README.md

metadata

title: Insights
emoji: 📈
colorFrom: gray
colorTo: yellow
sdk: streamlit
sdk_version: 1.33.0
app_file: app.py
pinned: false

Insights

Deployment

HuggingFace

Modules

• DataLoader: Handles the loading of data either by uploading a CSV file or inputting a URL to a CSV file.
• DataAnalyzer: Provides summary statistics and data types of the loaded dataset.
• DataFilter: Allows users to filter rows based on user-defined conditions.
• DataTransformer: Enables users to perform operations on columns.
• DataVisualizer: Visualizes data with various types of plots (Histogram, Box Plot, Pie Chart, Scatter Plot, Heatmap).

Features

• Upload CSV files or load data from a URL.
• Display the uploaded dataset.
• Show summary statistics and data types.
• Filter rows based on user-defined conditions.
• Perform operations on columns.
• Visualize data with various types of plots (Histogram, Box Plot, Pie Chart, Scatter Plot, Heatmap).
• Transform data.

Detailed Installation Instructions

1. Install the required packages: The project's dependencies are listed in the 'requirements.txt' file. You can install all of them using pip:

   pip install -r requirements.txt
   

2. Run the application: Now, you're ready to run the application. Use the following command to start the Streamlit server:

   streamlit run app.py
   

Web app

1. Main page Data Exploration -> Data Loader -> DataQA (LLM with python interpreter/CSV agent) -> Data Analyzer -> Data Filter -> Data Visualizer

2. Data Transformation -> handling null values -> creating new columns -> removing columns -> Changing datatypes -> give option to analyse the transformed dataset or save it.

3. Natural language dataparty (Pure LLM) -> Insights generation -> Automating the data analysis/transformation -> generating a report

Running using Docker

1. Build the docker image using

   docker build -t insights .
   

2. Run the Docker container with

   docker run -p 8501:8501 -e GOOGLE_API_KEY=<you-api-key> insights