Update app.py

app.py

@@ -1,10 +1,11 @@
 import gradio as gr
 import torch
+import torch.nn as nn  # Added missing import
 import numpy as np
 from sklearn.preprocessing import MinMaxScaler
 import pandas as pd
 
-# Load and preprocess data
+# 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)
@@ -13,46 +14,39 @@ df = df.sort_index()
 df['Close'] = df['Close'].replace({r'\$': ''}, regex=True).astype(float)
 
 scaler = MinMaxScaler(feature_range=(0, 1))
-scaled_data = scaler.fit_transform(df['Close'].values.reshape(-1, 1))
+scaler.fit(df['Close'].values.reshape(-1, 1))
 
 # 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):
+    def __init__(self, input_size=1, hidden_size=100, 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), 50)
-        c0 = torch.zeros(num_layers, x.size(0), 50)
+        h0 = torch.zeros(num_layers, x.size(0), 100)
+        c0 = torch.zeros(num_layers, x.size(0), 100)
         out, _ = self.lstm(x, (h0, c0))
         return self.fc(out[:, -1, :])
 
-# Load trained model
+# Load model
 model = LSTMModel()
-model.load_state_dict(torch.load('lstm_model.pth'))
+model.load_state_dict(torch.load('lstm_model.pth', map_location=torch.device('cpu')))  # Added map_location for compatibility
 model.eval()
 
 def forecast(past_prices, steps=30):
     try:
-        # Parse input (comma-separated prices)
         prices = [float(x.strip().replace('$', '')) for x in past_prices.split(',')]
         if len(prices) < 60:
-            return "Error: Please provide at least 60 prices."
-        
-        # Scale and prepare sequence
+            return "Error: At least 60 prices required."
         prices_scaled = scaler.transform(np.array(prices).reshape(-1, 1))
         current_seq = torch.from_numpy(prices_scaled[-60:].reshape(1, 60, 1)).float()
-        
-        # Forecast
         predictions = []
         for _ in range(steps):
             with torch.no_grad():
                 pred_scaled = model(current_seq).item()
                 predictions.append(pred_scaled)
                 current_seq = torch.cat((current_seq[:, 1:, :], torch.tensor([[[pred_scaled]]]).float()), dim=1)
-        
-        # Inverse transform
         predictions = scaler.inverse_transform(np.array(predictions).reshape(-1, 1)).flatten()
         return pd.DataFrame({'Forecast': predictions}).to_string()
     except Exception as e:
@@ -61,7 +55,7 @@ def forecast(past_prices, steps=30):
 # Create Gradio interface
 iface = gr.Interface(
     fn=forecast,
-    inputs=gr.Textbox(label="Past Prices (comma-separated, at least 60, e.g., 273.36,273.52,...)"),
+    inputs=gr.Textbox(label="Past Prices (comma-separated, e.g., 273.36,273.52,...)"),
     outputs=gr.Textbox(label="Forecasted Prices")
 )
 iface.launch(share=True)