Add app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+from prophet import Prophet
+import yfinance as yf
+from sklearn.metrics import mean_absolute_error, mean_squared_error
+import matplotlib.pyplot as plt
+from prophet.plot import plot_plotly, plot_components_plotly
+
+# Function to fetch stock data from Yahoo Finance
+def fetch_stock_data(ticker_symbol, start_date, end_date):
+    stock_data = yf.download(ticker_symbol, start=start_date, end=end_date)
+    df = stock_data[['Adj Close']].reset_index()
+    df = df.rename(columns={'Date': 'ds', 'Adj Close': 'y'})
+    return df
+
+# Function to train the Prophet model
+def train_prophet_model(df):
+    model = Prophet()
+    model.fit(df)
+    return model
+
+# Function to make the forecast
+def make_forecast(model, periods):
+    future = model.make_future_dataframe(periods=periods)
+    forecast = model.predict(future)
+    return forecast
+
+# Function to calculate performance metrics
+def calculate_performance_metrics(actual, predicted):
+    mae = mean_absolute_error(actual, predicted)
+    mse = mean_squared_error(actual, predicted)
+    rmse = np.sqrt(mse)
+    return {'MAE': mae, 'MSE': mse, 'RMSE': rmse}
+
+# Function to handle the complete process and return results
+def forecast_stock(ticker_symbol, start_date, end_date, forecast_horizon):
+    # Fetch stock data
+    df = fetch_stock_data(ticker_symbol, start_date, end_date)
+
+    # Train the model
+    model = train_prophet_model(df)
+
+    # Convert forecast horizon to days
+    horizon_mapping = {'1 Month': 30, '6 months': (365/2), '1 year': 365, '2 years': 730, '3 years': 1095, '5 years': 1825}
+    forecast_days = horizon_mapping[forecast_horizon]
+
+    # Make forecast
+    forecast = make_forecast(model, forecast_days)
+
+    # Plot the forecast results using matplotlib
+    plt.figure(figsize=(10, 6))
+    plt.plot(df['ds'], df['y'], label='Actual Data')
+    plt.plot(forecast['ds'], forecast['yhat'], label='Forecast', color='orange')
+    plt.fill_between(forecast['ds'], forecast['yhat_lower'], forecast['yhat_upper'], color='orange', alpha=0.2)
+    plt.xlabel('Date')
+    plt.ylabel('Price')
+    plt.legend()
+    plt.title('Stock Price Forecast')
+    plt.savefig('forecast_plot.png')
+    plt.close()
+
+    # Plot the forecast components
+    model.plot_components(forecast)
+    plt.savefig('forecast_components.png')
+    plt.close()
+
+    return 'forecast_plot.png', 'forecast_components.png'
+
+# Gradio Interface
+def main():
+    with gr.Blocks() as demo:
+        gr.Markdown("# Stock Forecasting")
+
+        ticker_symbol = gr.Textbox(label="Enter Ticker Symbol", value="RACE")
+        start_date = gr.Textbox(label="Start Date (YYYY-MM-DD) of Data", value="2015-01-01")
+        end_date = gr.Textbox(label="End Date (YYYY-MM-DD) of Data", value=str(pd.to_datetime('today').date()))
+        forecast_horizon = gr.Dropdown(
+            label="Forecast Horizon", 
+            choices=['1 Month','6 months','1 year', '2 years', '3 years', '5 years'], 
+            value='1 year'
+        )
+
+        forecast_button = gr.Button("Forecast Stock Prices")
+        plot_output1 = gr.Image(label="Forecast Plot")
+        plot_output2 = gr.Image(label="Forecast Components")
+
+        forecast_button.click(forecast_stock, 
+                              inputs=[ticker_symbol, start_date, end_date, forecast_horizon], 
+                              outputs=[plot_output1, plot_output2])
+
+    demo.launch()
+
+# Run the Gradio app
+if __name__ == "__main__":
+    main()