Update app.py

src/app.py

@@ -1,34 +1,55 @@
+import logging
 from flask import Flask, request, jsonify
 from pyspark.sql import SparkSession
 from pyspark.sql.functions import col, avg, count
 
+# Initialize Flask app
 app = Flask(__name__)
 
 # Initialize SparkSession
 spark = SparkSession.builder.appName("microservice").getOrCreate()
 
-# Reduce log verbosity
+# Reduce Spark log verbosity
 spark.sparkContext.setLogLevel("WARN")
 
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+    handlers=[
+        logging.StreamHandler()  # Log to console
+    ]
+)
+logger = logging.getLogger(__name__)
+
 @app.route("/process", methods=["POST"])
 def process_data():
     """
     Receive JSON data stream and return analysis results
     """
     try:
+        logger.info("Received request to /process")
         data = request.get_json()
-        # Create a temporary DataFrame
+        logger.info(f"Request data: {data}")
+
+        # Create temporary DataFrame
         df = spark.createDataFrame(data)
+
         # Perform simple data analysis
         result = df.groupBy("gender").agg(
             avg("salary").alias("average_salary"),
             count("*").alias("count")
         ).collect()
+
         # Convert results to JSON format
         result_json = [{"gender": row["gender"], "average_salary": row["average_salary"], "count": row["count"]} for row in result]
+        logger.info(f"Analysis results: {result_json}")
+
         return jsonify(result_json), 200
     except Exception as e:
+        logger.error(f"Error during processing: {str(e)}", exc_info=True)
         return jsonify({"error": str(e)}), 500
 
 if __name__ == "__main__":
+    logger.info("Starting Flask app on port 5000")
     app.run(host="0.0.0.0", port=5000)