Update main.py

main.py

@@ -50,13 +50,15 @@ PRODUCT_PRICES_USD_PER_TON = {
 
 OPTIMIZATION_SPACE = {
     'capacity_kta': (500, 600),
-    'technology': ["Engro_Pakistan", "Shin_Etsu_2004"],
+    'technology': ["Engro_Pakistan", "Shin_Etsu_2004"],  
     'sourcing_strategy': ['Integrated_Production'],
     'export_market_mix': (0.6, 0.8),
     'sell_byproducts': [True]
 }
 
+
 def forecast_prices(base_price, years=PROJECT_YEARS, cagr=0.04):
+    random.seed(42) 
     historical = [base_price * (1 + cagr + random.uniform(-0.03, 0.03)) ** i for i in range(-5, 0)]
     model = ARIMA(historical, order=(1, 1, 0))
     fit = model.fit()
@@ -108,7 +110,7 @@ def calculate_project_kpis(**params):
     cumulative_cash_flow = np.cumsum(cash_flows)
     payback_period_years = np.where(cumulative_cash_flow > 0)[0]
     payback_period = payback_period_years[0] + 1 if len(payback_period_years) > 0 else float('inf')
-    annual_profit = np.mean([cf for cf in cash_flows[1:] if cf > 0])
+    annual_profit = np.mean([cf for cf in cash_flows[1:] if cf > 0]) if any(cf > 0 for cf in cash_flows[1:]) else np.nan  # FIX: جلوگیری از nan
 
     return {"irr": irr, "annual_profit": annual_profit, "total_capex": total_capex, "payback_period": payback_period}
 
@@ -122,6 +124,7 @@ def run_optimizations_without_ml():
 
 def run_genetic_algorithm():
     print("Running Genetic Algorithm (GA)...")
+    random.seed(42)  # FIX: reproducibility
     population = [{k: random.choice(v) if isinstance(v, list) else random.uniform(*v) for k,v in OPTIMIZATION_SPACE.items()} for _ in range(40)]
     best_overall_individual = None
     best_overall_fitness = -float('inf')