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CoffePrice is a full-stack SaaS platform built for Colombia’s coffee supply chain. Farmers (caficultores) get live FNC reference prices, ML-driven next-day predictions, and email alerts when prices hit their target. Buyers (compradores) publish their prices and appear on a regional map. Admins oversee users, news, and platform configuration.

Quick Start

Set up your local environment and run CoffePrice in minutes

Deployment

Deploy the backend on Railway and the frontend on Netlify

API Reference

Explore every REST endpoint — auth, prices, predictions, and more

ML Pipeline

Learn how the hybrid Prophet + XGBoost model generates daily predictions

What CoffePrice does

CoffePrice aggregates the data Colombian coffee producers need every day — the FNC internal price, the New York futures benchmark, the COP/USD exchange rate, and regional buyer prices — and packages it into a single platform with three distinct dashboards for producers, buyers, and administrators.

Live Prices

FNC price scraped Mon–Fri at 8 am and 1 pm (Bogotá time)

Predictions

Next-day FNC price forecast with confidence range and trend signal

Buyer Map

Interactive map of approved buyers across Huila municipalities

Price Alerts

Email notification when any buyer posts a price above your threshold

News Feed

Auto-aggregated coffee-sector news refreshed multiple times a day

Kaffi Chatbot

OpenAI-powered assistant that answers questions about the platform

Get started

1

Clone the repository

Clone JaiderT/CoffeePrice and install dependencies for both the backend and frontend directories.
2

Configure environment variables

Copy backend/.env.example to backend/.env and fill in MONGODB_URI, JWT_SECRET, and FRONTEND_URL at minimum.
3

Start the backend

Run npm start (or npm run dev for hot-reload) from the backend/ directory. The API listens on port 8081 by default.
4

Start the frontend

Set VITE_API_URL in frontend/.env, then run npm run dev from the frontend/ directory. Open http://localhost:5173.
The ML prediction pipeline (ml-service-experimental/) runs separately as a Python script. In production it executes automatically via a GitHub Actions workflow every weekday at 3:15 pm Colombia time.

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