/hpm

This AI tool is designed to create succinct weekly reports from rough notes. By summarizing accomplishments, challenges, and next steps, the app aids in presenting clear and concise information for both peers and managers.

Primary LanguagePython

hpm

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

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Key Features

Jira Ticket Generator

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

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

Weekly Report

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

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

Daily Stand-Up

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

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

Email Writer

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

  1. Role Input: Users can input their role.
  2. Recipient Input: Users can input the recipient's name.
  3. Email Body Input: Users can input the content of the email.
  4. 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.
  5. User Configuration: The application uses a user configuration file to retrieve the user's details for use in the email generation.
  6. GPT Model: The application uses a GPT model to generate the email content.

Slack Writer

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

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

Software Installation

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

or

streamlit run hpm.py