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gpt-4o-mini model. Customers can log in (demo login, picks
one of the 4 accounts from the dataset) and ask questions about their
orders, cancellations, service credits, and policies. It can also raise an
escalation to the human support team, but only after the customer confirms.pip install -r requirements.txtexport OPENAI_API_KEY=sk-xxxxxx.streamlit/secrets.toml.example to .streamlit/secrets.toml
and put your key there instead.streamlit run app.pyhttp://localhost:8501)gpt-4o-mini. I picked this one because it's cheap and fast
and handles tool-calling pretty well, which is what this project needed.app.py - the streamlit UI (account picker, chat page, Ops Radar page)agent.py - talks to OpenAI, runs the tool-calling looptools.py - the actual tools the AI can use (search docs, get data, calculate, escalate)data_store.py - loads the excel data + the fee/credit calculation logicdocuments.py - loads and searches the PDF documentsinsights.py - the "Ops Radar" proactive detection logic (SLA risk, duplicate clusters, known-issue matches, volume spikes)data/ - the pdfs and excel file given in the assessment packARCHITECTURE.md and
PRODUCT.md.