Install RabbitMQ: Follow the official RabbitMQ installation guide for your operating system.

2. Start RabbitMQ Server: Once installed, start RabbitMQ using:

   rabbitmq-server

3. Install Bunny Gem: Add the bunny gem to your Gemfile to interact with RabbitMQ:

   # Gemfile
   gem 'bunny'

   Then run:

   bundle install

3. Connect Rails to RabbitMQ

1. Create an initializer for RabbitMQ:

   Create a file config/initializers/rabbitmq.rb to set up the RabbitMQ connection:

   require 'bunny'
   
   # Establish a connection to RabbitMQ
   CONNECTION = Bunny.new(ENV['RABBITMQ_URL'] || 'amqp://localhost')
   CONNECTION.start
   
   # Create a channel
   CHANNEL = CONNECTION.create_channel
   
   # Declare a queue
   QUEUE = CHANNEL.queue('messages', durable: true)

2. Publish Messages to RabbitMQ:

   Modify your MessagesController to publish messages to RabbitMQ when a message is created:

   class MessagesController < ApplicationController
     def create
       @message = Message.new(message_params)
       if @message.save
         publish_to_queue(@message)
         render json: @message, status: :created
       else
         render json: @message.errors, status: :unprocessable_entity
       end
     end
   
     private
   
     def message_params
       params.require(:message).permit(:content)
     end
   
     def publish_to_queue(message)
       # Publish message to RabbitMQ
       CHANNEL.default_exchange.publish(
         message.content,
         routing_key: QUEUE.name,
         persistent: true
       )
     end
   end

3. Consume Messages from RabbitMQ:

   Create a service or a background job to consume messages from RabbitMQ. For example, using Sidekiq:

   # Gemfile
   gem ' sidekiq'

   Create a Sidekiq worker (app/workers/message_worker.rb):

   class MessageWorker
     include Sidekiq::Worker
   
     def perform
       connection = Bunny.new(ENV['RABBITMQ_URL'] || 'amqp://localhost')
       connection.start
       channel = connection.create_channel
       queue = channel.queue('messages', durable: true)
   
       queue.subscribe(block: true) do |delivery_info, properties, body|
         # Process the message here
         puts "Received message: #{body}"
   
         # Acknowledge message processing
         channel.ack(delivery_info.delivery_tag)
       end
     end
   end

   Start the Sidekiq worker:

   bundle exec sidekiq

Note: Start the Redis server if it's not already running:

redis-server --daemonize yes

Summary

You've now set up a Rails API connected to RabbitMQ. The Rails API handles message creation and publishes these messages to RabbitMQ. A Sidekiq worker consumes these messages for further processing. Adjust the RabbitMQ connection details and message handling according to your specific needs.

Integrating with MongoDB

To integrate RabbitMQ with a Rails API using Mongoid, follow these steps:

1. Set Up Your Rails API with Mongoid

1. Install Rails (if not already installed): Ensure you have Rails installed on your system. You can install it using:

   gem install rails

2. Create a new Rails API application:

   rails new my_api --api
   cd my_api

   The --api flag generates a Rails application that is optimized for API-only applications.

3. Add Mongoid to Your Gemfile:

   # Gemfile
   gem 'mongoid', '~> 7.0'  # or the latest version
   gem 'bunny'
   gem 'sidekiq'

   Run bundle install to install the gems.

4. Generate Mongoid Configuration:

   Generate the Mongoid configuration file:

   rails g mongoid:config

   This creates a config/mongoid.yml file where you can configure MongoDB connection settings.

5. Create a Mongoid Model:

   Generate a model for your API:

   rails generate model Message content:string

   Update the generated model file to use Mongoid:

   # app/models/message.rb
   class Message
     include Mongoid::Document
     field :content, type: String
   end

6. Create a Controller:

   Generate a controller:

   rails generate controller Messages

   Update the controller to include actions such as create and index:

   # app/controllers/messages_controller.rb
   class MessagesController < ApplicationController
     def index
       @messages = Message.all
       render json: @messages
     end
   
     def create
       @message = Message.new(message_params)
       if @message.save
         publish_to_queue(@message)
         render json: @message, status: :created
       else
         render json: @message.errors, status: :unprocessable_entity
       end
     end
   
     private
   
     def message_params
       params.require(:message).permit(:content)
     end
   
     def publish_to_queue(message)
       # Publish message to RabbitMQ
       CHANNEL.default_exchange.publish(
         message.content,
         routing_key: QUEUE.name,
         persistent: true
       )
     end
   end

7. Set Up Routes in config/routes.rb:

   Rails.application.routes.draw do
     resources :messages, only: [:index, :create]
   end

2. Set Up RabbitMQ

1. Install RabbitMQ: Follow the official RabbitMQ installation guide for your operating system.

2. Start RabbitMQ Server: Once installed, start RabbitMQ using:

   rabbitmq-server

3. Connect Rails to RabbitMQ

1. Create an Initializer for RabbitMQ:

   Create a file config/initializers/rabbitmq.rb to set up the RabbitMQ connection:

   require 'bunny'
   
   # Establish a connection to RabbitMQ
   CONNECTION = Bunny.new(ENV['RABBITMQ_URL'] || 'amqp://localhost')
   CONNECTION.start
   
   # Create a channel
   CHANNEL = CONNECTION.create_channel
   
   # Declare a queue
   QUEUE = CHANNEL.queue('messages', durable: true)

2. Consume Messages from RabbitMQ:

   Create a service or a background job to consume messages from RabbitMQ. For example, using Sidekiq:

   # app/workers/message_worker.rb
   class MessageWorker
     include Sidekiq::Worker
   
     def perform
       connection = Bunny.new(ENV['RABBITMQ_URL'] || 'amqp://localhost')
       connection.start
       channel = connection.create_channel
       queue = channel.queue('messages', durable: true)
   
       queue.subscribe(block: true) do |delivery_info, properties, body|
         # Process the message here
         puts "Received message: #{body}"
   
         # Acknowledge message processing
         channel.ack(delivery_info.delivery_tag)
       end
     end
   end

   Start the Sidekiq worker:

   bundle exec sidekiq

Summary

You've now set up a Rails API connected to RabbitMQ using Mongoid for MongoDB interactions. The Rails API handles message creation and publishes these messages to RabbitMQ. A Sidekiq worker consumes these messages for further processing. Adjust RabbitMQ connection details and message handling according to your specific needs.

If you need more details on setting up MongoDB or RabbitMQ, or if you run into any issues, feel free to ask!

Sending Messages

Yes, you can definitely send emails whenever a message is created in your Rails API. You can achieve this by using Rails' built-in Action Mailer and integrating it into your message creation process. Here's a step-by-step guide on how to set this up:

1. Set Up Action Mailer

1. Configure Action Mailer:

   In your Rails application, you need to configure Action Mailer to work with your email provider. Open config/environments/development.rb (and/or production.rb for production environment settings) and set up your mailer configurations. For example, if you are using Gmail for development, you might add:

   # config/environments/development.rb
   config.action_mailer.delivery_method = :smtp
   config.action_mailer.smtp_settings = {
     address: 'smtp.gmail.com',
     port: 587,
     domain: 'example.com',
     user_name: 'your_email@gmail.com',
     password: 'your_email_password',
     authentication: 'plain',
     enable_starttls_auto: true
   }

   Replace the placeholders with your actual email provider settings. For production, use environment variables or secrets to manage sensitive information.

2. Generate a Mailer:

   Create a mailer using the Rails generator:

   rails generate mailer MessageMailer

   This will create a mailer class (app/mailers/message_mailer.rb) and view templates for the emails.

3. Define Mailer Method:

   Open app/mailers/message_mailer.rb and define a method to send the email:

   # app/mailers/message_mailer.rb
   class MessageMailer < ApplicationMailer
     default from: 'no-reply@example.com'
   
     def new_message_email(message)
       @message = message
       @recipient = 'recipient@example.com'  # Change this to the recipient's email address
   
       mail(to: @recipient, subject: 'New Message Created')
     end
   end

4. Create Email Views:

   Create views for the email content. For example, create an HTML view at app/views/message_mailer/new_message_email.html.erb:

   <!-- app/views/message_mailer/new_message_email.html.erb -->
   <h1>New Message Created</h1>
   <p>A new message has been created with the following content:</p>
   <p><%= @message.content %></p>

   You can also create a text version at app/views/message_mailer/new_message_email.text.erb if you want plain text emails:

   <!-- app/views/message_mailer/new_message_email.text.erb -->
   New Message Created
   
   A new message has been created with the following content:
   
   <%= @message.content %>

2. Trigger the Email on Message Creation

1. Modify the Messages Controller:

   Update the create action in your MessagesController to send an email after a message is successfully created:

   # app/controllers/messages_controller.rb
   class MessagesController < ApplicationController
     def create
       @message = Message.new(message_params)
       if @message.save
         publish_to_queue(@message)
         MessageMailer.new_message_email(@message).deliver_now
         render json: @message, status: :created
       else
         render json: @message.errors, status: :unprocessable_entity
       end
     end
   
     private
   
     def message_params
       params.require(:message).permit(:content)
     end
   
     def publish_to_queue(message)
       # Publish message to RabbitMQ
       CHANNEL.default_exchange.publish(
         message.content,
         routing_key: QUEUE.name,
         persistent: true
       )
     end
   end

   In the create action, after saving the message to MongoDB, it triggers the MessageMailer to send an email.

Summary

With these steps, you've integrated email notifications into your Rails API. Whenever a new message is created, an email will be sent to the recipient specified in the MessageMailer class. You can customize the email content and recipient based on your needs.

If you encounter any issues or have additional questions, feel free to ask!

Background Workers

In a Rails application, background workers are used to handle tasks that can be processed asynchronously, away from the main request-response cycle. This approach can improve performance and user experience by offloading time-consuming or resource-intensive operations. Here's a look at some common use cases for background workers:

1. Sending Emails

Background workers are ideal for sending emails, especially if you expect to send a high volume of emails or if generating the email content is resource-intensive.

Example: Sending welcome emails to users upon registration.

# app/workers/send_welcome_email_worker.rb
class SendWelcomeEmailWorker
  include Sidekiq::Worker

  def perform(user_id)
    user = User.find(user_id)
    UserMailer.welcome_email(user).deliver_now
  end
end

2. Processing Images or Files

Tasks like resizing images, converting file formats, or other media processing can be time-consuming and should be handled asynchronously.

Example: Resizing uploaded images.

# app/workers/resize_image_worker.rb
class ResizeImageWorker
  include Sidekiq::Worker

  def perform(image_id)
    image = Image.find(image_id)
    # Resize the image here
    image.resize!
    image.save
  end
end

3. Generating Reports or Analytics

Generating complex reports or performing data analysis can take a long time. Background workers can handle these tasks and provide the results once they are ready.

Example: Generating a PDF report of user activity.

# app/workers/generate_report_worker.rb
class GenerateReportWorker
  include Sidekiq::Worker

  def perform(report_id)
    report = Report.find(report_id)
    # Generate the report here
    report.generate!
    report.save
  end
end

4. Interacting with External APIs

When interacting with external APIs that have rate limits or might be slow, background workers can handle these interactions without blocking the main application.

Example: Fetching data from a third-party service and storing it.

# app/workers/fetch_data_from_api_worker.rb
class FetchDataFromApiWorker
  include Sidekiq::Worker

  def perform(api_endpoint)
    response = Net::HTTP.get(URI(api_endpoint))
    # Process and store the response
  end
end

5. Handling Long-running Tasks

Any long-running tasks that could slow down the user experience if handled synchronously should be processed in the background.

Example: Running a complex algorithm or calculation.

# app/workers/run_complex_calculation_worker.rb
class RunComplexCalculationWorker
  include Sidekiq::Worker

  def perform(data_id)
    data = Data.find(data_id)
    # Perform the complex calculation here
    result = perform_calculation(data)
    data.update(result: result)
  end

  def perform_calculation(data)
    # Implement your complex calculation here
  end
end

6. Scheduled Jobs

Background workers can also be used to run tasks on a schedule, like periodic cleanups or updates.

Example: Daily database cleanup.

# app/workers/daily_cleanup_worker.rb
class DailyCleanupWorker
  include Sidekiq::Worker

  def perform
    # Perform cleanup tasks
    CleanupService.run
  end
end

7. Queueing and Retries

You can use background workers to manage jobs that might fail and need to be retried. Sidekiq, for example, provides built-in support for retries and error handling.

Example: Retries for failed payments.

# app/workers/process_payment_worker.rb
class ProcessPaymentWorker
  include Sidekiq::Worker
  sidekiq_options retry: 5  # Retry up to 5 times

  def perform(payment_id)
    payment = Payment.find(payment_id)
    # Process the payment
    payment.process!
  end
end

Integration with Rails

To integrate a background worker into a Rails application, you'll typically use a background processing library such as Sidekiq, Resque, or Delayed Job. Here’s how you can integrate Sidekiq into your Rails application:

1. Add Sidekiq to Your Gemfile:

   gem 'sidekiq'

   Run bundle install to install the gem.

2. Configure Sidekiq:

   Create a config/sidekiq.yml file to configure Sidekiq:

   :concurrency: 5
   :queues:
     - default
     - mailers

3. Set Up Redis:

   Sidekiq requires Redis. Install Redis on your system and start the Redis server.

4. Start Sidekiq:

   Run Sidekiq in your terminal:

   bundle exec sidekiq

5. Add Background Jobs to Your Application:

   Define workers and trigger them from your controllers or models as needed.

By offloading these tasks to background workers, you can ensure that your web application remains responsive and performant, even when handling time-consuming operations.

Modifying Earlier Code

• Yes, you can certainly send the email in the background worker after the message is published to RabbitMQ. This approach is useful for decoupling the email sending process from the HTTP request-response cycle, thus making the API more responsive. Here’s how you can adjust your code to achieve this:

1. Modify the Background Worker to Send Emails

First, create or update your background worker to handle sending emails. This worker will subscribe to RabbitMQ messages and then send the email based on the received message content.

1. Define the Worker:

   Create a worker that listens to the RabbitMQ queue and sends an email when it receives a message. Here’s how you can define the worker using Sidekiq:

   # app/workers/send_email_worker.rb
   class SendEmailWorker
     include Sidekiq::Worker
   
     def perform(message_content)
       # Send an email with the message content
       MessageMailer.new_message_email(message_content).deliver_now
     end
   end

   Make sure MessageMailer has been configured to handle the message content properly.

2. Update the Mailer:

   Modify the mailer to handle raw message content:

   # app/mailers/message_mailer.rb
   class MessageMailer < ApplicationMailer
     default from: 'no-reply@example.com'
   
     def new_message_email(content)
       @content = content
       @recipient = 'recipient@example.com'  # Change this to the recipient's email address
   
       mail(to: @recipient, subject: 'New Message Created')
     end
   end

   Update the email view templates accordingly:

   <!-- app/views/message_mailer/new_message_email.html.erb -->
   <h1>New Message Created</h1>
   <p>A new message has been created with the following content:</p>
   <p><%= @content %></p>

2. Update the Controller to Publish Messages

Update your MessagesController to only publish the message to RabbitMQ, and let the background worker handle the email sending:

# app/controllers/messages_controller.rb
class MessagesController < ApplicationController
  def create
    @message = Message.new(message_params)
    if @message.save
      publish_to_queue(@message)
      render json: @message, status: :created
    else
      render json: @message.errors, status: :unprocessable_entity
    end
  end

  private

  def message_params
    params.require(:message).permit(:content)
  end

  def publish_to_queue(message)
    # Publish message to RabbitMQ
    CHANNEL.default_exchange.publish(
      message.content,
      routing_key: QUEUE.name,
      persistent: true
    )
  end
end

3. Set Up a Background Worker to Process RabbitMQ Messages

Create a separate worker that will process messages from RabbitMQ and enqueue the email sending job:

# app/workers/rabbitmq_consumer_worker.rb
class RabbitmqConsumerWorker
  include Sidekiq::Worker

  def perform
    connection = Bunny.new(ENV['RABBITMQ_URL'] || 'amqp://localhost')
    connection.start
    channel = connection.create_channel
    queue = channel.queue('messages', durable: true)

    queue.subscribe(block: true) do |delivery_info, properties, body|
      # Enqueue the email sending job
      SendEmailWorker.perform_async(body)

      # Acknowledge message processing
      channel.ack(delivery_info.delivery_tag)
    end
  end
end

4. Start the Sidekiq Worker

Run Sidekiq to process the background jobs:

bundle exec sidekiq

Summary

Here’s how the flow works with the updated setup:

1. User creates a message via the API.
2. The Rails controller saves the message to MongoDB and publishes the message content to RabbitMQ.
3. RabbitMQ Consumer Worker listens to the RabbitMQ queue, retrieves messages, and then enqueues a job in Sidekiq to send the email.
4. Sidekiq Worker (SendEmailWorker) picks up the job from the queue and sends the email.

This approach ensures that the API remains responsive, while the email sending is handled asynchronously in the background.

Potential Features/Applications of a Python API

In a microservices architecture where you have a Rails application handling job posts and a separate Python API, there are several services that the Python API could offer to complement or enhance the functionality of the Rails application. Here are some possible services the Python API could provide:

1. Machine Learning and Data Analysis

Python is a popular language for machine learning and data analysis. Your Python API could offer:

• Recommendation Systems: Use machine learning models to recommend job posts to users based on their profiles or past interactions.
• Resume Parsing: Analyze and extract key information from resumes or CVs uploaded by users.
• Predictive Analytics: Provide insights such as the likelihood of a job post receiving applications or predicting job market trends.

2. Natural Language Processing (NLP)

Python has robust libraries for NLP tasks. The Python API could offer:

• Job Description Analysis: Analyze job descriptions to extract key skills, qualifications, and requirements.
• Sentiment Analysis: Analyze user feedback or comments related to job posts to gauge sentiment.
• Text Classification: Automatically categorize job posts into different types or industries based on their content.

3. Search and Indexing

If the Python API is responsible for advanced search capabilities:

• Full-Text Search: Implement a sophisticated search engine that supports full-text search across job posts.
• ElasticSearch Integration: Integrate with Elasticsearch for fast and flexible search functionality.
• Semantic Search: Use embeddings or other techniques to improve search relevance and results.

4. Data Enrichment and Aggregation

Enhance job posts with additional data:

• Company Data Enrichment: Fetch and integrate additional data about companies from external sources or APIs.
• Salary Insights: Provide salary data or insights based on job titles, locations, and industries.
• Market Trends: Aggregate and provide insights on job market trends, skills in demand, etc.

5. External API Integrations

The Python API could handle integrations with other services:

• Social Media Integration: Fetch or analyze job-related data from social media platforms.
• Job Board Aggregation: Aggregate job posts from various external job boards and provide a unified API for job searches.
• User Verification: Integrate with external systems for user verification or background checks.

6. Notification and Alert Systems

Python could handle notification services:

• Email Notifications: Manage email notifications for job applications, new job postings, etc.
• SMS Notifications: Provide SMS alerts for critical updates or job opportunities.
• Push Notifications: Manage push notifications if the system has a mobile app component.

7. User Behavior Analysis

Analyze user interactions to provide better services:

• User Analytics: Track and analyze user interactions with job posts to improve user experience and engagement.
• A/B Testing: Implement A/B testing for different job post formats or features to optimize engagement and conversion.

8. API Gateway or Orchestration

If the Python service is handling multiple microservices:

• API Gateway: Act as a gateway to route requests to appropriate services.
• Orchestration: Manage and coordinate interactions between various microservices.

9. Custom Business Logic

Implement specific business logic that is better suited for Python:

• Custom Calculations: Perform complex calculations or simulations related to job matching or user recommendations.
• Advanced Filtering: Offer advanced filtering capabilities for job searches based on complex criteria.

10. Security and Compliance

Handle security and compliance aspects:

• Data Encryption: Implement encryption or security measures for sensitive data.
• Compliance Checks: Ensure that job posts or user data comply with legal and regulatory requirements.

Integration Points

For seamless integration between the Rails application and the Python API:

• RESTful API Endpoints: Define clear RESTful API endpoints in the Python service that the Rails application can call.
• Message Queues: Use message queues (e.g., RabbitMQ, Kafka) for asynchronous communication between the services.
• Shared Data Stores: Utilize shared data stores or databases if both services need access to common data.
• API Gateway: Consider using an API Gateway to manage and route requests between different microservices.

By defining these services, you can leverage Python’s strengths in areas like machine learning, NLP, and data analysis, while your Rails application focuses on job post management and user interactions.

On Duplication

While it’s technically possible for a Python API to duplicate the functionality of a Rails API, doing so is generally not ideal for several reasons. Here’s why it might be problematic and some alternative approaches you could consider:

Why Duplication May Not Be Ideal

1. Redundancy and Maintenance Overhead:

   • Code Duplication: Duplicating functionality across services leads to code redundancy. Maintaining and updating two sets of code can be cumbersome and error-prone.
   • Consistency Issues: Ensuring consistency between the two APIs can be challenging. Changes in business logic or data models need to be replicated across both services.

2. Increased Complexity:

   • Deployment and Monitoring: Managing two services that essentially perform the same functions can complicate deployment and monitoring efforts.
   • Debugging and Troubleshooting: Identifying issues becomes more complex when you have two services performing similar tasks but potentially with slight differences in implementation.

3. Resource Inefficiency:

   • Duplicated Efforts: Running two APIs with similar functionality means doubling the computational resources required, which may lead to inefficient use of infrastructure.
   • Higher Costs: Increased infrastructure and operational costs due to running redundant services.

Alternative Approaches

Instead of duplicating functionality, consider the following approaches:

1. Microservices Specialization:

   • Focused Responsibilities: Assign specific responsibilities to each service. For instance, the Rails API could handle job posting and user management, while the Python API could focus on advanced analytics or machine learning tasks.
   • Complementary Services: Use the Python API for tasks that leverage Python’s strengths, such as natural language processing, machine learning, or complex data analysis.

2. API Gateway or Orchestrator:

   • Unified Interface: Use an API Gateway to provide a single entry point for clients while internally routing requests to the appropriate service (Rails or Python).
   • Service Coordination: Implement an orchestrator to manage interactions between services and ensure a cohesive experience.

3. Shared Services and Libraries:

   • Common Libraries: If both APIs need to perform similar tasks, consider creating shared libraries or services that encapsulate common functionality. These libraries can be used by both the Rails and Python services to maintain consistency and reduce duplication.
   • Data Synchronization: Implement data synchronization mechanisms to keep data consistent between services, if they need to access or modify shared data.

4. Integration Points:

   • Inter-service Communication: Define clear API endpoints and use messaging or RPC (Remote Procedure Call) mechanisms to allow services to communicate and collaborate. For example, the Python service could process data and send results back to the Rails service.
   • Asynchronous Processing: Use background jobs or message queues for tasks that require coordination between services, such as processing data or handling notifications.

5. Service Delegation:

   • Delegated Responsibilities: Instead of duplicating functionality, delegate specific responsibilities to the Python service. For instance, handle job postings and user management in the Rails service, and delegate job search optimization or resume parsing to the Python service.

Example Scenarios

Here are a few scenarios where having both Rails and Python APIs can be beneficial:

• Rails API handles user management, job postings, and job application workflows.
• Python API provides advanced search capabilities, performs machine learning-based recommendations, or analyzes job market trends.

In summary, while it’s technically possible to have the Python API duplicate the Rails API’s functionality, it’s generally more efficient and maintainable to use the Python API for specialized tasks that complement the Rails API. This approach leverages the strengths of each technology and avoids unnecessary duplication of effort.

Setting Up Job Application Follow Up Notification

Setting up a notification system in your Python API to follow up with users after they've applied to a job post is a great idea. This can be accomplished by using the Python API to handle scheduling and sending notifications asynchronously, which offloads this responsibility from your Rails application and utilizes Python's strengths.

Here’s a step-by-step guide on how to implement this:

1. Define the Notification Workflow

Determine the logic for sending follow-up notifications, including:

• Timing: How long after a user applies should the follow-up notification be sent? (e.g., 24 hours, 1 week)
• Notification Content: What will the notification include? (e.g., reminder to check application status, offer additional job recommendations)
• Delivery Method: How will the notification be sent? (e.g., email, SMS, push notifications)

2. Set Up the Notification Service in Python

1. Create a Notification Worker:

   Define a background worker in Python that handles the scheduling and sending of notifications. For instance, using Celery (a distributed task queue for Python) with Redis as the broker:

   # tasks.py
   from celery import Celery
   from datetime import datetime, timedelta
   import smtplib  # or use a more robust email library
   from email.mime.text import MIMEText
   
   app = Celery('tasks', broker='redis://localhost:6379/0')
   
   @app.task
   def send_follow_up_notification(user_id, job_post_id):
       user = get_user(user_id)  # Function to retrieve user details
       job_post = get_job_post(job_post_id)  # Function to retrieve job post details
   
       # Compose the email content
       subject = 'Follow-Up on Your Job Application'
       body = f'Hi {user.name},\n\nYou applied to {job_post.title} on {datetime.now().strftime("%Y-%m-%d")}. We wanted to check in and see if you have any questions or need further assistance.\n\nBest regards,\nThe Job Portal Team'
   
       # Send the email
       msg = MIMEText(body)
       msg['Subject'] = subject
       msg['From'] = 'no-reply@example.com'
       msg['To'] = user.email
   
       with smtplib.SMTP('smtp.example.com') as server:
           server.sendmail(msg['From'], [msg['To']], msg.as_string())

   In this example, Celery handles the scheduling of the task, and smtplib is used to send the email. You might use a more advanced email library or service in production.

2. Schedule Notifications:

   Schedule follow-up notifications to be sent after a specific time period using Celery’s apply_async method with eta (estimated time of arrival) or countdown:

   # Example function to schedule a notification
   from datetime import datetime, timedelta
   from tasks import send_follow_up_notification
   
   def schedule_follow_up(user_id, job_post_id):
       # Schedule the task to run 24 hours after application
       eta = datetime.now() + timedelta(hours=24)
       send_follow_up_notification.apply_async((user_id, job_post_id), eta=eta)

3. Integrate with the Rails Application

1. Trigger Scheduling from Rails:

   When a user applies for a job, you can call the Python API to schedule the follow-up notification. This can be done via an HTTP request or a message queue:

   # app/controllers/applications_controller.rb
   require 'net/http'
   
   class ApplicationsController < ApplicationController
     def create
       @application = Application.new(application_params)
       if @application.save
         # Assuming Python API is running at http://python-api:8000
         uri = URI('http://python-api:8000/schedule_follow_up')
         Net::HTTP.post_form(uri, user_id: @application.user_id, job_post_id: @application.job_post_id)
         render json: @application, status: :created
       else
         render json: @application.errors, status: :unprocessable_entity
       end
     end
   
     private
   
     def application_params
       params.require(:application).permit(:user_id, :job_post_id, :resume)
     end
   end

2. Set Up the Python API Endpoint:

   Create an endpoint in the Python API to handle the scheduling request:

   # app.py
   from flask import Flask, request, jsonify
   from tasks import schedule_follow_up
   
   app = Flask(__name__)
   
   @app.route('/schedule_follow_up', methods=['POST'])
   def schedule_follow_up_endpoint():
       user_id = request.form['user_id']
       job_post_id = request.form['job_post_id']
       schedule_follow_up(user_id, job_post_id)
       return jsonify({'status': 'success'}), 200
   
   if __name__ == '__main__':
       app.run(host='0.0.0.0', port=8000)

4. Testing and Deployment

• Test the End-to-End Flow: Ensure that when a job application is submitted, the follow-up notification is scheduled and sent correctly.
• Monitor and Log: Implement monitoring and logging for your notification service to track successes and failures.
• Handle Failures: Implement retry mechanisms and error handling to manage potential issues in sending notifications.

Summary

By using a Python API to handle notification scheduling and sending, you leverage Python’s capabilities for background processing and asynchronous tasks. This approach ensures that your Rails application remains responsive while handling follow-up notifications efficiently.

Using RabbitMQ for Follow up Notifications

Yes, you can definitely use a message broker like RabbitMQ (or any other message queue) to publish a message when a job application is submitted, which can then be consumed by a background worker in the Python API. This approach can be more scalable and decoupled compared to direct HTTP requests.

Here’s how you can implement this:

1. Publish a Message to RabbitMQ from Rails

When a job application is submitted in your Rails application, you can publish a message to RabbitMQ. This message can include information needed for scheduling the follow-up notification.

1. Add RabbitMQ Gem

   Ensure you have the bunny gem for interacting with RabbitMQ in your Rails app:

   gem 'bunny'

   Run bundle install to install the gem.

2. Set Up RabbitMQ Connection

   Configure RabbitMQ in an initializer (e.g., config/initializers/rabbitmq.rb):

   require 'bunny'
   
   CONNECTION = Bunny.new(ENV['RABBITMQ_URL'] || 'amqp://localhost')
   CONNECTION.start
   CHANNEL = CONNECTION.create_channel

3. Publish a Message

   In your Rails controller, publish a message to the RabbitMQ queue when a job application is created:

   # app/controllers/applications_controller.rb
   class ApplicationsController < ApplicationController
     def create
       @application = Application.new(application_params)
       if @application.save
         publish_to_queue(@application)
         render json: @application, status: :created
       else
         render json: @application.errors, status: :unprocessable_entity
       end
     end
   
     private
   
     def application_params
       params.require(:application).permit(:user_id, :job_post_id, :resume)
     end
   
     def publish_to_queue(application)
       message = {
         user_id: application.user_id,
         job_post_id: application.job_post_id,
         apply_time: application.created_at.to_s
       }.to_json
   
       CHANNEL.default_exchange.publish(
         message,
         routing_key: 'application_queue',
         persistent: true
       )
     end
   end

2. Consume the Message in the Python API

Set up a Python worker to consume messages from RabbitMQ and handle scheduling follow-up notifications.

1. Install Dependencies

   Install pika for RabbitMQ and Celery for task management:

   pip install pika celery

2. Define the Celery Worker

   Create a Celery worker to handle follow-up notifications. This worker will be responsible for processing messages from RabbitMQ:

   # tasks.py
   from celery import Celery
   import smtplib
   from email.mime.text import MIMEText
   import pika
   import json
   
   app = Celery('tasks', broker='redis://localhost:6379/0')
   
   def send_email(user_id, job_post_id):
       # Implement email sending logic here
       subject = 'Follow-Up on Your Job Application'
       body = f'Hi User {user_id},\n\nYou applied to Job Post {job_post_id}.\n\nBest regards,\nThe Job Portal Team'
       msg = MIMEText(body)
       msg['Subject'] = subject
       msg['From'] = 'no-reply@example.com'
       msg['To'] = 'user@example.com'  # Replace with actual user email
   
       with smtplib.SMTP('smtp.example.com') as server:
           server.sendmail(msg['From'], [msg['To']], msg.as_string())
   
   @app.task
   def process_application(message_body):
       data = json.loads(message_body)
       user_id = data['user_id']
       job_post_id = data['job_post_id']
       # Schedule the notification here, e.g., with Celery's countdown
       send_email.apply_async((user_id, job_post_id), countdown=86400)  # 24 hours delay

3. Set Up RabbitMQ Consumer

   Create a script to consume messages from RabbitMQ and dispatch them to Celery:

   # consumer.py
   import pika
   import json
   from tasks import process_application
   
   connection = pika.BlockingConnection(pika.ConnectionParameters('localhost'))
   channel = connection.channel()
   
   channel.queue_declare(queue='application_queue', durable=True)
   
   def callback(ch, method, properties, body):
       process_application.delay(body)
   
   channel.basic_consume(queue='application_queue', on_message_callback=callback, auto_ack=True)
   
   print('Waiting for messages. To exit press CTRL+C')
   channel.start_consuming()

4. Run the Consumer and Celery Worker

   Start the RabbitMQ consumer:

   python consumer.py

   Start the Celery worker:

   celery -A tasks worker --loglevel=info

Summary

By using RabbitMQ to publish messages from your Rails application and consume them with a Python API, you achieve a more scalable and decoupled system. Here’s the workflow:

1. Rails API publishes a message to RabbitMQ when a job application is created.
2. Python API (using Celery) consumes the message from RabbitMQ and schedules a follow-up notification.

This setup allows you to leverage the strengths of both Ruby on Rails and Python, and it ensures that the notification scheduling is handled efficiently and asynchronously.

Integrating a Journaling System

Integrating a journaling system into your Python application can be a great way to enhance the job search experience for users by allowing them to store and manage notes related to their job search. Here’s how you can design and implement a journaling system in the Python app:

1. Design the Journaling System

Features to Consider:

• Create Notes: Users can create, update, and delete notes.
• View Notes: Users can view their notes, either as a list or individual entries.
• Search and Filter: Users can search or filter notes based on keywords, dates, or tags.
• Organize Notes: Allow users to organize notes by job posts or categories.
• Authentication: Ensure that users are authenticated to access their notes securely.

2. Set Up the Python API for Journaling

1. Define the Data Model

Define the data model for notes. You might use a database like PostgreSQL, MongoDB, or any other preferred database.

Example with MongoDB:

# models.py
from pymongo import MongoClient
from datetime import datetime

client = MongoClient('mongodb://localhost:27017/')
db = client['job_search']
notes_collection = db['notes']

def create_note(user_id, job_post_id, content):
    note = {
        'user_id': user_id,
        'job_post_id': job_post_id,
        'content': content,
        'created_at': datetime.utcnow(),
        'updated_at': datetime.utcnow()
    }
    notes_collection.insert_one(note)

def get_notes(user_id, job_post_id=None):
    query = {'user_id': user_id}
    if job_post_id:
        query['job_post_id'] = job_post_id
    return list(notes_collection.find(query))

def update_note(note_id, content):
    notes_collection.update_one(
        {'_id': note_id},
        {'$set': {'content': content, 'updated_at': datetime.utcnow()}}
    )

def delete_note(note_id):
    notes_collection.delete_one({'_id': note_id})

2. Create API Endpoints

Use Flask to create RESTful API endpoints for the journaling system.

# app.py
from flask import Flask, request, jsonify
from models import create_note, get_notes, update_note, delete_note

app = Flask(__name__)

@app.route('/notes', methods=['POST'])
def create_note_endpoint():
    data = request.json
    create_note(
        user_id=data['user_id'],
        job_post_id=data.get('job_post_id'),
        content=data['content']
    )
    return jsonify({'status': 'Note created'}), 201

@app.route('/notes', methods=['GET'])
def get_notes_endpoint():
    user_id = request.args.get('user_id')
    job_post_id = request.args.get('job_post_id')
    notes = get_notes(user_id, job_post_id)
    return jsonify(notes), 200

@app.route('/notes/<note_id>', methods=['PUT'])
def update_note_endpoint(note_id):
    data = request.json
    update_note(note_id, data['content'])
    return jsonify({'status': 'Note updated'}), 200

@app.route('/notes/<note_id>', methods=['DELETE'])
def delete_note_endpoint(note_id):
    delete_note(note_id)
    return jsonify({'status': 'Note deleted'}), 200

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8000)

3. Authentication and Authorization

Implement authentication to ensure that users can only access their own notes. This can be done using:

• JWT Tokens: Use JSON Web Tokens (JWT) for stateless authentication.
• OAuth: Integrate with OAuth providers if you are using third-party authentication.
• Session Management: Use sessions if you have a traditional authentication system.

Example with JWT:

# auth.py (simplified example)
import jwt
from flask import request, jsonify

SECRET_KEY = 'your_secret_key'

def authenticate():
    auth_header = request.headers.get('Authorization')
    if not auth_header:
        return jsonify({'message': 'Authorization header missing'}), 401
    token = auth_header.split(" ")[1]
    try:
        decoded = jwt.decode(token, SECRET_KEY, algorithms=['HS256'])
        return decoded['user_id']
    except jwt.ExpiredSignatureError:
        return jsonify({'message': 'Token expired'}), 401
    except jwt.InvalidTokenError:
        return jsonify({'message': 'Invalid token'}), 401

Integrate authentication into your endpoints:

from auth import authenticate

@app.route('/notes', methods=['POST'])
def create_note_endpoint():
    user_id = authenticate()
    if isinstance(user_id, tuple):
        return user_id
    data = request.json
    create_note(
        user_id=user_id,
        job_post_id=data.get('job_post_id'),
        content=data['content']
    )
    return jsonify({'status': 'Note created'}), 201

3. Integrate with the Rails Application

To allow users to interact with their notes from the Rails app:

1. Add Frontend Integration:

   • Implement a UI in your Rails application for users to create, view, update, and delete notes.
   • Use HTTP client libraries (like Net::HTTP or HTTParty) to communicate with the Python API.

2. Example of Calling Python API from Rails:

   # app/controllers/notes_controller.rb
   require 'net/http'
   require 'json'
   
   class NotesController < ApplicationController
     def create
       response = Net::HTTP.post(
         URI('http://python-api:8000/notes'),
         { user_id: current_user.id, content: params[:content] }.to_json,
         { 'Content-Type' => 'application/json' }
       )
       if response.code.to_i == 201
         render json: { status: 'Note created' }, status: :created
       else
         render json: { error: 'Failed to create note' }, status: :unprocessable_entity
       end
     end
   
     # Define other actions (show, update, destroy) similarly
   end

Summary

Incorporating a journaling system into your Python API can greatly enhance the user experience by providing a dedicated space for users to manage their job search notes. By using RabbitMQ to decouple tasks and Flask for the API, you can ensure that the journaling system is both robust and scalable. Integrating this with your Rails application will offer a seamless experience for users to interact with their notes.

Logging

Adding logging to a Rails project is crucial for monitoring, debugging, and understanding the behavior of your application. Rails provides a built-in logging framework that is highly configurable. Here’s a guide on how to set up and use logging effectively in a Rails project:

1. Basic Logging Configuration

Rails uses the Logger class from Ruby’s standard library for logging. By default, Rails logs messages to a file located at log/development.log for development and log/production.log for production.

Configuration File: config/environments/development.rb

config.log_level = :debug

Configuration File: config/environments/production.rb

config.log_level = :info

• :debug: Most verbose, includes all log messages.
• :info: Standard level, includes informational messages.
• :warn: Includes warnings and errors.
• :error: Includes only errors.
• :fatal: Includes only fatal errors.
• :unknown: Includes unknown log levels.

2. Customizing Log Format

You can customize the log format to include additional details like timestamps, severity levels, and custom messages.

Custom Logger Formatter

Create a custom logger formatter by defining a class or using a Proc.

Example: Custom Formatter in config/environments/development.rb

config.log_formatter = proc do |severity, time, progname, msg|
  formatted_msg = "#{severity} [#{time}] #{progname}: #{msg}\n"
  formatted_msg
end

3. Adding Logs in Your Application Code

You can use different logging methods provided by Rails to log messages at various levels. Here’s how to log messages from your application code:

Logging at Different Levels

class MyController < ApplicationController
  def show
    logger.debug "Debug message: entering show action"
    logger.info "Info message: showing item details"
    logger.warn "Warning message: potential issue detected"
    logger.error "Error message: something went wrong"
    logger.fatal "Fatal message: critical error occurred"
  end
end

Using Rails.logger Directly

Rails.logger.info "This is an info-level log message."
Rails.logger.debug "This is a debug-level log message."

4. Logging to External Services

For production environments, you may want to send logs to external services for better monitoring and analysis. Popular logging services include:

• Loggly
• Splunk
• Papertrail
• Sentry

Example: Logging with Lograge

Lograge is a popular gem that provides more structured and concise logging for Rails applications.

1. Add Lograge to Your Gemfile:

   gem 'lograge'

2. Run bundle install to install the gem.

3. Configure Lograge in config/environments/production.rb:

   config.lograge.enabled = true

   You can customize Lograge further to include additional details or format the logs.

Example: Integrating with Papertrail

1. Add Papertrail to Your Gemfile:

   gem 'remote_syslog_logger'

2. Run bundle install to install the gem.

3. Configure Papertrail in config/environments/production.rb:

   config.logger = RemoteSyslogLogger.new('logs.papertrailapp.com', 12345)

   Replace 'logs.papertrailapp.com' and 12345 with your Papertrail server address and port.

5. Advanced Logging Configuration

If you need more advanced logging configurations, you can set up a custom logger by creating an initializer.

Example: Custom Logger Initializer

Create a file config/initializers/logger.rb:

my_log = Logger.new(STDOUT)
my_log.level = Logger::DEBUG

Rails.application.config.logger = ActiveSupport::TaggedLogging.new(my_log)

6. Testing and Verification

After configuring logging, make sure to test and verify that logs are being generated and stored as expected. Review your log files and external logging service dashboards to ensure that the logging setup is working correctly.

Summary

1. Basic Logging: Use Rails’ default logging configuration for development and production.
2. Custom Formats: Customize log formatting to suit your needs.
3. Application Logs: Use logger methods to add log messages at various levels in your application code.
4. External Services: Integrate with external logging services for enhanced log management and monitoring.
5. Advanced Configuration: Set up custom loggers or use gems like Lograge for advanced logging features.

By following these steps, you can effectively manage and utilize logging in your Rails application, aiding in debugging, monitoring, and maintaining your application.

Custom Logging Example

It looks like you are encountering an issue where logger is not recognized in your class or module. This is a common problem when you try to use logging in a context where logger is not directly available. Here’s how you can address this issue in a Ruby on Rails application, particularly in a background job or service object like a Scraper.

1. Using Rails’ Logger in a Non-Rails Context

If you are in a class that does not directly inherit from ApplicationController or ActiveRecord::Base, logger may not be automatically available. In such cases, you can explicitly set up a logger for your class.

1.1. Directly Using Rails’ Logger

You can access the Rails logger directly via Rails.logger. Here’s how to use it:

class Scraper
  def initialize
    @urls = ["https://weworkremotely.com/categories/remote-full-stack-programming-jobs#job-listings"]
  end

  def perform
    job = { title: '', company_title: '', location: '', date: '', link: '' }
    if job[:title].empty? || job[:company_title].empty? || job[:location].empty? || job[:date].empty? || job[:link].empty?
      Rails.logger.warn "One or more required fields are missing or empty #{job}"
    end
  end
end

In this example, Rails.logger.warn is used to log the warning message.

1.2. Setting Up a Custom Logger

If you need a more flexible or separate logger instance, you can create a custom logger within your class. Here’s how to set it up:

class Scraper
  def initialize
    @urls = ["https://weworkremotely.com/categories/remote-full-stack-programming-jobs#job-listings"]
    @logger = Logger.new(STDOUT) # Or use a file: Logger.new('log/scraper.log')
    @logger.level = Logger::DEBUG
  end

  def perform
    job = { title: '', company_title: '', location: '', date: '', link: '' }
    if job[:title].empty? || job[:company_title].empty? || job[:location].empty? || job[:date].empty? || job[:link].empty?
      @logger.warn "One or more required fields are missing or empty #{job}"
    end
  end
end

2. Using Logging in Background Jobs

If you are using a background job processor like Sidekiq, you should use Rails.logger directly or set up a logger as shown above. Sidekiq also supports logging directly through its own configuration.

Example with Sidekiq:

If Scraper is a Sidekiq worker:

class ScraperWorker
  include Sidekiq::Worker

  def perform
    job = { title: '', company_title: '', location: '', date: '', link: '' }
    if job[:title].empty? || job[:company_title].empty? || job[:location].empty? || job[:date].empty? || job[:link].empty?
      Rails.logger.warn "One or more required fields are missing or empty #{job}"
    end
  end
end

3. Testing Your Logger

After setting up your logger, make sure to test it by triggering the relevant functionality and checking the output in your log files or console.

Summary

1. Direct Rails Logger: Use Rails.logger for logging if you are working in a Rails context but outside controllers/models.
2. Custom Logger: Set up a custom logger instance if needed.
3. Background Jobs: Ensure your background job workers also use Rails.logger or a custom logger for logging.

By configuring and using the logger appropriately, you can ensure that important messages and warnings are captured and recorded as expected.

Logging With Sentry

Integrating Sentry with a Rails application provides powerful error tracking and monitoring capabilities. Sentry helps you capture and manage exceptions, performance issues, and other application errors. Here's a step-by-step guide on how to set up Sentry in a Rails application:

1. Create a Sentry Account and Project

1. Sign Up: Go to Sentry's website and sign up for an account if you don't already have one.

2. Create a Project: Once logged in, create a new project for your Rails application. Sentry will provide you with a DSN (Data Source Name) that you'll need for configuration.

2. Add the Sentry Gem to Your Gemfile

In your Rails application, add the sentry-ruby and sentry-rails gems to your Gemfile.

gem 'sentry-ruby'
gem 'sentry-rails'

Run bundle install to install the gems.

3. Configure Sentry in Your Rails Application

You need to configure Sentry with the DSN provided by Sentry. You typically do this in an initializer file.

Create a Configuration File

Create a file named config/initializers/sentry.rb and add the following configuration:

Sentry.init do |config|
  config.dsn = 'your_sentry_dsn_here'
  config.breadcrumbs_logger = [:active_support_logger, :http_logger]

  # To enable performance monitoring
  config.traces_sample_rate = 1.0 # Adjust the rate as needed (0.0 to 1.0)

  # Optionally set environment or release version
  config.environment = Rails.env
  config.release = 'my-app@1.0.0'

  # You can also configure additional options here
end

Replace 'your_sentry_dsn_here' with the DSN provided by Sentry.

4. Verify Integration

To ensure that Sentry is properly integrated, trigger a test error in your Rails application.

Example Test in a Controller

Add a test action to a controller:

class TestController < ApplicationController
  def trigger_error
    begin
      # Simulate an error
      raise 'This is a test error for Sentry!'
    rescue => e
      Sentry.capture_exception(e)
    end

    render plain: 'Error triggered!'
  end
end

Add a route to trigger this action:

# config/routes.rb
get 'trigger_error', to: 'test#trigger_error'

Visit http://localhost:3000/trigger_error in your browser, and it should trigger an error that gets sent to Sentry.

5. Optional Configurations

You can adjust or add more configurations based on your needs.

• Filter Out Sensitive Data: Configure Sentry to filter out sensitive information using before_send:

  Sentry.init do |config|
    config.before_send = lambda do |event, hint|
      # Modify or filter the event here if needed
      event
    end
  end

• Ignore Certain Errors: Ignore specific types of errors:

  Sentry.init do |config|
    config.excluded_exceptions = ['ActionController::RoutingError']
  end

• Set Up Environment-specific Settings: Adjust settings for different environments (development, production):

  Sentry.init do |config|
    if Rails.env.production?
      config.traces_sample_rate = 1.0 # Full sampling in production
    else
      config.traces_sample_rate = 0.0 # No performance monitoring in non-production environments
    end
  end

6. Monitor and Review Errors

Log in to your Sentry dashboard to monitor errors, performance issues, and other events. Sentry will provide detailed reports and context for each captured exception.

Summary

1. Sign Up: Create a Sentry account and project to get your DSN.
2. Add Gems: Include sentry-ruby and sentry-rails in your Gemfile.
3. Configure Sentry: Add the DSN and other configurations in config/initializers/sentry.rb.
4. Test Integration: Trigger a test error to ensure Sentry is capturing events.
5. Optional Configurations: Customize your Sentry setup as needed for filtering, ignoring errors, and environment-specific settings.
6. Monitor: Use the Sentry dashboard to review and analyze captured events.

By following these steps, you will have integrated Sentry with your Rails application, providing you with valuable insights into errors and performance issues.

Setting Up Devise

Automating Emails Using Python Script

Automating the sending of daily email reports in Python involves a few steps. Below is a simple script using the smtplib library to send emails, along with a walkthrough to set it up.

Step 1: Install Required Libraries

If you haven't already, make sure you have Python installed. You can install any additional libraries using pip if needed (though smtplib is part of the standard library).

pip install yagmail

Step 2: Create the Python Script

Here's a basic script that sends an email with a report. We'll use yagmail, which simplifies sending emails.

import yagmail
import datetime

def send_daily_report():
    # Email credentials
    email_address = "your_email@gmail.com"
    email_password = "your_password"  # Consider using an app password for security
    
    # Create a Yagmail client
    yag = yagmail.SMTP(email_address, email_password)
    
    # Email details
    recipient = "recipient@example.com"
    subject = f"Daily Report - {datetime.date.today()}"
    
    # Here you can generate your report content
    report_content = "This is your daily report:\n\n"
    report_content += "Add your report data here."
    
    # Send the email
    yag.send(
        to=recipient,
        subject=subject,
        contents=report_content
    )
    print(f"Daily report sent to {recipient}.")

if __name__ == "__main__":
    send_daily_report()

Step 3: Set Up Email Credentials

1. Create an Email Account: If you don't want to use your personal email, consider creating a dedicated email account for sending reports.

2. Allow Less Secure Apps: For Gmail, you might need to allow access for less secure apps or set up an app password:

   • Go to your Google account settings.
   • Under "Security," look for "App passwords" to generate a specific password for this script.

3. Replace Credentials: In the script, replace your_email@gmail.com and your_password with your actual email and the password or app password.

Step 4: Test the Script

Run the script to ensure it's working properly:

python send_daily_report.py

You should see a confirmation message, and the recipient should receive the email.

Step 5: Schedule the Script to Run Daily

To run this script automatically every day, you can use a task scheduler:

• On Windows: Use Task Scheduler.
• On macOS/Linux: Use cron.

Example of Setting Up a Cron Job (macOS/Linux)

1. Open your terminal.

2. Type crontab -e to edit your crontab file.

3. Add a line to schedule your script. For example, to run it every day at 8 AM:

   0 8 * * * /usr/bin/python3 /path/to/your/send_daily_report.py

   Make sure to replace /usr/bin/python3 with the path to your Python executable and /path/to/your/send_daily_report.py with the path to your script.

Step 6: Monitor the Script

After setting it up, keep an eye on the email delivery to ensure it's working as expected. You may want to log errors or successes for debugging.

Security Note

Always be cautious about hardcoding credentials in your scripts. For enhanced security, consider using environment variables or a configuration file that isn't included in version control.

Conclusion

That's it! You've now set up a Python script to automate sending daily email reports. If you have further questions or need adjustments, feel free to ask!

Contrast Between Flask API and Rails API

Flask API

• Handles Job applications and user interactions with job posts such as
• Pinned job posts
• Pinned quotes
• Saved job posts
• Follow ups
• Viewed job posts
• User's journal notes
• Handles analytics such as weekly job reports
• Searching and filtering

Rails API

• Job Posts management
• Email alerts/notifivations when a new job is created