📊 Data Insights AI Assistant

An interactive and intelligent Streamlit-based data analysis tool powered by Google's Gemini AI via LangChain. Upload your datasets, get instant statistics, visualizations, and ask natural language questions about your data — all in one app. Link: https://data-storyteller-cvdh59pt2r2uzdovxdxapo.streamlit.app/

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🚀 Features

• 📁 Upload Support
  Upload .csv, .xlsx, or .xls files for analysis.

• 📈 Overview Tab
  View dataset shape, data types, missing values, duplicate rows, correlation matrix, distributions, and more.

• 💬 Chat Assistant
  Ask questions about your data using natural language. Powered by Gemini 1.5 via LangChain.

• 🔍 Insights Tab
  Auto-generated basic and advanced statistical insights, categorical analysis, and ML preprocessing recommendations.

• 📊 Interactive Graphs
  Create scatter plots, line charts, bar charts, histograms, box plots, violin plots, pair plots, and heatmaps. Filter data interactively.

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🧠 Powered By

• 🧠 Gemini 1.5 (via LangChain)
• 📦 Streamlit
• 📉 Plotly
• 📊 Seaborn / Matplotlib
• 🧪 Pandas / NumPy
• 🌐 Dotenv for API key management

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📂 Folder Structure

├── app.py              # Main Streamlit app
├── .env                # Store your Google API key here
├── requirements.txt    # All required Python dependencies
└── README.md           # This file

🔐 Environment Setup Create a .env file in the root directory with your Google API key:

GOOGLE_API_KEY=your_google_genai_key 🔑 You must have access to Google Gemini API via Google AI Studio or [Vertex AI].

🧪 Installation 🔧 Step-by-step

1. Clone the repository

git clone https://github.com/your-username/data-insights-ai-assistant.git cd data-insights-ai-assistant

2. Create a virtual environment (optional but recommended)

python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt ▶️ Run the App streamlit run app.py

🖼️ Screenshots

🧾 Overview Tab

🤖 Chat Assistant

📊 Interactive Graphs

🧠 Example Prompts for Chat Assistant "What is the average salary across departments?"

"Suggest preprocessing steps for building a classification model."

"Are there any correlations between age and purchase amount?"

"Which categories have the highest frequency?"

📦 Requirements

txt Copy Edit streamlit pandas numpy plotly matplotlib seaborn langchain langchain-google-genai python-dotenv openpyxl

🙌 Acknowledgements

LangChain

Google Gemini

Streamlit

Plotly

💡 Future Improvements

Add support for uploading multiple files

Enable export of visualizations

Add session saving and report generation

Improve multilingual chat support

Built with ❤️ by Aman Kumar Rai – Contributions Welcome!