Create app.py

app.py

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+import gradio as gr
+import torch
+import numpy as np
+from sklearn.preprocessing import MinMaxScaler
+import pandas as pd
+
+# Load and preprocess data
+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()
+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))
+
+# 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.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)
+        out, _ = self.lstm(x, (h0, c0))
+        return self.fc(out[:, -1, :])
+
+# Load trained model
+model = LSTMModel()
+model.load_state_dict(torch.load('lstm_model.pth'))
+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
+        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:
+        return f"Error: {str(e)}"
+
+# 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,...)"),
+    outputs=gr.Textbox(label="Forecasted Prices")
+)
+iface.launch(share=True)