🌟 PlotSense: AI-Powered Data Visualization Assistant

📌 Overview

PlotSense is an AI-powered assistant that helps data professionals and analysts make smarter, faster, and more explainable data visualizations. Whether you're exploring a new dataset or building dashboards, PlotSense simplifies the process with:

• ✅ Smart Visualization Suggestions - Recommends the best plots based on your data structure and relationships.
• 🧠 Natural Language Explanations – Automatically explains charts in plain English.
• 🔗 Seamless Integration – Works out of the box with pandas, matplotlib, and seaborn.

Let AI supercharge your EDA (Exploratory Data Analysis).

⚡ Quickstart

🔧 Install the package

pip install plotsense

🧠 Import PlotSense:

import plotsense as ps
from plotsense import recommender, plotgen, explainer

🔐 Authenticate with Groq API:

Get your free API key from Groq Cloud https://console.groq.com/home

import os
# Set GROQ_API_KEY environment variable
os.environ['GROQ_API_KEY'] = 'your-api-key-here'

#or

# Set API key (one-time setup)
ps.set_api_key("your-api-key-here")

🚀 Core Features

🎯 1. AI-Recommended Visualizations

Let PlotSense analyze your data and suggest optimal charts.

import pandas as pd
# Load your dataset (e.g., pandas DataFrame)
df = pd.read_csv("data.csv")

# Get AI-recommended visualizations
suggestions = ps.recommender(df) # default number of suggestions is 5
print(suggestions)

📊 Sample Output:

🎛️ Want more suggestions?

suggestions = ps.recommender(df, n=10)  

📈 2. One-Click Plot Generation

Generate recommended charts instantly:

plot1 = ps.plotgen(df, suggestions.iloc[0]) # This will plot a bar chart with variables 'survived', 'pclass'
plot2 = ps.plotgen(df, suggestions.iloc[1]) # This will plot a bar chart with variables 'survived', 'sex'
plot3 = ps.plotgen(df, suggestions.iloc[2]) # This will plot a histogram with variable 'age'

🎛️ Want more control?

plot1 = ps.plotgen(df, suggestions.iloc[0], x='pclass', y='survived') 

Supported Plots

• scatter
• bar
• barh
• histogram
• boxplot
• violinplot
• pie
• hexbin

🧾 3. AI-Powered Plot Explanation

Turn your visualizations into stories with natural language insights:

explanation = ps.explainer(plot1)

print(explanation)

⚙️ Advanced Options

• Custom Prompts: You can provide your own prompt to guide the explanation

explanation = explainer(
    fig,
    prompt="Explain the key trends in this sales data visualization"
)

• Multiple Refinement Iterations: Increase the number of refinement cycles for more polished explanations:

explanation = explainer(fig, max_iterations=3)  # Default is 2

🔄 Combined Workflow: Suggest → Plot → Explain

suggestions = ps.recommender(df)
plot = ps.plotgen(df, suggestions.iloc[0])
insight = ps.explainer(plot)

🤝 Contributing

We welcome contributions!

Branching Strategy

• main → The stable production-ready version of PlotSense.
• dev → Active development
• feature/ → Branches for specific features (e.g., feature/ai-visualization-suggestions).

💡 How to Help

• 🐞 Bug Reports → GitHub Issues
• 💡 Suggest features → Open a discussion
• 🚀 Submit PRs → Fork → Branch → Test → Pull Request

📅 Roadmap

• More model integrations
• Automated insight highlighting
• Jupyter widget support
• Features/target analysis
• More supported plots

📥 Install or Update

pip install --upgrade plotsense  # Get the latest features!

🛡 License

Apache License 2.0

🔐 API & Privacy Notes

• Your API key is securely held in memory for your current Python session.
• All requests are processed via Groq's API servers—no data is stored locally by PlotSense.
• Requires an internet connection for model-backed features.

Let your data speak—with clarity, power, and PlotSense. 📊✨

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