Update OpenAI prompt to be global/worldwide instead of India-specific for international users

app/smart_recommendation.py

@@ -945,32 +945,79 @@ class SmartBudgetRecommender:
         )
 
         prompt = (
-            "You are an expert Indian personal finance coach. "
-            "Analyze the user's spending history and provide a smart, personalized budget recommendation.\n\n"
-            "IMPORTANT: You MUST analyze the spending pattern and provide a recommendation that is DIFFERENT from just keeping the same amount.\n\n"
-            "Analysis Guidelines:\n"
-            "1. TRENDING UPWARD: If monthly values show an increasing trend (later months > earlier months), "
-            "   recommend INCREASE by 10-25% to accommodate the growth trend.\n"
-            "   Example: [180000, 190000, 200000] → Recommend ~220000-250000 (increase)\n\n"
-            "2. TRENDING DOWNWARD: If monthly values show a decreasing trend (later months < earlier months), "
-            "   recommend DECREASE by 5-15% to reflect the reduction.\n"
-            "   Example: [220000, 210000, 200000] → Recommend ~180000-190000 (decrease)\n\n"
-            "3. HIGH VARIATION: If std_dev is high (>15% of average), recommend INCREASE by 15-25% "
-            "   to create a safety buffer for unpredictable spending.\n"
-            "   Example: std_dev = 30000, avg = 200000 → Recommend ~230000-250000 (increase)\n\n"
-            "4. STABLE & LOW VARIATION: Only if spending is very stable (std_dev < 5% of average) "
-            "   AND no clear trend, then KEEP with a small 5% buffer.\n"
-            "   Example: [200000, 200000, 200000] with std_dev < 10000 → Recommend ~210000 (keep with buffer)\n\n"
-            "5. SINGLE DATA POINT: If only one month of data, recommend INCREASE by 10-15% "
-            "   to account for potential variability.\n\n"
+            "You are an expert global personal finance coach with deep knowledge of:\n"
+            "- Spending patterns across different categories worldwide (Food, Transport, Entertainment, Utilities, etc.)\n"
+            "- Economic trends and inflation impacts globally\n"
+            "- Seasonal variations in spending (holidays, weather, cultural events)\n"
+            "- Best practices for budget management and financial planning\n"
+            "- Category-specific insights (e.g., Food & Drinks tend to have inflation, Transport varies with fuel prices)\n"
+            "- Regional economic factors and currency considerations\n\n"
+            "TASK: Analyze the user's spending history intelligently using your knowledge and provide a smart, personalized budget recommendation.\n\n"
+            "INTELLIGENT ANALYSIS APPROACH:\n\n"
+            "1. CATEGORY-SPECIFIC KNOWLEDGE:\n"
+            "   - Food & Drinks: Consider inflation (typically 2-5% annually globally), seasonal spikes, cultural events\n"
+            "   - Transport/Travel: Fuel price volatility, seasonal travel patterns, regional variations\n"
+            "   - Entertainment: Weekend/holiday variations, seasonal trends, cultural events\n"
+            "   - Utilities: Seasonal variations (cooling in summer, heating in winter, regional differences)\n"
+            "   - Healthcare: Unpredictable but essential, recommend buffer\n"
+            "   - Use your knowledge about how this category typically behaves globally\n\n"
+            "2. TREND ANALYSIS:\n"
+            "   - TRENDING UPWARD: If spending is increasing, consider if it's:\n"
+            "     * Inflation-driven (recommend increase to match inflation + buffer)\n"
+            "     * Lifestyle change (recommend increase with caution and explanation)\n"
+            "     * One-time spike (recommend keep or slight increase, explain it's temporary)\n"
+            "     * Seasonal pattern (recommend increase if entering high-spending season)\n"
+            "   - TRENDING DOWNWARD: If spending is decreasing, consider if it's:\n"
+            "     * Sustainable reduction (recommend decrease to reflect new pattern)\n"
+            "     * Temporary dip (recommend keep with buffer for recovery)\n"
+            "     * Seasonal pattern (recommend decrease if entering low-spending season)\n\n"
+            "3. VARIABILITY INTELLIGENCE:\n"
+            "   - HIGH VARIATION (std_dev > 15%): Indicates unpredictable spending\n"
+            "     * Recommend INCREASE by 20-30% to create safety buffer\n"
+            "     * Explain that high variability requires larger buffer for financial security\n"
+            "   - LOW VARIATION (std_dev < 5%): Indicates stable spending\n"
+            "     * Can recommend KEEP or small increase (5-10% for inflation buffer)\n"
+            "     * Still consider category-specific factors and economic trends\n\n"
+            "4. ECONOMIC CONTEXT:\n"
+            "   - Consider global inflation trends (typically 2-5% annually in most countries)\n"
+            "   - Factor in category-specific inflation (food inflation often higher than general inflation)\n"
+            "   - Account for seasonal price variations (holidays, weather, supply/demand)\n"
+            "   - Consider regional economic factors if relevant\n\n"
+            "5. BEST PRACTICES:\n"
+            "   - Always include a small buffer (5-10%) even for stable spending to handle unexpected expenses\n"
+            "   - For new budgets (single data point), be conservative but realistic (10-15% buffer)\n"
+            "   - Consider the user's spending history length (more data = more confidence in recommendation)\n"
+            "   - 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"
-            "CRITICAL: Do NOT always recommend 'keep'. Analyze the pattern and provide increase/decrease/keep based on the actual data trends.\n"
-            "The recommended_budget MUST reflect your analysis - if there's a trend, the budget should reflect it.\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 RULES:\n"
+            "- Do NOT always recommend 'keep' - use your intelligence to determine the best action\n"
+            "- If there's an upward trend, recommend INCREASE (don't ignore the trend)\n"
+            "- If there's high variation, recommend INCREASE to create buffer\n"
+            "- If spending is stable AND category is predictable, you can recommend KEEP with small buffer (5-10%)\n"
+            "- The recommended_budget should be SMART: Specific, Measurable, Achievable, Relevant, Time-bound\n"
+            "- Consider inflation: even stable spending should account for 2-5% annual inflation\n\n"
             "Respond strictly as JSON with the following keys:\n"
-            '{ "recommended_budget": number, "action": "increase|decrease|keep", "reason": "string" }.\n'
-            "The reason must be specific, explain the trend you observed, and justify why you chose increase/decrease/keep.\n"
-            "Use rupees for all amounts. Round recommended_budget to nearest 100.\n"
+            '{ "recommended_budget": number, "action": "increase|decrease|keep", "reason": "string" }.\n\n'
+            "The 'reason' field is CRITICAL - it must:\n"
+            "- Explain the spending pattern you observed\n"
+            "- Reference category-specific knowledge if relevant\n"
+            "- Justify why you chose increase/decrease/keep\n"
+            "- Be specific, actionable, and educational\n"
+            "- Use your knowledge to provide insights the user might not know\n"
+            "- Be culturally and regionally sensitive\n\n"
+            "Round recommended_budget to nearest 100. Use appropriate currency context in your reasoning.\n"
+            "Example reason: 'Your spending on Food & Drinks shows an upward trend (1800 → 2000 over 3 months), "
+            "likely due to food inflation (typically 3-5% annually globally). I recommend increasing your budget by 15% "
+            "to 2300 to accommodate this trend and provide a buffer for continued price increases and unexpected expenses.'\n"
         )
 
         try: