Make OpenAI primary recommender with rule fallback

app/smart_recommendation.py

@@ -55,23 +55,24 @@ class SmartBudgetRecommender:
         recommendations = []
         for category, data in category_data.items():
             avg_expense = data["average_monthly"]
-            recommended_budget = self._calculate_recommended_budget(avg_expense, data)
             confidence = self._calculate_confidence(data)
-            
-            reason = self._generate_reason(category, avg_expense, recommended_budget)
-            
-            ai_result = self._get_ai_recommendation(category, data, avg_expense, recommended_budget)
+
+            # 1) Try OpenAI first (primary source of recommendation)
+            ai_result = self._get_ai_recommendation(category, data, avg_expense)
             if ai_result:
-                recommended_budget = ai_result.get("recommended_budget", recommended_budget)
-                reason = ai_result.get("reason", reason)
+                recommended_budget = ai_result.get("recommended_budget")
+                reason = ai_result.get("reason")
                 action = ai_result.get("action")
             else:
+                # 2) Fallback to rule-based recommendation
+                recommended_budget = self._calculate_recommended_budget(avg_expense, data)
+                reason = self._generate_reason(category, avg_expense, recommended_budget)
                 action = None
 
             recommendations.append(BudgetRecommendation(
                 category=category,
                 average_expense=round(avg_expense, 2),
-                recommended_budget=round(recommended_budget, 2),
+                recommended_budget=round(recommended_budget or 0, 2),
                 reason=reason,
                 confidence=confidence,
                 action=action
@@ -234,7 +235,7 @@ class SmartBudgetRecommender:
         result.sort(key=lambda x: x.average_monthly_expense, reverse=True)
         return result
 
-    def _get_ai_recommendation(self, category: str, data: Dict, avg_expense: float, fallback_budget: float):
+    def _get_ai_recommendation(self, category: str, data: Dict, avg_expense: float):
         """Use OpenAI to refine the budget recommendation."""
         if not OPENAI_API_KEY:
             return None
@@ -246,7 +247,6 @@ class SmartBudgetRecommender:
             f"Average spend: {avg_expense:.2f}\n"
             f"Std deviation: {data['std_dev']:.2f}\n"
             f"Months observed: {data['months_analyzed']}\n"
-            f"Current suggested budget: {fallback_budget:.2f}\n"
         )
 
         prompt = (

test_hf_api.py

@@ -8,7 +8,7 @@ import json
 from datetime import datetime, timedelta
 
 BASE_URL = "https://logicgoinfotechspaces-smart-budget-recommendation.hf.space"
-USER_ID = "68a834c3f4694b11efedacd2"
+USER_ID = "688c80ca990b63f0e945ecd9"
 
 def test_health():
     """Test health endpoint"""