Streamlit Jobs

I want to turn my raw market-trends data into clear, action-ready insights with the help of a custom AI assistant. The core requirement is data analysis: the system should ingest whatever format I send—usually CSV and Excel dumps, occasionally a direct database export—and automatically clean, analyse, and visualise the material so that I can spot patterns, outliers, and emerging opportunities at a glance. I expect the workflow to be fully automated once a file is dropped into a designated folder (or a table is updated). Behind the scenes you can rely on Python, pandas, scikit-learn, or similar tooling; as long as the final solution is reproducible and easy for me to extend, the choice of libraries is up to you. I’m comfortable running scripts locally, but a lightweight w...

I need a complete, ready-to-run video analytics tool that flags car-collision accidents in city-surveillance footage. The workflow must stay simple for the end user: they upload a clip, hit “Analyze,” and, within seconds, the screen returns something like: Accident Detected: YES Severity: HIGH (low | medium | high) Confidence: 92 % Time-stamp: 00:12 Key points to build in • Scope of detection: only car collisions; no pedestrian or bicycle tracking at this stage. • Source footage: city CCTV style feeds (fixed street-level cameras). • No real-time push notifications are required—the result can appear once processing is finished. I will rely on you to select or curate a robust, publicly available dataset (or a combination of datasets) that...

I am building a decision-support tool that acts like an investment banker for hospital deals. The workflow starts with raw historical statements—revenue, profit, cash flow, debt and related line items—pulled in from spreadsheets or a database. I need this data to be cleaned and validated automatically so that outliers, missing values and inconsistent formats are handled without manual intervention. Once the dataset is in shape, the system must calculate core ratios—operating and EBITDA margins, liquidity and leverage indicators, year-over-year growth rates, cash conversion, risk flags—and feed them into a machine-learning pipeline. I have no fixed allegiance to any one algorithm, but I would like to begin with a solid linear regression benchmark and keep the door...