📊 AI Data Analysis Assistant

Intelligent CSV Analysis powered by Groq LLaMA 3.3 & Streamlit

Upload any CSV, get instant statistics, beautiful charts, and AI-powered answers — all in one sleek dark-themed web app.

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✨ Features

Feature	Description
📁 CSV Upload	Upload any CSV or use the built-in retail dataset
📈 Auto Visualizations	Bar, Pie, Line, Histogram & Scatter charts via Plotly + Seaborn
🤖 AI Q&A Chat	Ask anything about your data — powered by Groq LLaMA 3.3 70B
📊 Smart Statistics	Min, max, mean, std, quartiles, frequency distributions
🔒 Secure API Handling	Keys loaded automatically from .env / Streamlit Secrets — no manual input needed
🌙 Dark Glassmorphism UI	Premium dark-themed Streamlit interface

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🗂️ Project Structure

📦 AI Data Analysis Assistant/
├── 📄 app.py               → Streamlit web interface (main app)
├── 📄 main.py              → CLI pipeline (terminal mode)
├── 📄 analysis.py          → Data loading + statistics + Q&A logic
├── 📄 visualization.py     → Seaborn/Matplotlib chart generator
├── 📄 dataset.csv          → 50-row realistic retail dataset
├── 📄 requirements.txt     → Python dependencies
├── 📄 .env.example         → API key template (safe to commit)
├── 📄 .env                 → Your actual API key (NEVER commit this)
├── 📁 charts/              → Auto-generated PNG charts saved here
└── 📄 README.md            → You are here!

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🚀 Quick Start

1️⃣ Clone the Repository

git clone https://github.com/binarylaiba/AI-Data-Analysis-Asistant.git
cd AI-Data-Analysis-Asistant

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Configure Your Groq API Key

# Copy the example file
copy .env.example .env

Open .env and add your key:

GROQ_API_KEY=gsk_your_real_key_here

🔑 Get a free Groq API key at → https://console.groq.com/keys

Note: The app works without a key — AI chat is simply skipped gracefully.

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▶️ Running the App

🌐 Streamlit Web App (Recommended)

py -m streamlit run app.py

Opens at http://localhost:8501

What you get:

• 📌 Sidebar — Upload any CSV or use default dataset.csv
• 📊 Metric Cards — Total records, total sales, avg age, top category
• 🔍 Dataset Preview — First 10 rows + column types + missing values
• ❓ Competition Q&A — Styled answers to all 3 analysis questions
• 📉 Interactive Plotly Chart — Dark-themed, hover-enabled charts
• 🖼️ Chart Export — Saved PNG preview (Seaborn/Matplotlib)
• 💬 AI Chat — Ask anything about your data in real time

💻 CLI Pipeline (Terminal Mode)

py main.py dataset.csv

Steps it runs:

1. Loads & inspects the CSV
2. Prints full descriptive statistics
3. Answers the 3 competition questions
4. Generates charts/sales_by_category.png
5. Sends chart to Groq for a 2-sentence AI explanation
6. Launches interactive Q&A — type questions, get instant answers. Type exit to quit.

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📋 Dataset Overview

dataset.csv — 50 rows of realistic retail transactions:

Column	Type	Description
Order_ID	int	Unique order ID (1001–1050)
Product	string	Product name
Category	string	Electronics / Furniture / Clothing / Home & Kitchen
Sales	float	Revenue per order (USD)
Age	int	Customer age
City	string	US city

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🏆 Competition Q&A Results

#	Question	Answer
1	Which product has the highest total sales?	🥇 Laptop
2	What is the average customer age?	📅 37.14 years
3	Which product category appears most frequently?	🏷️ Electronics

Exact figures are computed at runtime directly from dataset.csv.

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📦 Dependencies

pandas
numpy
matplotlib
seaborn
groq
streamlit
python-dotenv
plotly

Install all at once:

pip install -r requirements.txt

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✅ Deliverables Checklist

• dataset.csv — 50-row retail dataset with required headers
• analysis.py — load_data, analyze_data, answer_questions
• visualization.py — generate_chart (Seaborn/Matplotlib PNG)
• main.py — CLI pipeline + interactive Q&A (Groq)
• app.py — Streamlit web interface with all required panels
• requirements.txt — all dependencies listed
• .env.example — placeholder API key (safe for version control)
• README.md — this file

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🔐 Security

• API key is loaded automatically from .env or Streamlit Cloud Secrets
• No manual API key input in the UI — clean & secure by design
• .env is listed in .gitignore — your key is never committed

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📝 Notes

• AI model used: llama-3.3-70b-versatile via Groq (with fallback models)
• All Python files are commented with clean section dividers
• Charts are auto-saved to charts/sales_by_category.png
• Streamlit app and CLI pipeline run independently of each other

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Made with ❤️ by Laiba | Powered by Groq + Streamlit