Update app.py

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 import torch
-import torch.nn as nn  # Added missing import
+import torch.nn as nn
 import numpy as np
 from sklearn.preprocessing import MinMaxScaler
 import pandas as pd
@@ -9,29 +9,29 @@ import pandas as pd
 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[['Close/Last']].rename(columns={'Close/Last': 'Close'})
 df = df.sort_index()
 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
+# Define LSTM model (matching trained model: hidden_size=50)
 class LSTMModel(nn.Module):
-    def __init__(self, input_size=1, hidden_size=100, num_layers=2, output_size=1, dropout=0.2):
+    def __init__(self, input_size=1, hidden_size=50, num_layers=2, output_size=1, dropout=0.2):
         super().__init__()
         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), 100)
-        c0 = torch.zeros(num_layers, x.size(0), 100)
+        h0 = torch.zeros(num_layers, x.size(0), hidden_size)
+        c0 = torch.zeros(num_layers, x.size(0), hidden_size)
         out, _ = self.lstm(x, (h0, c0))
         return self.fc(out[:, -1, :])
 
 # Load model
 model = LSTMModel()
-model.load_state_dict(torch.load('lstm_model.pth', map_location=torch.device('cpu')))  # Added map_location for compatibility
+model.load_state_dict(torch.load('lstm_model.pth', map_location=torch.device('cpu')))
 model.eval()
 
 def forecast(past_prices, steps=30):