Containerize fraud detection consumer - full Docker integration

- Added consumer/Dockerfile with all dependencies
- Updated consumer pipeline.py to use environment variables
- Updated features.py and decision.py to support env var configuration
- Updated docker-compose.yml to include consumer service
- Consumer now runs automatically with 'docker compose up'
- Proper Kafka and Redis networking for Docker
- Environment variables for ML_MODELS_DIR, KAFKA_BROKER, REDIS_HOST/PORT

Now entire PayGuard system runs with: docker compose up -d

Co-authored-by: Copilot <[email protected]>

consumer/pipeline.py

@@ -14,6 +14,7 @@ Each step is independently verifiable, making it suitable for academic demonstra
 
 import json
 import logging
+import os
 from kafka import KafkaConsumer
 from kafka.errors import KafkaError
 
@@ -25,10 +26,19 @@ from decision import DecisionEngine
 # CONFIGURATION
 # ══════════════════════════════════════════════════════════════════════════════
 
-KAFKA_BROKER = "localhost:9092"
+KAFKA_BROKER = os.getenv("KAFKA_BROKER", "localhost:9092")
 KAFKA_TOPIC = "transactions"
 KAFKA_GROUP = "fraud-detection-consumer-group"
 
+# Redis configuration
+REDIS_HOST = os.getenv("REDIS_HOST", "localhost")
+REDIS_PORT = int(os.getenv("REDIS_PORT", "6379"))
+
+# ML Models configuration
+ML_MODELS_DIR = os.getenv("ML_MODELS_DIR", "models")
+LAYER1_PATH = os.path.join(ML_MODELS_DIR, "layer1.pkl")
+LAYER2_PATH = os.path.join(ML_MODELS_DIR, "layer2.pkl")
+
 # Logging setup
 logging.basicConfig(
     level=logging.INFO,
@@ -49,20 +59,20 @@ def initialize_pipeline():
     
     # Feature extraction
     feature_extractor = FeatureExtractor(
-        redis_host="localhost",
-        redis_port=6379
+        redis_host=REDIS_HOST,
+        redis_port=REDIS_PORT
     )
     
     # Model scoring
     model_scorer = ModelScorer(
-        layer1_path="models/layer1.pkl",
-        layer2_path="models/layer2.pkl"
+        layer1_path=LAYER1_PATH,
+        layer2_path=LAYER2_PATH
     )
     
     # Decision engine with circuit breaker
     decision_engine = DecisionEngine(
-        redis_host="localhost",
-        redis_port=6379
+        redis_host=REDIS_HOST,
+        redis_port=REDIS_PORT
     )
     
     # Kafka consumer