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PANDASAI APP
Overview :
The PANDASAI APP is an application that leverages generative artificial intelligence (LLMs) to interact with Pandas DataFrames. It uses Gradio as the frontend interface and employs PandasAI as a high-level Python wrapper to enable conversational interaction with DataFrames. PandasAI provides generative AI capabilities through APIs like OpenAI, HuggingFace, and Azure, allowing users to configure the backend platform as per their requirements. Key advantages of this application include the ability to upload CSV files and ask questions about the data, facilitating a human-like interaction with the data.
Target Users :
The target audience includes data analysts, data scientists, and anyone who needs to interact with large datasets. The PANDASAI APP provides an intuitive interaction method that allows users to process and analyze data more easily, especially suited for professionals looking to gain quick insights into their data.
Use Cases
A data analyst uploads a sales data CSV file using the PANDASAI APP and asks about sales trends.
A data scientist engages in dialogue with their machine learning model data via the PANDASAI APP to optimize model parameters.
A business analyst leverages the application to obtain quick insights about customer data to support decision-making.
Features
Upload CSV files and inquire about data-related questions.
Engage in conversational interactions with DataFrames using generative AI.
Choose OpenAI, HuggingFace, or Azure as the backend platform.
Plan to support additional models such as HuggingFace and Azure.
Enhance with plotting features.
Provide general insights about the uploaded data, such as shape and head.
Support Docker for running the application within a Docker container.
Plan to push the Docker image to DockerHub for public use.
Deploy the application to Google App Engine.
How to Use
1. Clone the repository: Run `git clone https://github.com/amjadraza/pandasai-app-gradio.git` in the command line.
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2. Install dependencies: First, install the `uv` tool, then create a virtual environment and use `uv` along with the `pyproject.toml` file to install dependencies: `uv venv` and `source .venv/bin/activate`, followed by `uv pip install -r pyproject.toml`.
3. Run the Gradio server: Start the application by running `python src/main.py` in the command line.
4. Access the application: Open the Gradio interface in your browser, upload a CSV file, or start asking data-related questions.
5. Interactive operations: Follow the prompts in the application to query and manipulate the DataFrame.
6. View results: The application will return query results in a conversational format, which can be directly viewed on the interface.
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