Enhance OpenAI prompt to analyze actual spending data (trends, variability) for intelligent recommendations

app/smart_recommendation.py

@@ -1045,12 +1045,47 @@ class SmartBudgetRecommender:
         else:
             history = ", ".join(f"{value:.0f}" for value in data["monthly_values"])
         
+        # Build comprehensive data summary for OpenAI to analyze
+        monthly_values = data.get("monthly_values", [])
+        
+        # Calculate trend analysis
+        trend_analysis = ""
+        if len(monthly_values) > 1:
+            first_half = monthly_values[:len(monthly_values)//2]
+            second_half = monthly_values[len(monthly_values)//2:]
+            first_avg = sum(first_half) / len(first_half) if first_half else avg_expense
+            second_avg = sum(second_half) / len(second_half) if second_half else avg_expense
+            
+            if second_avg > first_avg * 1.05:
+                trend_analysis = f"UPWARD TREND: Early period average ({first_avg:,.0f}) vs Recent period average ({second_avg:,.0f}) - spending is increasing by {((second_avg/first_avg - 1) * 100):.1f}%"
+            elif second_avg < first_avg * 0.95:
+                trend_analysis = f"DOWNWARD TREND: Early period average ({first_avg:,.0f}) vs Recent period average ({second_avg:,.0f}) - spending is decreasing by {((1 - second_avg/first_avg) * 100):.1f}%"
+            else:
+                trend_analysis = f"STABLE TREND: Early period average ({first_avg:,.0f}) vs Recent period average ({second_avg:,.0f}) - spending is relatively stable"
+        else:
+            trend_analysis = "INSUFFICIENT DATA: Only one data point available"
+        
+        # Calculate coefficient of variation
+        cv = (data['std_dev'] / avg_expense * 100) if avg_expense > 0 else 0
+        variability_level = ""
+        if cv > 20:
+            variability_level = "HIGH VARIABILITY - spending is very unpredictable"
+        elif cv > 10:
+            variability_level = "MODERATE VARIABILITY - some unpredictability"
+        elif cv > 5:
+            variability_level = "LOW VARIABILITY - relatively predictable"
+        else:
+            variability_level = "VERY LOW VARIABILITY - very predictable spending"
+        
         summary = (
             f"Category: {category}\n"
-            f"Monthly totals: [{history}]\n"
-            f"Average spend: {avg_expense:.2f}\n"
-            f"Std deviation: {data['std_dev']:.2f}\n"
-            f"Months observed: {data['months_analyzed']}\n"
+            f"Monthly spending values: [{history}]\n"
+            f"Average monthly spend: {avg_expense:,.2f}\n"
+            f"Standard deviation: {data['std_dev']:,.2f}\n"
+            f"Coefficient of variation: {cv:.1f}% ({variability_level})\n"
+            f"Number of months analyzed: {data['months_analyzed']}\n"
+            f"Total spending: {data.get('total', avg_expense * data['months_analyzed']):,.2f}\n"
+            f"Trend Analysis: {trend_analysis}\n"
         )
 
         prompt = (
@@ -1099,14 +1134,26 @@ class SmartBudgetRecommender:
             "   - Apply the 50/30/20 rule principles when appropriate (needs/wants/savings)\n\n"
             "Given the user's spending history:\n"
             f"{summary}\n\n"
-            "YOUR INTELLIGENT RECOMMENDATION:\n"
-            "1. Analyze the spending pattern using your knowledge of this category globally\n"
-            "2. Consider economic factors, seasonal trends, and best practices\n"
-            "3. Provide a SMART recommendation that:\n"
-            "   - Reflects actual trends in the data\n"
-            "   - Accounts for category-specific factors\n"
-            "   - Includes appropriate buffer for variability and inflation\n"
-            "   - Is justified by your analysis and knowledge\n\n"
+            "🚨 CRITICAL: YOU MUST ANALYZE THE ACTUAL DATA ABOVE!\n\n"
+            "YOUR INTELLIGENT RECOMMENDATION PROCESS:\n"
+            "STEP 1: ANALYZE THE DATA FIRST (This is mandatory!):\n"
+            "   - Look at the 'Monthly spending values' array - what pattern do you see?\n"
+            "   - Read the 'Trend Analysis' - is spending increasing, decreasing, or stable?\n"
+            "   - Check the 'Coefficient of variation' - how predictable is the spending?\n"
+            "   - Calculate: If there's an upward trend, you MUST recommend increase\n"
+            "   - Calculate: If variability is high, you MUST recommend increase with larger buffer\n\n"
+            "STEP 2: APPLY YOUR KNOWLEDGE:\n"
+            "   - Consider category-specific factors (Food inflation, Transport volatility, etc.)\n"
+            "   - Factor in economic trends and inflation\n"
+            "   - Account for seasonal variations if relevant\n\n"
+            "STEP 3: PROVIDE SMART RECOMMENDATION:\n"
+            "   - The recommended_budget MUST reflect the data analysis from STEP 1\n"
+            "   - If trend shows increase → recommended_budget should be higher than average_expense\n"
+            "   - If trend shows decrease → recommended_budget should be lower than average_expense\n"
+            "   - If variability is high → recommended_budget should have larger buffer (20-30%)\n"
+            "   - Include appropriate buffer for inflation and unexpected expenses\n"
+            "   - Your reason MUST reference the specific data patterns you observed\n\n"
+            "⚠️ DO NOT give generic recommendations! Base your recommendation on the ACTUAL DATA provided above!\n\n"
             "CRITICAL RULES - READ CAREFULLY:\n"
             "⚠️ DO NOT ALWAYS RECOMMEND 'KEEP' - This is a common mistake. Analyze the data first!\n\n"
             "MANDATORY ANALYSIS STEPS:\n"