# ramyahonnali/rala architecture

> Architecture diagram of the GitHub repository ramyahonnali/rala, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-25). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/ramyahonnali/rala
- Repository: https://github.com/ramyahonnali/rala

## Overview

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`](https://github.com/ramyahonnali/rala/blob/main/app.py) (Flask Application), [`static/script.js`](https://github.com/ramyahonnali/rala/blob/main/static/script.js) (Dashboard Controller), [`templates/index.html`](https://github.com/ramyahonnali/rala/blob/main/templates/index.html) (Dashboard View), [`model.py`](https://github.com/ramyahonnali/rala/blob/main/model.py) (Model Pipeline)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_interface["Manager Dashboard"]
  node_browser["Dashboard View<br/>[index.html]"]
  node_frontend["Dashboard Controller<br/>[script.js]"]
  node_styles["Dashboard Styling<br/>[style.css]"]
end

subgraph group_service["Forecasting Service"]
  node_server["Flask Application<br/>[app.py]"]
  node_model["Model Pipeline<br/>[model.py]"]
  node_forecast["Demand Inference<br/>[model.py]"]
  node_metrics["Waste Metrics<br/>[model.py]"]
end

subgraph group_analytics["Historical Analytics"]
  node_history[("Historical Dataset<br/>[dataset.csv]")]
  node_history_api["History API<br/>[app.py]"]
  node_analytics_ui["Analytics Charts<br/>[script.js]"]
end

node_manager(("Canteen Manager"))
node_chartjs["Chart.js"]

node_manager -->|"uses"| node_browser
node_browser -->|"styled by"| node_styles
node_browser -->|"runs"| node_frontend
node_frontend -->|"posts forecast"| node_server
node_server -->|"loads model"| node_model
node_server -->|"requests forecast"| node_forecast
node_forecast -->|"calculates metrics"| node_metrics
node_server -->|"returns forecast"| node_frontend
node_frontend -->|"posts chat"| node_server
node_server -->|"returns reply"| node_frontend
node_server -->|"serves history"| node_history_api
node_history_api -->|"reads records"| node_history
node_model -->|"trains from"| node_history
node_frontend -->|"fetches history"| node_history_api
node_frontend -->|"renders trends"| node_analytics_ui
node_analytics_ui -.->|"draws charts"| node_chartjs

click node_browser "https://github.com/ramyahonnali/rala/blob/main/templates/index.html"
click node_frontend "https://github.com/ramyahonnali/rala/blob/main/static/script.js"
click node_styles "https://github.com/ramyahonnali/rala/blob/main/static/style.css"
click node_server "https://github.com/ramyahonnali/rala/blob/main/app.py"
click node_model "https://github.com/ramyahonnali/rala/blob/main/model.py"
click node_forecast "https://github.com/ramyahonnali/rala/blob/main/model.py"
click node_metrics "https://github.com/ramyahonnali/rala/blob/main/model.py"
click node_history "https://github.com/ramyahonnali/rala/blob/main/dataset.csv"
click node_history_api "https://github.com/ramyahonnali/rala/blob/main/app.py"
click node_analytics_ui "https://github.com/ramyahonnali/rala/blob/main/static/script.js"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_browser,node_frontend,node_styles toneBlue
class node_server,node_model,node_forecast,node_metrics toneAmber
class node_history,node_history_api,node_analytics_ui toneMint
class node_manager,node_chartjs toneIndigo
```

## Components

### Manager Dashboard

- [Dashboard View](https://github.com/ramyahonnali/rala/blob/main/templates/index.html): `templates/index.html`
- [Dashboard Controller](https://github.com/ramyahonnali/rala/blob/main/static/script.js): `static/script.js`
- [Dashboard Styling](https://github.com/ramyahonnali/rala/blob/main/static/style.css): `static/style.css`

### Forecasting Service

- [Flask Application](https://github.com/ramyahonnali/rala/blob/main/app.py): `app.py`
- [Model Pipeline](https://github.com/ramyahonnali/rala/blob/main/model.py): `model.py`
- [Demand Inference](https://github.com/ramyahonnali/rala/blob/main/model.py): `model.py`
- [Waste Metrics](https://github.com/ramyahonnali/rala/blob/main/model.py): `model.py`

### Historical Analytics

- [Historical Dataset](https://github.com/ramyahonnali/rala/blob/main/dataset.csv): `dataset.csv`
- [History API](https://github.com/ramyahonnali/rala/blob/main/app.py): `app.py`
- [Analytics Charts](https://github.com/ramyahonnali/rala/blob/main/static/script.js): `static/script.js`

### Other components

- **Canteen Manager**
- **Chart.js**

## Connections

- 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