Update app.py

app.py

@@ -1,53 +1,75 @@
-from flask import Flask, request, jsonify, send_from_directory
-from flask_cors import CORS
-import joblib, numpy as np, json, math, xgboost as xgb, logging
+from flask import Flask, request, jsonify
+import json
+import math
+import joblib
+import xgboost as xgb
 
-app = Flask(__name__, static_folder=".", static_url_path="")
-CORS(app)
-logging.basicConfig(level=logging.INFO)
+app = Flask(__name__)
 
-# Load models
+# Load ML models
 try:
-    rf = joblib.load("rf_model.pkl")
+    rf_model = joblib.load("rf_model.pkl")
     xgb_model = xgb.Booster()
     xgb_model.load_model("xgb_model.json")
-    app.logger.info("✅ Models loaded.")
+    print("✅ Models loaded")
 except Exception as e:
-    app.logger.error(f"❌ Model load error: {e}")
-    raise
+    print(f"❌ Model loading failed: {e}")
+    rf_model = None
+    xgb_model = None
 
-# Load tile data
+# Load product catalog
 with open("tile_catalog.json") as f:
     tile_catalog = json.load(f)
-with open("tile_sizes.json") as f:
-    tile_sizes = json.load(f)
 
-@app.route('/')
-def homepage():
-    return send_from_directory(".", "index.html")
+@app.route("/api/calculate", methods=["POST"])
+def calculate_tiles():
+    data = request.json
+    try:
+        length = float(data.get("length", 0))
+        width = float(data.get("width", 0))
+        tile_length = float(data.get("tile_length", 0))
+        tile_width = float(data.get("tile_width", 0))
 
-@app.route('/recommend', methods=['POST'])
+        if not all([length, width, tile_length, tile_width]):
+            return jsonify({"error": "All dimensions must be provided"}), 400
+
+        area = length * width
+        tile_area = tile_length * tile_width
+        tiles_needed = math.ceil((area / tile_area) * 1.1)  # 10% buffer
+        boxes_needed = math.ceil(tiles_needed / 10)  # Assume 10 tiles/box
+
+        return jsonify({
+            "area": area,
+            "tile_area": tile_area,
+            "tiles_needed": tiles_needed,
+            "boxes_needed": boxes_needed
+        })
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+@app.route("/recommend", methods=["POST"])
 def recommend():
-    data = request.get_json()
-    # Basic validation
-    for key in ('tile_type','length','width','area','price_range'):
-        if key not in data:
-            return jsonify({"error":f"Missing {key}"}),400
-
-    length = float(data['length']); width = float(data['width'])
-    coverage = length * width
-    # build features for ML
-    features = np.array([[0 if data['tile_type']=='floor' else 1, data['area'], coverage, *data['price_range'], data['price_range'][1]/coverage, coverage/data['price_range'][1]]])
-    xgb_pred = xgb_model.predict(xgb.DMatrix(features))[0]
-    rf_pred = rf.predict_proba(features)[0,1]
-    score = (xgb_pred+rf_pred)/2
-
-    # Filter products
-    recs = []
-    for p in tile_catalog:
-        if p['type']==data['tile_type'] and p['size']==f"{length}x{width}" and data['price_range'][0] <= p['price'] <= data['price_range'][1]:
-            recs.append({**p})
-    return jsonify({"recommendation_score": round(score,3), "recommended_products": recs})
-
-if __name__=="__main__":
-    app.run(host="0.0.0.0", port=7860, debug=False)
+    data = request.json
+    tile_type = data.get("tile_type", "").lower()
+    area = float(data.get("area_range", 0))
+
+    if not tile_catalog:
+        return jsonify({"result": "❌ No catalog available"})
+
+    suggestions = []
+    for product in tile_catalog:
+        if product["type"].lower() == tile_type:
+            if product.get("area_range_min", 0) <= area <= product.get("area_range_max", float("inf")):
+                suggestions.append({
+                    "name": product.get("name", "Tile Product"),
+                    "price": product.get("price", "₹0"),
+                    "link": product.get("link", "#")
+                })
+
+    if suggestions:
+        return jsonify({"result": "✅ Recommended", "suggestions": suggestions[:4]})
+    else:
+        return jsonify({"result": "❌ No matching products", "suggestions": []})
+
+if __name__ == "__main__":
+    app.run(debug=True)