Update app.py

app.py

@@ -8,24 +8,37 @@ import pandas as pd
 # Load data and scaler
 df = pd.read_csv('HistoricalQuotes.csv')
 df['Date'] = pd.to_datetime(df['Date'], format='%m/%d/%Y')
-df.set_index('Date', inplace=True)
-df = df[[' Close/Last']].rename(columns={' Close/Last': 'Close'})
 df = df.sort_index()
+
+# Find the closing price column
+possible_columns = [' Close/Last', 'Close', 'close', 'close_last']
+close_column = None
+for col in possible_columns:
+    if col in df.columns:
+        close_column = col
+        break
+
+if close_column is None:
+    raise KeyError(f"None of {possible_columns} found in columns: {list(df.columns)}")
+
+df = df[[close_column]].rename(columns={close_column: 'Close'})
 df['Close'] = df['Close'].replace({r'\$': ''}, regex=True).astype(float)
 
 scaler = MinMaxScaler(feature_range=(0, 1))
 scaler.fit(df['Close'].values.reshape(-1, 1))
 
-# Define LSTM model (matching trained model: hidden_size=50)
+# Define LSTM model
 class LSTMModel(nn.Module):
     def __init__(self, input_size=1, hidden_size=50, num_layers=2, output_size=1, dropout=0.2):
         super().__init__()
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers  # Store num_layers as instance variable
         self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, dropout=dropout)
         self.fc = nn.Linear(hidden_size, output_size)
 
     def forward(self, x):
-        h0 = torch.zeros(num_layers, x.size(0), hidden_size)
-        c0 = torch.zeros(num_layers, x.size(0), hidden_size)
+        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size)
+        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size)
         out, _ = self.lstm(x, (h0, c0))
         return self.fc(out[:, -1, :])