Fertilizer price intelligence for farmers — powered by natural gas market data.

# Install Python dependencies (use the venv, not system Python)
.\.venv\Scripts\pip install -r requirements.txt

Step 1 — Train the ML models (run once):

.\.venv\Scripts\python backend/train_models.py

Only needs to be re-run if you change training data or model code.

Step 2 — Ingest data & generate forecasts (run any time you want fresh data):

.\.venv\Scripts\python backend/run_pipeline.py

Open http://localhost:8000 in your browser.

inside the venv, call streamlit run login.py

The easiest way to run the app — no Python setup required.

Prerequisites: Docker Desktop installed and running.

# 1. Copy the env file and add your API key
cp .env.example .env
# Edit .env and set EIA_API_KEY=your_key_here

# 2. Build and start
docker-compose up --build

# 3. Open http://localhost:8501

User accounts persist across restarts via Docker volumes. To stop: docker-compose down.

• Always use .\.venv\Scripts\python instead of python — the venv contains all required packages (xgboost, pandas, etc.)
• Re-run run_pipeline.py to refresh forecasts without retraining
• The server reads data/processed/cache.json on every request — no restart needed after running the pipeline