DataLens AI 🔍📊

DataLens AI is a premium, state-of-the-art data cleaning, insight generation, and executive PDF reporting suite. Powered by the Google Gemini API, it enables users to drop raw files (CSV, Excel), clean anomalies in one-click, auto-generate analytical insights, render responsive charts, and download beautiful multi-page PDF executive summaries.

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✨ Key Features

• 🪄 One-Click Auto-Pilot: Automates null-value dropping, duplicate elimination, statistical calculation, and AI insight generation instantly.
• 📈 Advanced Aggregated Visualization: Smart data handling prevents cluttered raw-row plots. Automatically groups categorical fields (e.g. Genre, Subscription Type) by mean metrics and renders numeric-to-numeric variables (e.g. Rating vs. Watch Time) as beautiful Scatter Plots.
• 📄 Pixel-Perfect PDF Export: Powered by a custom html2canvas and jsPDF pipeline.
  • Auto-Avoiding Breaks: Visualizations, titles, and legends are guaranteed to stay together without breaking mid-page.
  • Dynamic Layout Budgeting: Automatically scales down/compresses margins and chart heights (360px to 220px) to fit charts on a single page before forcing page breaks.
  • Dynamic Pagination: Auto-generates "Page X of Y" footers and aligns interactive Table of Contents anchors dynamically.
  • Anti-Overlap X-Axis: Dense tick labels automatically tilt (-30°) and adjust spacing dynamically.
• 💎 Premium Dark Theme: A gorgeous, glassmorphism-based UI featuring Outfit/Inter typography, harmonious Indigo/Cyan gradients, and fluid micro-animations.
• 📂 High Performance Parsing: Utilizes PapaParse and xlsx chunk-based loading with real-time UI progress/ETA indicators for files up to 500,000 rows.

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🚀 Run Locally

Prerequisites

• Node.js (v18+)
• npm (v9+)

Setup Guide

1. Clone the repository:

   git clone https://github.com/MD-NAVED/DataLens-AI.git
   cd DataLens-AI

2. Install dependencies:

   npm install

3. Configure the Environment: Create a .env.local or .env file in the root directory and append your API credential:

   VITE_GEMINI_API_KEY=your_gemini_api_key_here

4. Launch Development Server:

   npm run dev

   Open http://localhost:3000 in your browser.

5. Production Build: Validate production correctness or build the bundle using:

   npm run build

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🛠️ Technology Stack

• Frontend Core: React 19, TypeScript, Vite
• Styling & UI: TailwindCSS v4, Lucide React, Framer Motion, HSL tailored variables
• Data Engineering: PapaParse (CSV chunk parser), XLSX (Excel parser)
• Charts Engine: Recharts (fully customized responsive Tooltips, Scatters, and Bars)
• PDF Engine: jsPDF, html2canvas (configured with high-DPI scaling and dynamic layout checkers)
• AI Core: @google/genai (Google Gemini SDK)

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🌐 Enterprise Production Architecture (GCP)

DataLens AI is designed to scale from local CSV file analysis to an enterprise-grade cloud analytics platform. Below is the blueprint of the serverless topology designed to deploy this platform on Google Cloud Platform (GCP).

graph TD
    User([User Browser]) -->|HTTPS| LB[Global HTTPS Load Balancer]
    subgraph GCP_Cloud ["Google Cloud Platform"]
        LB -->|Serverless NEG| CR[Cloud Run: DataLens App]
        CR -->|Secrets Auth| SM[Secret Manager]
        CR -->|SQL Proxy Connection| SQL[(Cloud SQL: PostgreSQL 15)]
        CR -->|Analytics Data Streaming| BQ[(BigQuery: Dataset)]
        CR -->|Advanced LLMs & Agents| Vertex[Vertex AI platform]
        SQL -.->|Retrieve DB Password| SM
    end

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⚡ Architecture Breakdown

• Cloud Run (Serverless Compute): Hosts the Dockerized DataLens AI application, auto-scaling dynamically from 0 to 1000+ instances.
• Secret Manager: Securely stores database passwords, API credentials, and JWT secrets, keeping them out of code repositories.
• Cloud SQL (PostgreSQL 15): Managed relational database instance storing persistent client metrics and user records.
• BigQuery: Large-scale dataset analytics warehouse, allowing streaming queries of high-volume data.
• Vertex AI Platform: Direct integration to advanced LLMs (Gemini Pro/Ultra) via secure, private interior GCP networking.
• Infrastructure as Code (IaC): Deploy and manage this entire infrastructure using Terraform blueprint configurations.