Readme 4 (#13)

* Update README.md to enhance documentation on services, dependencies, and new chatbot integration

* Update README.md to improve image presentation

* Update README.md to enhance image presentation

---------

Co-authored-by: Jamie Liu <[email protected]>

README.md

@@ -37,7 +37,7 @@ These services work together to form a stable and efficient microservices applic
 
 ### Diagram
 
-![AirSense-design](/imgs/airsense_diagram.png)
+<img src="/imgs/airsense-2.png" alt="airsense-2" style="width:70%;">
 
 ### Services
 
@@ -54,9 +54,30 @@ Designed for **Publics**.
 
 `AirGateway` is powered by Flask and aims to handle users' and administrators' restful API requests and forward them to proper mircro-services (`AirConnector`, `AirStore`). It has the ability to handle at least 10,000 requests per seconds.
 
-- Query test example:
+1. Users can utilize the advanced search function to query any keywords throughout the entire database, with results returned in milliseconds.
 
-  `[host_uri]/api/dynamic_query?table_name=busiest_airports_by_delays&limit=6&where=Avg_Departure_Delay:87,Total_Arrivals:4`
+<div style="display: flex; justify-content: space-around;">
+  <img src="/imgs/home-1.png" alt="home-1" style="width:45%;">
+  <img src="/imgs/home-2.png" alt="home-2" style="width:45%;">
+</div>
+
+2. Users can interact with an interactive database exploration tool, enabling them to view data by order, ascending, descending, limit, and more.
+
+<div style="display: flex; justify-content: space-around;">
+  <img src="/imgs/explore-1.png" alt="explore-1" style="width:45%;">
+  <img src="/imgs/explore-2.png" alt="explore-2" style="width:45%;">
+</div>
+
+3. Gateway also provides powerful query API portal, allowing user and other services query data via Restful API easily.
+
+<div style="display: flex; justify-content: space-around;">
+  <img src="/imgs/api-1.png" alt="api-1" style="width:45%;">
+  <img src="/imgs/api-2.png" alt="api-2" style="width:45%;">
+</div>
+
+Query test example:
+
+`[host_uri]/api/dynamic_query?table_name=busiest_airports_by_delays&limit=6&where=Avg_Departure_Delay:87,Total_Arrivals:4`
 
 #### AirConnector
 
@@ -65,47 +86,71 @@ Designed for **Administrator**.
 - _Access using the following link:
   [https://pywepkfr9p.us-east-2.awsapprunner.com/](https://pywepkfr9p.us-east-2.awsapprunner.com/)_
 
-`AirConnector` connectes `AirBroker` and `AirStore`. It transfers data tables from databricks to `AirStore` and ready for query by `AirGateway`.
+`AirConnector` connectes `AirBroker` and `AirStore`. It transfers data tables from databricks to `AirStore` and ready for query by `AirGateway`. Administrator can check the status of two database services, manually transfer data, and see history of operations.
 
 - Transfering test example:
 
   `curl -X POST "[host_uri]:8000/transfer_table?table_name=delays_by_day`
 
-### Dependencies
+<img src="/imgs/admin.png" alt="AdminDash" style="width:600px;">
+
+## Leveraging LLM for Chatbot
+
+We have integrated an advanced chatbot powered by OpenAI's API. This allows users to interact with the application through natural language conversations. Users can ask questions and get insights about flights, data, and any concern while using this application
+
+### Key Features:
+
+- **Natural Language Processing**: Understands and processes user queries in plain English.
+- **Real-time Responses**: Provides instant answers to user questions.
+- **Contextual Understanding**: Maintains context to handle follow-up questions effectively.
 
-1. Languages and Frameworks:
+<img src="/imgs/chat.png" alt="Chatbot" style="width:600px;">
 
-   - Python 3.12.8 with Flask and FastAPI for API handling.
+## Dependencies
 
-   - SQL for querying and GraphSQL database for data storage.
+### Languages and Frameworks:
 
-2. Tools and Services:
+- Python 3.12.8
 
-   - Databricks: For data processing.
+- Flask: For powering `AirGateway` user interface and Restful API requests.
 
-   - Docker: Containerization of services for consistency and portability.
+- FastAPI: For powering `AirConnector` and administrator interface.
 
-   - AWS App Runner: For auto-scaling microservices.
+- Spark: For preprocessing raw data and outputting clean tables in Databricks.
 
-   - AWS RDS Aurora: As the relational database solution.
+- SQL: For querying and GraphSQL database for data storage.
 
-   - Locust: For load testing Flask applications.
+### Tools and Services:
 
-   - CloudFormation: Infrastructure as Code for streamlined AWS resource provisioning.
+- Databricks: For data preprocessing.
 
-3. Development Tools:
+- Docker: Containerization of services for consistency and portability.
 
-   - Pyenv: Python version management.
+- AWS Elastic Container Registry (ECR): Store, manage, and deploy container images securely.
 
-   - Virtual Environment: For dependency isolation during development.
+- AWS App Runner: For auto-scaling microservices.
 
-   - GitHub Actions: CI/CD pipelines for automated testing and deployment.
+- AWS RDS Aurora: As the relational database solution.
+
+- Locust: For load testing Flask applications.
+
+- CloudFormation: Infrastructure as Code for streamlined AWS resource provisioning.
+
+- CloudWatch: For monitoring and observability of AWS resources and applications.
+
+### Development Tools:
+
+- Pyenv: Python version management.
+
+- Virtual Environment: For dependency isolation during development.
+
+- GitHub Actions: CI/CD pipelines for automated testing and deployment.
 
 ## Development
 
 For details regarding repo structure and how to set up development environment, see [development.md](/development.md).
 
-### Logging
+## Logging
 
 We use AWS CloudWatch for logging. AWS CloudWatch is a
 monitoring and observability service provided by AWS.
@@ -193,3 +238,7 @@ Resources:
 
 All reflections including team member self reflection files and
 team reflection after meeting are included in the `Team_Reflections` folder in this repository.
+
+## About
+
+![about](/imgs/about.png)