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app.py

@@ -1,17 +1,20 @@
 
-%%bash
 cat > app.py <<'PY'
-import os, logging, joblib, numpy as np, pandas as pd
+import os
+import logging
+import joblib
+import numpy as np
+import pandas as pd
 from flask import Flask, request, jsonify
 
-# Attempt optional CORS
+# optional CORS
 try:
     from flask_cors import CORS
     _CORS = True
 except Exception:
     _CORS = False
 
-# WSGI callable expected by gunicorn must be named `app`
+# WSGI callable must be named `app`
 app = Flask(__name__)
 if _CORS:
     CORS(app)
@@ -33,14 +36,16 @@ else:
     load_error = f"Model file not found at {MODEL_PATH}"
     logger.warning(load_error)
 
+
 @app.route("/", methods=["GET"])
 def home():
-    return jsonify({"message":"API up"}), 200
+    return jsonify({"message": "API up"}), 200
+
 
 @app.route("/v1/sales", methods=["POST"])
 def predict_single():
     if model is None:
-        return jsonify({"error":"Model not loaded","details": load_error}), 500
+        return jsonify({"error": "Model not loaded", "details": load_error}), 500
     try:
         data = request.get_json(force=True)
         expected_cols = [
@@ -53,15 +58,16 @@ def predict_single():
         pred = model.predict(df)
         log_out = bool(data.get("log_output", False))
         val = float(np.exp(pred[0])) if log_out else float(pred[0])
-        return jsonify({"predicted_sales": round(val,2)}), 200
+        return jsonify({"predicted_sales": round(val, 2)}), 200
     except Exception as e:
         logger.exception("predict_single failed")
-        return jsonify({"error":"prediction failed","details": str(e)}), 500
+        return jsonify({"error": "prediction failed", "details": str(e)}), 500
+
 
 @app.route("/v1/sales/batch", methods=["POST"])
 def predict_batch():
     if model is None:
-        return jsonify({"error":"Model not loaded","details": load_error}), 500
+        return jsonify({"error": "Model not loaded", "details": load_error}), 500
     try:
         if "file" in request.files:
             df = pd.read_csv(request.files["file"])
@@ -72,27 +78,33 @@ def predict_batch():
             elif isinstance(jb, list):
                 df = pd.DataFrame(jb)
             else:
-                return jsonify({"error":"No file or JSON data provided"}), 400
+                return jsonify({"error": "No file or JSON data provided"}), 400
+
         if df.empty:
-            return jsonify({"error":"Input empty"}), 400
+            return jsonify({"error": "Input empty"}), 400
+
         preds = model.predict(df).tolist()
-        log_flag = request.args.get("log_output","false").lower() == "true"
+        log_flag = request.args.get("log_output", "false").lower() == "true"
         if log_flag:
-            preds = [round(float(np.exp(p)),2) for p in preds]
+            preds = [round(float(np.exp(p)), 2) for p in preds]
         else:
-            preds = [round(float(p),2) for p in preds]
-        id_col = next((c for c in ("id","ID","Product_Id") if c in df.columns), None)
+            preds = [round(float(p), 2) for p in preds]
+
+        id_col = next((c for c in ("id", "ID", "Product_Id") if c in df.columns), None)
         if id_col:
             keys = df[id_col].astype(str).tolist()
             out = dict(zip(keys, preds))
         else:
             out = {str(i): preds[i] for i in range(len(preds))}
+
         return jsonify({"predictions": out}), 200
     except Exception as e:
         logger.exception("predict_batch failed")
-        return jsonify({"error":"batch prediction failed","details": str(e)}), 500
+        return jsonify({"error": "batch prediction failed", "details": str(e)}), 500
+
 
 if __name__ == "__main__":
     port = int(os.environ.get("PORT", 7860))
+    logger.info("Starting local Flask server on port %s", port)
     app.run(host="0.0.0.0", port=port, debug=True)
 PY