hitesh-warhate/DataSentinel-AI-LLM-Powered-Data-Quality-Pipeline ? reverse-engineered prompt

Build me a Streamlit app that lets me upload a raw CSV file and then runs it through an AI powered data quality pipeline. I want it to profile the data first, then use three separate agents to handle schema creation, data quality rules, and SQL generation, so the app can create Bronze and Silver tables in PostgreSQL, load the raw data into Bronze, and then clean and promote the good records into Silver.

Use an LLM connection that can work with Groq, Gemini, OpenAI, or similar providers from environment settings, and keep the database config in a simple .env file. The app should show the profiling info, the generated rules, the SQL it creates, and the database results in a clear way. If you need to, look up the current docs for any library or API details. Please also make sure the code is organized cleanly across the agent, database, and utility parts, and that it runs from app.py without me having to wire everything together manually.