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app.py

@@ -3,12 +3,18 @@ from flask import Flask, request, jsonify
 import joblib
 import numpy as np
 import logging
+import sys
 
 # -----------------------
-# Setup logging
+# Setup logger (stdout captured by HF Spaces)
 # -----------------------
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+logger = logging.getLogger("capacity_logger")
+logger.setLevel(logging.INFO)
+handler = logging.StreamHandler(sys.stdout)  # important for HF Spaces
+handler.setLevel(logging.INFO)
+formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+handler.setFormatter(formatter)
+logger.addHandler(handler)
 
 # -----------------------
 # Initialize Flask app
@@ -19,13 +25,15 @@ app = Flask("Store Capacity Predictor")
 pipeline = joblib.load("catbooster_model_v1_0.joblib")
 
 # -----------------------
-# Routes
+# Home route
 # -----------------------
 @app.get('/')
 def home():
     return "Welcome to the Store Capacity Prediction API"
 
-# ----------------------- SINGLE PREDICTION -----------------------
+# -----------------------
+# Single prediction
+# -----------------------
 @app.post('/v1/predict')
 def predict_capacity():
     try:
@@ -38,22 +46,24 @@ def predict_capacity():
         # Predict
         prediction = pipeline.predict(input_data).tolist()[0]
 
-        # Sanitize and convert to integer
-        if pd.isna(prediction) or np.isinf(prediction) or not np.isfinite(prediction):
+        # Sanitize prediction
+        if not np.isfinite(prediction):
             logger.warning("Single prediction invalid (%s), replacing with 0", prediction)
             prediction = 0
         else:
-            prediction = int(np.clip(prediction, 0, 10000))  # Adjust max if needed
+            prediction = int(np.clip(prediction, 0, 10000))
 
         logger.info("Single prediction output: %s", prediction)
 
         return jsonify({'Predicted_Capacity': prediction})
 
     except Exception as e:
-        logger.error("Error in single prediction: %s", e)
+        logger.error("Error in single prediction: %s", e, exc_info=True)
         return jsonify({'error': str(e)}), 400
 
-# ----------------------- BATCH PREDICTION -----------------------
+# -----------------------
+# Batch prediction
+# -----------------------
 @app.post('/v1/predict_batch')
 def predict_capacity_batch():
     try:
@@ -69,7 +79,7 @@ def predict_capacity_batch():
         clean_predictions = []
 
         for idx, p in enumerate(predictions):
-            if pd.isna(p) or np.isinf(p) or not np.isfinite(p):
+            if not np.isfinite(p):
                 logger.warning("Row %d prediction invalid (%s), replacing with 0", idx, p)
                 clean_predictions.append(0)
             else:
@@ -86,7 +96,7 @@ def predict_capacity_batch():
         return output_df.to_html(index=False)
 
     except Exception as e:
-        logger.error("Error in batch prediction: %s", e)
+        logger.error("Error in batch prediction: %s", e, exc_info=True)
         return jsonify({"error": str(e)}), 400
 
 # -----------------------