AskHR is a simple Streamlit app that demos how a chatbot can be build with Large Language Models (LLMs). It works with synthetic policy data, department/level selectors, and chat history. It has also functionality like starter chats and contact support button. Here you will find the structure and guidelines how to use it.
AskHR is a simple Streamlit app that demos how a chatbot can be build with Large Language Models (LLMs). It works with synthetic policy data, department/level selectors, and chat history. It has also functionality like starter chats and contact support button. Here you will find the structure and guidline how to use it.
. ├── data/ │ └── policies.json # Sample HR policies (6 departments × 4 seniority levels) ├── src/ │ ├── app.py # Streamlit UI + chat logic │ └── llm.py # OpenAIClient class (prompt builder + responder) ├── .env # Your OpenAI API key lives here ├── requirements.txt # Python dependencies └── README.mdpolicies.jsonSix departments: Engineering, Sales, Marketing, Finance, HR, Customer Support
Four levels each: Junior, Senior, Team Lead, Manager
llm.pyclass OpenAIClient: def __init__(self, openai_api_key, department, level): self.client = OpenAI(api_key=openai_api_key) self.department = department self.level = level def build_prompt(self, policy_summary, user_question) -> list[dict]: """Returns a messages list ready for ChatCompletion.create().""" def generate_response(self, messages: List) -> str: """Calls OpenAI and returns the assistant’s answer."""app.pyDepartment and Level selectors
Starter chats options
Full chat history stored in st.session_state
Contact Support button (mailto: link)
Fallback
Clone & install
git clone https://github.com/maryami66/AskHR.git cd askhr pip install -r requirements.txtAdd your OpenAI key in .env file
OPENAI_API_KEY=sk-...Launch
streamlit run src/app.pyExplore
Pick a department and level in the sidebar.
Click a starter question or type your own.
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Fire up the app, tweak the policies, and enjoy building your very first chatbot! 🎉