Build a Local CSV Query Assistant Using Gradio and LangChain 0 like November 15, 2024Share this postLast Updated on November 15, 2024 by Editorial TeamAuthor(s): Vikram Bhat Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium.In this blog, well walk through creating an interactive Gradio application that allows users to upload a CSV file and query its data using a conversational AI model powered by LangChains create_pandas_dataframe_agent and Ollama's Llama 3.2. This guide will focus on building a local application where the user can upload CSVs, ask questions about the data, and receive answers in real-time.You can find the complete code for this application in the GitHub repository.Gradio is a powerful alternative to Streamlit, offering many new features that make building machine learning applications easy. Gradio excels with simple interfaces and impressive integration capabilities. Some standout features include native support for various data types (such as images, audio, and text), dynamic UI updates, and easy integration with popular libraries like TensorFlow, PyTorch, and LangChain.In this tutorial, we leverage LangChains experimental create_pandas_dataframe_agent, which allows us to analyze simple CSVs without the need to implement complex Retrieval-Augmented Generation (RAG) systems. This makes it ideal for users who want to quickly query CSV data in a conversational manner without the overhead of building a full-fledged RAG system.Additionally, Ollama enables us to run the entire system locally, using Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post