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What We Did So Far?

So far, our team has undertaken numerous projects encompassing Django and full-stack development, culminating in robust web solutions. We've seamlessly integrated OpenAI functionalities into our projects, enhancing their capabilities and intelligence. These

GLA AI

GLA AI v1.0

GLA AI

Here’s a breakdown of the GLA AI project:

  1. Technologies Used:
    – Django: Web framework for backend development.
    – React JS: Frontend library for building dynamic user interfaces.
    – Open AI: Integration for advanced natural language processing capabilities.
  2. Execution Optimization:
    – Django Q: Utilized for multiprocessing, enhancing task execution efficiency.
  3. Deployment:
    – Docker: Containerization technology to ensure consistency across different environments.
    – Azure Web App: Hosting platform for robust infrastructure and scalability.
  4. Automation
    – CI/CD Pipelines: Implemented for continuous integration and continuous deployment, streamlining the release cycle.
  5. Integration with Messaging Platforms:
    – WhatsApp, Telegram, and Email Bots: Integrated with the GLA AI web application.
    – Purpose: Facilitates the seamless transfer of keys and documents for quality assurance (Q/A).
    – Process: Users interact with bots via their preferred messaging platform to initiate Q/A processes.
  6. Workflow Enhancement:
    – Unified Communication and Document Management: Centralizes communication and document handling workflows.
    – Benefits: Improves efficiency and productivity by streamlining processes and reducing manual intervention.
  7. Objective:
    – Enhance Communication and Document Handling: Simplifies interactions and ensures smooth collaboration between users and the GLA AI system.
  8. Outcome:
    – Improved Efficiency and Productivity: Optimizes workflows and reduces turnaround time for document handling and quality assurance processes.

Organio Courses

Deployed Version

Organio Courses

Here’s a breakdown of the OrganioCourses project:

  1. Project Name: OrganioCourses
  2. Technologies Used:
    – Angular: Frontend framework for building dynamic user interfaces.
    – Django: Backend framework for web development and API management.
    – Azure Web App: Hosting platform for deploying and managing web applications.
    – Azure Serverless Functions: Serverless computing service for running event-triggered code without provisioning or managing servers.
  3. Integration with 3rd Party Service:
    – Stripe Payment Method: Integrated for handling online payments and subscription management.
    – Purpose: Facilitates secure and seamless payment processing for course subscriptions.
    – Process: Users can securely pay for course subscriptions using credit/debit cards through the Stripe payment gateway.
  4. Description:
    OrganioCourses is an online course selling website offering monthly-based subscription plans. The platform provides access to a wide range of video courses and content courses designed to enhance users’ knowledge and skills in various domains.
  5. Key Features:
    – Subscription-based Model: Users can subscribe to monthly plans to gain access to all available courses.
    – Video Courses: High-quality video content covering a variety of topics and subjects.
    – Content Courses: Comprehensive written content, tutorials, and resources for self-paced learning.
    – Secure Payment Processing: Integration with Stripe payment gateway ensures secure and seamless online payments.
    – User Authentication and Authorization: Secure user authentication and authorization mechanisms to protect user data and course content.
    – Responsive Design: Responsive user interface design optimized for different devices and screen sizes for a seamless user experience.
  6. Objective:
    The primary objective of OrganioCourses is to provide users with a convenient platform to access high-quality educational content through a subscription-based model. By offering a diverse range of video courses and content courses, the platform aims to cater to the learning needs of users across various domains.
  7. Outcome:
    OrganioCourses aims to empower users by providing them with access to valuable educational resources and learning materials. Through its user-friendly interface, secure payment processing, and diverse course offerings, the platform strives to enhance the learning experience and facilitate continuous skill development for its users.

Ticket Master API's Automation

Here’s a description of the automation process for data scraping of Ticketmaster events:

  1. Project Name: TicketMaster Data Scraping Automation
  2. Overview:
    The TicketMaster Data Scraping Automation project aims to automatically retrieve event data from Ticketmaster’s platform on a daily basis. This data is focused on events in the UK region and is processed for further analysis and utilization.
  3. Technologies Used:
    – AWS Lambda Functions: Serverless computing service used to execute Python scripts on a scheduled basis for data scraping.
    – Python: Programming language utilized for web scraping and data processing.
    – AWS RDS Database: Managed relational database service used for storing scraped event data.
    – Data Cleaning and Processing: Python libraries and custom scripts employed for cleaning and processing the scraped data to ensure accuracy and consistency.
  4. Automation Process:
    – AWS Lambda Functions Setup: Python scripts for web scraping are deployed as AWS Lambda functions. These functions are scheduled to run daily using AWS CloudWatch Events or AWS EventBridge.
    – Web Scraping: The Python scripts utilize web scraping techniques to extract event data from Ticketmaster’s website. This includes information such as event name, venue, date, time, ticket prices, and event description.
  5.  Data Extraction and Storage: The scraped event data is extracted from the website and stored in the AWS RDS database. The database schema is designed to efficiently organize and store the relevant information.
  6. Data Cleaning and Processing: – Upon retrieval, the event data undergoes cleaning and processing steps to ensure consistency and quality. This may involve removing duplicates, handling missing values, and formatting data fields.
  7. Data Analysis and Utilization: Once the data is cleaned and processed, it can be utilized for various purposes such as generating reports, analyzing trends, and informing decision-making processes related to event management and marketing.
  8. Objective:
    The primary objective of the TicketMaster Data Scraping Automation project is to streamline the process of collecting event data from Ticketmaster’s platform and make it readily available for analysis and utilization. By automating the data scraping process, the project aims to improve efficiency, accuracy, and timeliness in accessing event information.
  9. Outcome:
    With the automation of data scraping and processing, stakeholders gain access to up-to-date and accurate event data from Ticketmaster’s platform. This enables informed decision-making, enhances event planning and marketing strategies, and facilitates better engagement with audiences interested in attending events in the UK region.