Update tile_api.py

tile_api.py

@@ -1,39 +1,39 @@
-from flask import Flask, request, jsonify
-import joblib
-import numpy as np
-
-app = Flask(__name__)
-
-# Load models (make sure these files exist)
-xgb = joblib.load("xgb_model.pkl")
-rf = joblib.load("rf_model.pkl")
-
-@app.route("/recommend", methods=["POST"])
-def recommend():
-    data = request.get_json()
-
-    # Extract input features
-    length = float(data["length"])
-    width = float(data["width"])
-    price = float(data["price"])
-    coverage = float(data["coverage"])
-    area_range = float(data["area_range"])
-    tile_type = data["tile_type"].lower()
-
-    # Feature engineering
-    tile_type_num = 0 if tile_type == "floor" else 1
-    tile_area = length * width
-    price_per_sqft = price / coverage
-    budget_eff = coverage / price
-
-    features = np.array([[tile_type_num, length, width, price, coverage,
-                          area_range, tile_area, price_per_sqft, budget_eff]])
-
-    # Predict using both models and average
-    prob = (xgb.predict_proba(features)[0][1] + rf.predict_proba(features)[0][1]) / 2
-    result = "✅ Recommended" if prob >= 0.5 else "❌ Not Recommended"
-
-    return jsonify({"result": result, "score": round(float(prob), 3)})
-
-if __name__ == "__main__":
-    app.run(debug=True)
+from flask import Flask, request, jsonify
+import joblib
+import numpy as np
+
+app = Flask(__name__)
+
+# Load models (make sure these files exist)
+xgb = joblib.load("xgb_model.json")
+rf = joblib.load("rf_model.pkl")
+
+@app.route("/recommend", methods=["POST"])
+def recommend():
+    data = request.get_json()
+
+    # Extract input features
+    length = float(data["length"])
+    width = float(data["width"])
+    price = float(data["price"])
+    coverage = float(data["coverage"])
+    area_range = float(data["area_range"])
+    tile_type = data["tile_type"].lower()
+
+    # Feature engineering
+    tile_type_num = 0 if tile_type == "floor" else 1
+    tile_area = length * width
+    price_per_sqft = price / coverage
+    budget_eff = coverage / price
+
+    features = np.array([[tile_type_num, length, width, price, coverage,
+                          area_range, tile_area, price_per_sqft, budget_eff]])
+
+    # Predict using both models and average
+    prob = (xgb.predict_proba(features)[0][1] + rf.predict_proba(features)[0][1]) / 2
+    result = "✅ Recommended" if prob >= 0.5 else "❌ Not Recommended"
+
+    return jsonify({"result": result, "score": round(float(prob), 3)})
+
+if __name__ == "__main__":
+    app.run(debug=True)