FileQna is a web application that allows users to upload a CSV file and ask questions related to the data in the file. It is powered by Langchain, an AI-based language model, and deployed using the Streamlit framework.
- Upload a CSV file: Users can easily upload their CSV files containing data that they want to analyze and ask questions about.
- Ask any question: Once the file is uploaded, users can input their questions in natural language and get relevant answers based on the data in the CSV file.
- Powered by Langchain: FileQna leverages the capabilities of Langchain, an advanced AI-based language model, to understand and process the user's queries.
- Streamlit Deployment: The web application is deployed using Streamlit, making it easy to access and use without the need for any complex setup.
- Upload CSV File: Click on the "Upload" button and select the CSV file containing the data you want to analyze.
- Ask a Question: In the text input box, enter your question about the data. For example, "What is the average age of the employees?" or "Which product has the highest sales?"
- Get the Answer: Click on the "Ask" button, and FileQna will process your question using Langchain and provide you with the answer based on the data in the CSV file.
To run FileQna locally, follow these steps:
- Clone this repository to your local machine.
- Install the required dependencies by running:
pip install -r requirements.txt- Run the Streamlit app:
streamlit run app.py- Access the web application by opening your web browser and navigating to the URL displayed in the terminal.
- Langchain: An advanced AI-based language model for natural language processing.
- Streamlit: A Python framework for building web applications with simple Python scripts.
- Pandas: A powerful library for data manipulation and analysis, used to read and process CSV files.
- SQLAlchemy: A SQL toolkit and Object-Relational Mapper (ORM) for Python, used to manage the temporary SQLite database.
demo2.Streamlit.-.Personal.-.Microsoft.Edge.2023-07-23.23-30-36.mp4
FileQna is built on the amazing work of the Langchain and Streamlit communities, and we are grateful for their open-source contributions.