# ==============================================================================
# Olist Agentic BI — Makefile (Kaggle Dataset Version)
# ==============================================================================

.PHONY: help setup setup-data start stop clean etl analytics test all

help: ## Show this help
	@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-20s\033[0m %s\n", $$1, $$2}'

# ============================================================
# SETUP
# ============================================================

setup: ## Install Python dependencies
	pip install -r requirements.txt

setup-data: ## Download Olist dataset from Kaggle and extract
	@echo "Downloading dataset from Kaggle..."
	@mkdir -p data/raw
	kaggle datasets download -d olistbr/brazilian-ecommerce -p data/
	@echo "Extracting CSV files to data/raw/..."
	unzip -o data/brazilian-ecommerce.zip -d data/raw/ || python3 -c "import zipfile; z=zipfile.ZipFile('data/brazilian-ecommerce.zip'); z.extractall('data/raw/')"
	@echo "Dataset ready in data/raw/"

# ============================================================
# INFRASTRUCTURE
# ============================================================

start: ## Start all Docker services
	docker-compose up -d
	@echo "Waiting for services to initialize..."
	@sleep 15
	@echo "Services started. Kafka UI: http://localhost:8080"

stop: ## Stop all Docker services
	docker-compose down

restart: ## Restart all services
	docker-compose down && docker-compose up -d

logs: ## View Docker logs
	docker-compose logs -f --tail=50

status: ## Check service status
	docker-compose ps

# ============================================================
# ETL PIPELINE
# ============================================================

etl: etl-bronze etl-gold ## Run full ETL pipeline

etl-bronze: ## Bronze → Silver (requires data/raw/ CSV files)
	python transforms/bronze_to_silver.py --data-dir ./data/raw --output-dir ./data/silver

etl-gold: ## Silver → Gold
	python transforms/silver_to_gold.py --silver-dir ./data/silver --output-dir ./data/gold

etl-preprocess: ## Run data preprocessing
	python analytics/data_preprocessing.py --data-dir ./data/raw --output-dir ./data/processed

# ============================================================
# ANALYTICS & ML
# ============================================================

analytics: ## Run all analytics (requires data/raw/ CSV files)
	python analytics/association_rules.py --data-dir ./data/raw --output-dir ./data/analytics
	python analytics/customer_segmentation.py --data-dir ./data/raw --output-dir ./data/analytics
	python analytics/satisfaction_model.py --data-dir ./data/raw --output-dir ./data/analytics

association: ## Run Association Rules Mining
	python analytics/association_rules.py --data-dir ./data/raw --output-dir ./data/analytics

segmentation: ## Run Customer Segmentation (K-Means + DBSCAN)
	python analytics/customer_segmentation.py --data-dir ./data/raw --output-dir ./data/analytics

ml-model: ## Run Satisfaction Prediction Models
	python analytics/satisfaction_model.py --data-dir ./data/raw --output-dir ./data/analytics

# ============================================================
# STREAMING
# ============================================================

stream: ## Start streaming simulator with Kaggle data
	python streaming_simulator/simulator.py --data-dir ./data/raw --speed 1000

stream-fast: ## Fast streaming (10000x, test mode)
	python streaming_simulator/simulator.py --data-dir ./data/raw --speed 10000 --max-events 5000

stream-test: ## Test data loading without Kafka (no Kafka required)
	python -c "from streaming_simulator.simulator import OlistStreamSimulator; \
		sim = OlistStreamSimulator(data_dir='./data/raw', speed_factor=1000); \
		sim.load_data(); \
		print(f'Events loaded: {len(sim.events):,}'); \
		from collections import Counter; \
		c = Counter(e.event_type for e in sim.events); \
		[print(f'  {k}: {v:,}') for k, v in sorted(c.items())]"

# ============================================================
# AGENTIC BI
# ============================================================

agent: ## Start Agentic BI interactive session
	python agentic_bi/orchestrator.py

app: ## Start Streamlit frontend
	streamlit run frontend/streamlit_app.py --server.port 8501

# ============================================================
# TESTING & QUALITY
# ============================================================

test: ## Run tests
	python -m pytest tests/ -v

quality: ## Run data quality checks
	python -c "from governance.data_governance import demo; demo()"

# ============================================================
# CLEANUP
# ============================================================

clean: ## Remove processed data (keep raw)
	rm -rf data/silver data/gold data/analytics data/processed
	@echo "Cleaned processed data. Raw data preserved in data/raw/"

clean-all: clean ## Remove everything including Docker volumes
	docker-compose down -v
	@echo "All data and volumes removed."

# ============================================================
# FULL PIPELINE
# ============================================================

all: setup-data etl analytics ## Run complete pipeline
	@echo "============================================"
	@echo "  PIPELINE COMPLETE!"
	@echo "  Gold tables: data/gold/"
	@echo "  Analytics: data/analytics/"
	@echo "============================================"