Project: AI-Powered Clause Evaluation + Q&A Tool (Python) I want an AI-powered tool built in Python that works with two data sources: 1. A CSV file containing player match data (minutes played, starts, appearances, competitions, seasons). 2. An Excel file containing performance-based contract clauses (e.g. “£10k bonus after 20 league games”). The goal is to use AI to help interpret and work with these contract clauses, then evaluate them against real performance data. ⸻ Task Your task is to: • Merge these two datasets • Evaluate every clause for every player • Determine whether each clause has been triggered • Calculate any financial payout Then export a final consolidated CSV showing: • the clause description • triggered status (TRUE/FALSE) • payout amount • supporting metrics (appearances, minutes, date triggered where applicable) ⸻ AI Q&A Component I also need a lightweight AI Q&A interface on top of this processed dataset. I want to be able to ask natural-language questions like: • “How many games did Player X play?” • “Did Clause Y trigger?” • “What is our total liability this season?” • “Which players are close to triggering a clause?” The system should: • Use an LLM (OpenAI API or similar) to interpret the question • Translate the question into queries over the processed data • Answer only using the data, not by guessing or hard-coding numbers A simple Streamlit app, Flask app, or CLI tool is fine, as long as it’s clear and easy to run locally with updated CSV/Excel files each season. ⸻ Deliverables 1. A Python script or notebook that: • loads the CSV player data and Excel clause file • evaluates all clauses against actual performance • calculates payouts • exports a final results CSV 2. An AI-powered Q&A interface that: • loads the processed dataset • uses an LLM (OpenAI or similar) to understand free-text questions • returns answers based strictly on the data 3. A short README explaining: • setup and required libraries • how to run the script and Q&A tool • how to plug in new season files (new CSV/Excel) 4. Verification that: • sample queries return correct answers • payout totals match a manual check on a few test examples.