Upload forecaster_cli.py

forecaster_cli.py

@@ -98,13 +98,14 @@ def fetch_live_features(ticker):
 def get_market_cap_tier(ticker):
     print(f"[{ticker}] Resolving market cap classification from NSE servers...")
     try:
-        large = pd.read_csv(TIERS["Large"])['Symbol'].tolist()
+        ua = {'User-Agent': 'Mozilla/5.0'}
+        large = pd.read_csv(TIERS["Large"], storage_options=ua)['Symbol'].tolist()
         if ticker in large: return "Large"
         
-        mid = pd.read_csv(TIERS["Mid"])['Symbol'].tolist()
+        mid = pd.read_csv(TIERS["Mid"], storage_options=ua)['Symbol'].tolist()
         if ticker in mid: return "Mid"
         
-        small = pd.read_csv(TIERS["Small"])['Symbol'].tolist()
+        small = pd.read_csv(TIERS["Small"], storage_options=ua)['Symbol'].tolist()
         if ticker in small: return "Small"
         
     except Exception as e:
@@ -265,6 +266,13 @@ def run_daemon():
             # Format report and save to JSON
             report = df[['Ticker', 'Decision', 'Confidence', 'Reasoning', 'Sales_Growth', 'ROE', 'Debt_to_Equity', 'OPM']]
             report_dict = report.to_dict(orient='records')
+            
+            import math
+            for item in report_dict:
+                for k, v in item.items():
+                    if isinstance(v, float) and math.isnan(v):
+                        item[k] = None
+
             import json
             with open("nifty50_predictions.json", "w") as f:
                 json.dump({"last_updated": datetime.datetime.now().isoformat(), "predictions": report_dict}, f, indent=4)