How ramyahonnali/rala works
This is a Flask-based canteen meal-demand forecasting and food-waste planning application. Managers enter operating conditions, receive a demand forecast and preparation recommendation with waste-risk details, and can ask a context-aware chatbot about the latest forecast. The dashboard also presents historical analytics and one-click scenarios. The graph reflects the README’s stated capabilities; source excerpts are partial, so uncertain internal routing and omitted implementation details are not shown.
Main entry points: app.py Flask Application · script.js Dashboard Controller · index.html Dashboard View · model.py Model Pipeline
Components 12
Manager Dashboard
- Dashboard View
templates/index.html - Dashboard Controller
static/script.js - Dashboard Styling
static/style.css
Forecasting Service
- Flask Application
app.py - Model Pipeline
model.py - Demand Inference
model.py - Waste Metrics
model.py
Historical Analytics
- Historical Dataset
dataset.csv - History API
app.py - Analytics Charts
static/script.js
Other components
- Canteen Manager
- Chart.js
Connections 16
- Canteen Manager → Dashboard View: uses
- Dashboard View → Dashboard Styling: styled by
- Dashboard View → Dashboard Controller: runs
- Dashboard Controller → Flask Application: posts forecast
- Flask Application → Model Pipeline: loads model
- Flask Application → Demand Inference: requests forecast
- Demand Inference → Waste Metrics: calculates metrics
- Flask Application → Dashboard Controller: returns forecast
- Dashboard Controller → Flask Application: posts chat
- Flask Application → Dashboard Controller: returns reply
- Flask Application → History API: serves history
- History API → Historical Dataset: reads records
- Model Pipeline → Historical Dataset: trains from
- Dashboard Controller → History API: fetches history
- Dashboard Controller → Analytics Charts: renders trends
- Analytics Charts → Chart.js: draws charts