pure-dashboard

bySmriti Gupta

You are a senior full-stack engineer, AI/ML engineer, and system architect. Your task is to build a COMPLETE, REAL, DEPLOYABLE application based on the following project: PROJECT NAME: SMARTREN – Smart Dashboard for Monitoring and Transitioning to Renewable Household Energy GOAL: Build a production-ready intelligent dashboard that: - Simulates household energy consumption (small, medium, large homes) - Performs analytics and forecasting - Provides AI-based recommendations for energy optimization - Suggests renewable energy solutions (e.g., solar panel capacity) - Displays everything in a modern interactive dashboard ----------------------------------- ⚙️ TECH STACK (STRICT) ----------------------------------- Backend: - Python (FastAPI preferred) - MySQL database - Pandas, NumPy - Scikit-learn (KMeans, regression) - Prophet or ARIMA (forecasting) Frontend: - React.js (with Vite or Next.js) - Tailwind CSS (modern premium UI) - Chart.js or Recharts (for graphs) Other: - REST API architecture - JWT Authentication (basic login system) - Docker support (optional but preferred) ----------------------------------- 📦 CORE FEATURES (MANDATORY) ----------------------------------- 1. USER & HOUSEHOLD SYSTEM - Create household profiles: - Small, Medium, Large - Store: - Number of appliances - Energy rating - Location (optional) - Tariff rate 2. DATA SIMULATION ENGINE - Generate realistic hourly energy usage data - Appliances: - AC, Fan, Lights, Fridge, Washing Machine, TV - Include: - Peak hours - Seasonal variation - Random noise for realism 3. DATABASE DESIGN - MySQL schema with tables: - users - households - appliances - energy_usage - recommendations - Provide full SQL schema 4. ANALYTICS ENGINE - Identify: - Peak consumption hours - High-energy appliances - K-Means clustering: - Low / Medium / High usage classification - Regression: - Consumption vs cost 5. FORECASTING MODULE - Use Prophet or ARIMA to: - Predict next 7–30 days energy usage - Return API endpoints for predictions 6. RECOMMENDATION ENGINE - Rule-based + ML hybrid: Examples: - "Run washing machine during off-peak hours" - "Reduce AC usage during peak load" - Suggest solar panel capacity based on usage - Output: - Cost savings - Carbon reduction estimate 7. DASHBOARD (IMPORTANT – PREMIUM UI) - Pages: - Overview - Analytics - Forecast - Recommendations - Charts: - Daily / Weekly / Monthly usage - Appliance-wise consumption - Cost trends - Forecast graph - UI must be: - Clean - Modern - Responsive - Premium looking 8. API DESIGN Provide full FastAPI routes: - /generate-data - /get-usage - /analytics - /forecast - /recommendations 9. EXPORT FEATURE - Download reports: - CSV / PDF 10. DEPLOYMENT - Step-by-step instructions: - Run backend (FastAPI) - Run frontend (React) - Connect MySQL - Include: - requirements.txt - package.json - environment variables ----------------------------------- 📁 OUTPUT FORMAT (VERY IMPORTANT) ----------------------------------- Give output in THIS ORDER: 1. System Architecture Diagram (text explanation) 2. Database Schema (SQL) 3. Backend Code (FastAPI complete) 4. ML Models Code 5. Data Simulation Script 6. Frontend Code (React with charts) 7. API Integration (frontend ↔ backend) 8. Deployment Guide (VS Code step-by-step) 9. Folder Structure (clean professional) ----------------------------------- 🚨 RULES (STRICT) ----------------------------------- - Do NOT give partial code - Do NOT skip modules - Code must run without errors - Use realistic dummy data - UI must look like a startup product - Keep code modular and clean - Comment important logic ----------------------------------- 🎯 END GOAL ----------------------------------- The final result should be a: 👉 Fully working Smart Energy Dashboard 👉 That I can run locally in VS Code 👉 And present as a FINAL YEAR BTECH PROJECT