ProphAIcy

ProphAIcy is a simple web app built with Streamlit that summarizes and visualizees your raw data into meaningful insights with help of Pandas and Seaborn. It also uses PandasAI powered with Gemini to let you speak with your data!

Project Structure

├── app.py       # contains the Entire App Code written in Streamlit
├── setup.sh
├── requirements.txt
└── README.md

Steps for App Installation

1. Clone the App

   git clone https://github.com/pranayvaranasi/ProphAIcy

2. Create the Virtual Environment and Activate it

   python3 -m venv streamlit_env

   source streamlit_env/bin/activate

3. Install Necessary Libraries

   pip3 install -r requirements.txt

4. Set your Google Gemini API key in app.py code and save it

5. Set basic environmental variables

   export STREAMLIT_APP= app.py

   export STREAMLIT_ENV=development

6. Run the Application

   python3 app.py

App Overview

1. Dataset Upload:

   • User uploads dataset (usually in CSV format) to ProphAIcy.
   • Upon upload, ProphAIcy shows the head of the dataset—the first few rows.

2. Summarization:

   • When the user clicks "Summarize," ProphAIcy summarizes on the uploaded content.
   • Similary, user can see Null Values, Value Counts, Missing Values etc

3. Visualization:

   • User can jump to Visualization section by clicking on the arrow in top left
   • Users can click on multiple options like Bar graph, Pie Chart, Box Plot etc to visualize their dataset

4. Chat with Dataset:

   • A Text Box is provided in the end of both Visualization and Summarization section to let users chat with their dataset

Demonstration

Click on the image below for video demonstration 👇

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Tools Used :

Skills

Web Development, Generative AI, Frontend, Backend, API Integration, Data Visualization, Data Analysis