Update app.py

app.py

@@ -44,43 +44,38 @@ SEQ_LENGTH = 60  # Should match your training
 
 def fetch_stock_data(ticker, days=365):
     """
-    Load stock data from a local CSV file in the project root folder.
-    If the file doesn't exist, returns an error message.
-
-    Parameters:
-        ticker (str): Stock ticker symbol (used as filename prefix)
-        days (int): Number of days of recent data to keep (optional)
-
-    Returns:
-        tuple: (DataFrame, error_message)
+    Load stock data from a local CSV file in the root folder.
+    Automatically detects 'Date' and 'Close' columns (case-insensitive).
     """
     try:
-        # Construct the file path (e.g., ./AAPL.csv)
         filename = f"{ticker.upper()}.csv"
         file_path = os.path.join(os.getcwd(), filename)
 
-        # Check if file exists
         if not os.path.exists(file_path):
             return None, f"Dataset file not found in root folder: {filename}"
 
-        # Load dataset
         df = pd.read_csv(file_path)
+        df.columns = [col.strip().lower() for col in df.columns]  # normalize column names
+
+        # Try to identify date and close columns
+        date_col = next((c for c in df.columns if 'date' in c or 'time' in c), None)
+        close_col = next((c for c in df.columns if 'close' in c), None)
 
-        # Ensure 'Date' and 'Close' columns exist
-        if 'Date' not in df.columns or 'Close' not in df.columns:
-            return None, f"File must contain 'Date' and 'Close' columns in {filename}"
+        if not date_col or not close_col:
+            return None, f"File must contain a date-like and close-like column in {filename}"
 
-        # Parse dates and set index
-        df['Date'] = pd.to_datetime(df['Date'])
-        df.set_index('Date', inplace=True)
+        # Convert to datetime and set index
+        df[date_col] = pd.to_datetime(df[date_col])
+        df.set_index(date_col, inplace=True)
+        df.sort_index(inplace=True)
 
-        # Keep only recent `days`
+        # Keep last `days`
         end_date = df.index.max()
         start_date = end_date - timedelta(days=days)
         df = df.loc[df.index >= start_date]
 
-        # Rename column for consistency
-        df = df[['Close']].copy()
+        # Keep only close price
+        df = df[[close_col]].copy()
         df.columns = ['Price']
 
         return df, None