Class Diagram Generation

Hello Everyone,
I’m looking for a way to get GPT3 to help make class diagrams (UML) from a random text as context or any other way to get modular building blocks for designing a program.

For Example - Class Diagram Examples Created by the Creately Team | Creately

In the linked site there are examples for random projects.
Is there a way to use GPT3 as an assistant to create these?

Person
+name_string
+age_number +date of birth_number +address_string +phone num_number +gender_string
+date of join() +date of discharge()
Patient
+gender_string +prescription_string -allergies_string
+in_time() +out_time()
+name_string +address_string +phon_number
+available time() +states()
Hospital
Staff
+joined: date +qulification_string +certification_string +languages_string
+work dutes() +hoil days()
Administrative staff
+name of emp_string #details_string -time period_number
+names_string +deprt_string
+join time()
Technical staff
+name_string +location_string +join time()
+open_list() +dose_list()
Operations staff
Doctor
+name_string +addresss_string +phone_number + speciality_string
+joined_date() +dute_time()
surgen
+opr_type +free_number +block_string
+opr_time() +week period()
Nurse
+name_string +address_string +phone_number
+join_date()
Receptionist
+patient_details +problem_string +age_string
+in_time() +ot_time()
Technician
+name_string +address_string +counter_number
+opr_name() +total_opr()
Laboratorist
+name_string +test_details +time_number
+room-num() +opr_time() make tree representation of it

can you crreate diagram

erson
+name_string
+age_number +date of birth_number +address_string +phone num_number +gender_string
+date of join() +date of discharge()
Patient
+gender_string +prescription_string -allergies_string
+in_time() +out_time()
+name_string +address_string +phon_number
+available time() +states()
Hospital
Staff
+joined: date +qulification_string +certification_string +languages_string
+work dutes() +hoil days()
Administrative staff
+name of emp_string #details_string -time period_number
+names_string +deprt_string
+join time()
Technical staff
+name_string +location_string +join time()
+open_list() +dose_list()
Operations staff
Doctor
+name_string +addresss_string +phone_number + speciality_string
+joined_date() +dute_time()
surgen
+opr_type +free_number +block_string
+opr_time() +week period()
Nurse
+name_string +address_string +phone_number
+join_date()
Receptionist
+patient_details +problem_string +age_string
+in_time() +ot_time()
Technician
+name_string +address_string +counter_number

can you crreate diagram

Are you looking for ChatGPT?

https://chat.openai.com

After some discussion with bing…, Here is what Bing Suggested

(Using Google Colab ask big to generate in for pydot.graph_from_dot_data)

Import pydot library

import pydot

Create a DOT graph object from the code

dot = pydot.graph_from_dot_data(“”"
digraph class_diagram {
// Set graph attributes
graph [rankdir=BT]
// Set node attributes
node [shape=record]
// Define nodes
Person [label=“{Person|+name_string\l+age_number\l+date_of_birth_number\l+address_string\l+phone_num_number\l+gender_string\l|+date_of_join()\l+date_of_discharge()\l}”]
Patient [label=“{Patient|+gender_string\l+prescription_string\l-allergies_string\l|+in_time()\l+out_time()\l}”]
Hospital [label=“{Hospital|+name_string\l+address_string\l+phone_number\l|+available_time()\l+states()\l}”]
Staff [label=“{Staff|+joined: date\l+qualification_string\l+certification_string\l+languages_string\l|+work_duties()\l+holiday_days()\l}”]
Administrative_staff [label=“{Administrative staff|+name_of_emp_string\l#details_string\l-time_period_number\l|}”]
Technical_staff [label=“{Technical staff|+name_string\l+location_string\l+join_time()\l|}”]
Operations_staff [label=“{Operations staff|+name_string\l+location_string\l+join_time()\l|}”]
Doctor [label=“{Doctor|+name_string\l+addresss_string\l+phone_number\l +speciality_string\l|+joined_date()\l+duty_time()\l}”]
Surgeon [label=“{Surgeon|opr_type\lop_free_number\lop_block_string\lop|opr_time()\lop_week_period()\lop}”]
Nurse [label=“{Nurse|+name_string\l +address_string \lop_phone_number \lop | +join_date() \lop }”]
Receptionist [label=“{Receptionist | +patient_details \lop_problem_string \lop_age_string \lop | +in_time() \lop_ot_time() \lop }”]
Technician [label=“{Technician | +name_string \lop_address_string \lop_counter_number \lop | }”]

// Define edges
Person → Patient
Person → Staff
Staff → Administrative_staff
Staff → Technical_staff
Staff → Operations_staff
Operations_staff → Doctor
Operations_staff → Nurse
Doctor → Surgeon
Technical_staff → Receptionist
Technical_staff → Technician

}
“”")

Write the graph to a PNG file

dot[0].write_png(“hospital1.png”)

Display the graph on the screen

dot[0].write_png(“hospital1.png”, prog=“dot”)

This code will generate

1 Like

create a diagram for data security including protocols like access control, authentically protocols etc…,

please use pydot to create a diagram for data security including protocols like access control, authentically protocols etc…,

ChatGPT

ChatGPT

Creating a diagram for data security using pydot involves several steps. Pydot is a Python interface to Graphviz, which allows the creation of highly customizable graphs. Here’s a general approach:

  1. Identify the Components: Before coding, identify the key components and how they interact. For a data security diagram, this might include:
  • Access Control
  • Authentication Protocols
  • Encryption Methods
  • Firewalls
  • Intrusion Detection Systems
  • Data Storage
  • Network Infrastructure
  • User Devices
  1. Define the Relationships: Determine how these components interact or relate to each other. For instance, Authentication Protocols might be connected to Access Control and User Devices.
  2. Implementing with Pydot: Create nodes for each component and edges to represent the relationships.
  3. Customization: Customize the graph’s appearance, like color, shape, or label style, to make it more informative and visually appealing.

Let’s start by creating a basic diagram. We’ll define nodes for each major component and create edges to illustrate their relationships. After the initial implementation, you can provide more specific relationships or customization requests, and I’ll adjust the diagram accordingly.

Let’s begin the coding process.

I’ve created a basic diagram representing various components of data security and their relationships. You can download and view the diagram using the link below:

Download Data Security Diagram

This diagram is a starting point. Feel free to suggest specific relationships, additional components, or customization (like color or shape changes) to make it more aligned with your requirements. ​​

You

we need more detail on each node please

ChatGPT

ChatGPT

Certainly! To add more detail to each node in the diagram, we can include descriptions or specific examples of each component. For instance, under “Authentication Protocols,” we could list examples like OAuth, SAML, or Kerberos. Similarly, for “Encryption Methods,” we might include AES, RSA, etc.

Let’s update each node with a brief description or examples. This will make the diagram more informative. I’ll proceed with adding these details, and if you have specific information you want to be included for each node, please let me know. Let’s enhance the diagram.

I have updated the diagram by adding more detailed descriptions and examples to each node. This should provide a clearer understanding of each component within the data security framework.

You can download and view the updated diagram here:

Download Updated Data Security Diagram

If there’s more specific information you’d like to include, or if you have other customization requests, feel free to let me know! ​​

You

for authentification protocols please make a seperate node tree and add it back to the original

ChatGPT

ChatGPT

Certainly! We can create a separate node tree specifically for Authentication Protocols and then integrate this detailed tree back into the original diagram. In the detailed tree for Authentication Protocols, we’ll include various types of authentication methods and protocols.

Let’s start by creating this detailed tree for Authentication Protocols. Afterward, I’ll integrate it into the main Data Security Diagram. I’ll proceed with the detailed tree now.

The detailed node tree for Authentication Protocols has been created and integrated back into the main Data Security Diagram. This updated diagram now includes a more comprehensive view of various authentication methods and protocols.

You can download and view the final diagram here:

Download Final Data Security Diagram

This diagram should provide a clearer and more detailed view of the authentication protocols within the context of data security. If there are any further modifications or details you’d like to add, please let me know! ​

Before there was no data analysis tool so its easier now. The trick is you have to ask for seperate graphs and ask it to add back. if that doesnt work then try to



If anyone knows how to get chatgpt to auto iterate over this process please let know.

±---------------------------+ ±-----------------------+
| Faculty/Division |<>----------| Admin |
±---------------------------+ ±-----------------------+
| - facultyCode: string | | - adminId: string |
| - facultyName: string | | - password: string |
±---------------------------+ ±-----------------------+

±---------------------------+ ±-----------------------+
| Home Page | | Dashboard |
±---------------------------+ ±-----------------------+
| - categories: string | | - studentInfo: string |
±---------------------------+ | - employeeInfo: string |
| - academicInfo: string |
±-----------------------+

±---------------------------+ ±-----------------------+
| Educational Entity | | Admin Academic |
±---------------------------+ ±-----------------------+
| - entityCode: string | | - academicNotice: string|
| - entityName: string | | - eventDetails: string |
| | ±-----------------------+
| |
| |
| |
| |
| |
| |
| |
±---------------------------+

±---------------------------+ ±-----------------------+
| Course Creation Process | | Programme Creation |
±---------------------------+ | Process |
| - courseCode: string | ±-----------------------+
| - courseTitle: string | | - programmeCode: string |
| - courseType: string | | - programmeTitle: string|
| - courseComponents: string | | - programmeType: string |
| - courseCAComponents: string | | - specializations: string |
| - finalAssessmentComponents: string | | - qualificationType: string |
| - prerequisites: string | | - courses: string |
| - creditValue: int | | - totalCredits: int |
| - level: int | ±-----------------------+
| - status: string |
| - offerYear: int |
±---------------------------+

±---------------------------+ ±-----------------------+
| Admin Application | | Operation Plan Creation|
±---------------------------+ | Process |
| - applicationNo: string | ±-----------------------+
| - applicationName: string | | - faculty: string |
| - regYear: int | | - academicYear: int |
| - programme: string | | - activeCourses: string|
| - applicantId: string | ±-----------------------+
| - status: string |
±---------------------------+

±---------------------------+ ±-----------------------+
| Human Resource | | Application Process |
±---------------------------+ ±-----------------------+
| - staffProfile: string | | - qualification: string |
| - name: string | | - appliedProgram: string|
| - designation: string | | - processingFee: string |
| - qualification: string | | - initialApprrovals: string |
| - typeOfAppointment: string| ±-----------------------+
| - affiliation: string |
| - function: string |
±---------------------------+

±---------------------------+
| Staff |
±---------------------------+
| - fullName: string |
| - qualification: string |
| - designation: string |
| - image: string |
| - natureOfAppointment: string |
±---------------------------+

±---------------------------+
| Admission Management |
±---------------------------+
| - student: Student |
±---------------------------+

±---------------------------+
| Student |
±---------------------------+
| - studentId: string |
| - studentName: string |
| - program: string |
| - admissionYear: int I apologize, but it seems that the content you provided does not include enough information to create a complete and accurate class diagram. The document you provided only includes some requirements and descriptions, but it does not provide a comprehensive overview of the system or its entities and relationships.

To create a class diagram, we would need a more detailed understanding of the system, including all the entities, their attributes, and the relationships between them. Additionally, the class diagram would require information about methods and behaviors of the classes.

If you can provide more specific and detailed information about the system, including the entities, their attributes, and the relationships, I would be happy to assist you in creating a class diagram.

Yeah, I now see that the response was not a class diagram (I am not a class diagram expert - was interested in learning it). The main idea I wanted to get across was to use pydot to get the drawings.

You have to start with all the types of nodes then keep adjusting each node one by one as GPT cant manage the context or read the graph reliably with vision.

My Understanding from your response is that a comprehensive class diagram should show links at the variable and method level?

Hospital Graphs - The Nodes in the new graphs would have to be added back and then expanded seperately.


For the Education example (Apologies if I didnt understand it correctly)

It gave me these



Then I asked it to add the detail back and it did this


For the variable level - I provided this

±---------------------------+ ±-----------------------+
| Course Creation Process | | Programme Creation |
±---------------------------+ | Process |
| - courseCode: string | ±-----------------------+
| - courseTitle: string | | - programmeCode: string |
| - courseType: string | | - programmeTitle: string|
| - courseComponents: string | | - programmeType: string |
| - courseCAComponents: string | | - specializations: string |
| - finalAssessmentComponents: string | | - qualificationType: string |
| - prerequisites: string | | - courses: string |
| - creditValue: int | | - totalCredits: int |
| - level: int | ±-----------------------+
| - status: string |
| - offerYear: int |
±---------------------------+

±---------------------------+ ±-----------------------+
| Admin Application | | Operation Plan Creation|
±---------------------------+ | Process |
| - applicationNo: string | ±-----------------------+
| - applicationName: string | | - faculty: string |
| - regYear: int | | - academicYear: int |
| - programme: string | | - activeCourses: string|
| - applicantId: string | ±-----------------------+
| - status: string |
±---------------------------+

It converted these to python classes and then gave me back this

For comprehensive Class Diagrams for now we have to treat each node as a separate diagram and keep the pydot notation GPT writes for use (From Code Interpreter / Data Analysis ) to later ask it to join the seperate graphs together