Update app.py

app.py

@@ -11,6 +11,8 @@ from tensorflow import keras
 from sklearn.preprocessing import MinMaxScaler
 import warnings
 warnings.filterwarnings('ignore')
+import os
+from datetime import datetime, timedelta
 
 # Load your saved models (update paths as needed)
 # For Hugging Face, these will be in the same directory as app.py
@@ -39,51 +41,26 @@ def load_models():
 arima_model, prophet_model, lstm_model, scaler = load_models()
 SEQ_LENGTH = 60  # Should match your training
 
+
 def fetch_stock_data(ticker, days=365):
-    """Fetch stock data from Yahoo Finance"""
-    try:
-        end_date = datetime.now()
-        start_date = end_date - timedelta(days=days)
-        
-        # Add retry logic and better error handling
-        import time
-        max_retries = 3
-        for attempt in range(max_retries):
-            try:
-                df = yf.download(ticker, start=start_date, end=end_date, progress=False, auto_adjust=True)
-                if not df.empty:
-                    break
-                time.sleep(1)  # Wait before retry
-            except Exception as e:
-                if attempt == max_retries - 1:
-                    raise e
-                time.sleep(1)
-        
-        if df.empty:
-            return None, f"No data found for ticker: {ticker}. Please verify the ticker symbol is correct."
-        
-        # Handle both multi-level and single-level columns
-        if isinstance(df.columns, pd.MultiIndex):
-            df = df['Close'].to_frame()
-        elif 'Close' in df.columns:
-            df = df[['Close']].copy()
-        else:
-            # Try to find a price column
-            price_col = [col for col in df.columns if 'close' in col.lower()]
-            if price_col:
-                df = df[[price_col[0]]].copy()
-            else:
-                return None, f"Could not find price data for {ticker}"
-        
+    ticker = ticker.upper()
+    filename = f"{ticker}.csv"
+    if not os.path.exists(filename):
+        return None, f"No data found for ticker {ticker}. Upload {ticker}.csv to use offline mode."
+
+    df = pd.read_csv(filename, index_col=0, parse_dates=True)
+    if 'Close' in df.columns:
+        df = df[['Close']].copy()
+    else:
         df.columns = ['Price']
-        df = df.dropna()
-        
-        if len(df) < 100:
-            return None, f"Insufficient data for {ticker}. Only {len(df)} days available."
-        
-        return df, None
-    except Exception as e:
-        return None, f"Error fetching data for {ticker}: {str(e)}"
+    df.columns = ['Price']
+
+    df = df.dropna()
+    df = df.tail(days)
+    if df.empty:
+        return None, f"No data available in {filename}"
+    return df, None
+
 
 def make_arima_forecast(data, days):
     """Make ARIMA forecast"""