💠 Visio AI Enterprise Platform

The efficient, local-first alternative to cloud AI.
Clean, Visualize, and Model your data without writing a single line of code.

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📖 Overview

Visio AI is an enterprise-grade Data Science platform designed to democratize Machine Learning. Unlike heavy, resource-intensive Cloud AI solutions, Visio AI runs highly efficient algorithms (XGBoost, Random Forest, etc.) directly on your local hardware.

It serves as a "Command Center" for your data, handling the full pipeline:

1. Ingestion & Wrangling (Cleaning dirty data)
2. Exploratory Data Analysis (Interpreting patterns)
3. Predictive Modeling (Forecasting future trends)
4. Computer Vision (Analyzing images)

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✨ Key Features

🏗️ Data Engineering

• Universal Loader: Support for CSV, Excel (.xlsx), and Text files.
• Smart Wrangling:
  • Auto-detect and fix missing values (Imputation).
  • One-click "Remove Commas" features for financial datasets.
  • Type correction (String $\to$ Number).

📊 Advanced Visualization

• Dual Mode Graphics: Switch between Interactive (Plotly) for exploration and Static (Seaborn) for publication.
• Smart Suggestions: The system automatically recommends the right chart (e.g., Heatmap vs Scatter) based on your variables.

🧠 Machine Learning Engine

• Supervised Learning: Training interface for Regression and Classification.
  • Algorithms: XGBoost, Random Forest, JVM, Linear Models, Decision Trees.
• Unsupervised Learning: K-Means Clustering (with 3D Viz) and PCA Dimensionality Reduction.
• AutoML: Automatically trains 6+ models and ranks them on a leaderboard.

👁️ Image Intelligence

• Multimodal Analysis: Integrated with Nvidia Nemotron-12B for analyzing images.
• Tasks: OCR, Scene Description, Defect Detection.

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🚀 Installation & Usage

Prerequisites

• Python 3.8 or higher
• pip

Setup

1. Clone the Repository

   git clone https://github.com/StartUp-Jaiho/Visio-AI.git
   cd Visio-AI

2. Create a Virtual Environment (Optional but Recommended)

   python -m venv venv
   # Windows
   venv\Scripts\activate
   # Mac/Linux
   source venv/bin/activate

3. Install Dependencies

   pip install -r requirements.txt

Running the App

streamlit run app.py

The application will launch automatically in your web browser.

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📂 Project Structure

Visio-AI/
├── Home.py                 # Application Entry Point
├── Docs.html               # Deep Dive Documentation (HTML)
├── Guide.md                # User Guide (Markdown)
├── utils.py                # Shared Utility Functions
├── styles.css              # Enterprise CSS Theme
├── pages/                  # Application Modules
│   ├── 1_Data_Loader.py    # Ingestion & Cleaning
│   ├── 2_EDA.py            # Visualization Engine
│   ├── 3_Supervised.py     # ML Training & Prediction
│   ├── 4_Unsupervised.py   # Clustering & PCA
│   ├── 5_Image_AI.py       # Computer Vision
│   ├── 6_AutoML.py         # Automated Modeling
│   ├── 7_Report.py         # PDF Reporting
│   └── ... (Utilities)
└── assets/                 # Static Assets (Images, Icons)

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🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for details on how to submit a Pull Request.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Built with ❤️ by Arshvir