hpm

hpm is an AI-powered tool designed to assist with common office writing tasks.

Key Features

This Streamlit application assists in creating tickets for Jira/Rally ticketing systems. The key features include:

Goal Input: Users can input the specific issue or feature that needs to be addressed in the ticket.
Acceptance Criteria Input: Users can define the expected outcome or desired functionality after the ticket resolution.
Ticket Generation: The application generates a ticket summary using a GPT model upon clicking the 'Write Ticket' button.
GPT Model Integration: The application integrates with a GPT model to generate the ticket summary.
User-friendly Interface: The application uses Streamlit's simple and intuitive interface for easy ticket creation.
Formatting Guide: The application provides a formatting guide to help users create well-structured and clear tickets.

This Streamlit application generates a weekly report for AISL. The main features include:

Role Input: Users can input their role, with "Principal Cloud Architect" set as the default.
Accomplishments Section: Users can enter their accomplishments for the week.
Challenges/Blockers Section: Users can describe any challenges or blockers they faced during the week.
Next Steps Section: Users can outline their planned next steps.
Summary Generation: The application generates a summary of the user's input using a GPT model upon clicking the "Summarize" button.

This Streamlit application assists users in providing an update for a daily stand-up in the agile/scrum methodology. The main features include:

Text Input Areas: Users can enter their daily accomplishments, plans for tomorrow, and any challenges or blockers they are facing.
Summarize Button: Users can generate a summary of their input using a GPT model by clicking the 'Summarize' button.
GPT Model Integration: The application integrates with a GPT model to generate a summary of the user's input.
Dynamic Content Rendering: The application dynamically renders content based on user interaction.
User-Friendly Interface: The application uses headers and separators to clearly distinguish between different sections.

This Streamlit application assists in writing professional emails. The main features include:

Role Input: Users can input their role.
Recipient Input: Users can input the recipient's name.
Email Body Input: Users can input the content of the email.
Email Generation: The application generates a professional email based on the user's input and the predefined prompt structure upon clicking the 'Write Email' button.
User Configuration: The application uses a user configuration file to retrieve the user's details for use in the email generation.
GPT Model: The application uses a GPT model to generate the email content.

This Streamlit application generates conversational responses to Slack messages. The key features include:

Title Rendering: The application displays a title "Chat Response" and the user's job title.
Slack Message Input: Users can input recent Slack messages that they want to respond to.
Response Generation Button: Users can trigger the response generation process by clicking the "Respond" button.
Response Generation: The application uses a GPT model to generate a conversational response to the input Slack messages.
User Configuration: The application uses a UserConfig class from a config module to get user-specific information.

Ensure you have the secrets.toml in the .streamlit directory.

ls .streamlit

Should return the following:

secrets.toml

This file should contain the following keys to be able to access API keys.

[openai]
key = "{MY OPENAI KEY HERE}"
org = "{MY OPENAI ORG HERE}"

Create a virtual environment and activate it.

python -m venv .venv
source .venv/bin/activate

Set up dependencies with Makefile

make setup

Run application with Makefile

make run

streamlit run hpm.py