Spaces:
Sleeping
Sleeping
| # %% | |
| import gradio as gr | |
| import pandas as pd | |
| import numpy as np | |
| import yfinance as yf | |
| from prophet import Prophet | |
| import matplotlib.pyplot as plt | |
| import io | |
| from PIL import Image | |
| from datetime import datetime | |
| import plotly.express as px # Make sure plotly.express is imported | |
| import warnings | |
| # Suppress specific FutureWarnings | |
| warnings.simplefilter(action='ignore', category=FutureWarning) | |
| def stock_forecast(ticker, start_date='2022-01-01', end_date=None): | |
| # Set end_date to today's date if not provided | |
| if end_date is None: | |
| end_date = datetime.today().strftime('%Y-%m-%d') | |
| # Download stock market data | |
| df = yf.download(ticker, start=start_date, end=end_date) | |
| # Reset the index to get 'Date' back as a column | |
| df_plot = df.reset_index() | |
| # Prepare the data for Prophet | |
| df1 = df_plot[['Date', 'Close']].rename(columns={'Date': 'ds', 'Close': 'y'}) | |
| # Fit the model | |
| m = Prophet() | |
| m.fit(df1) | |
| # Create future dataframe and make predictions | |
| future = m.make_future_dataframe(periods=30) | |
| forecast = m.predict(future) | |
| # Plotting stock closing prices | |
| fig1 = px.line(df1.tail(90), x="ds", y="y", title='Stock Closing Prices by Date') | |
| fig1.update_layout( | |
| plot_bgcolor='black', | |
| paper_bgcolor='black', | |
| font=dict(color='white') | |
| ) | |
| # Plotting forecast | |
| forecast_40 = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(40) | |
| fig2 = px.line(forecast_40, x="ds", y=['yhat', 'yhat_lower', 'yhat_upper'], title=f'{ticker} 30 Days Forecast') | |
| fig2.update_layout( | |
| plot_bgcolor='black', | |
| paper_bgcolor='black', | |
| font=dict(color='white') | |
| ) | |
| # Customizing Matplotlib plot for forecast components | |
| plt.style.use('dark_background') # Use dark background style | |
| # Plot the components of the forecast using Matplotlib | |
| fig, ax = plt.subplots(figsize=(10, 8)) | |
| m.plot_components(forecast) | |
| # Apply customizations to the plot | |
| for line in ax.get_lines(): | |
| line.set_color('purple') # Set line color to purple | |
| ax.set_facecolor('black') # Set background color of the axes to black | |
| fig.patch.set_facecolor('black') # Set background color of the figure to black | |
| # Save the plot to a BytesIO object | |
| buf = io.BytesIO() | |
| plt.savefig(buf, format='png') | |
| buf.seek(0) | |
| plt.close(fig) | |
| # Convert the image to a PIL Image | |
| img = Image.open(buf) | |
| return fig1, fig2, img | |
| # %% | |
| # Create Gradio interface | |
| interface = gr.Interface( | |
| fn=stock_forecast, # Function to run | |
| inputs=[gr.Textbox(label="Enter Stock Ticker", value="NVDA"), # Input: stock ticker | |
| gr.Textbox(label="Enter Start Date (YYYY-MM-DD)", value="2022-01-01"), # Input: start date | |
| gr.Textbox(label="Enter End Date (YYYY-MM-DD)", value=datetime.today().strftime('%Y-%m-%d'))], # Input: end date | |
| outputs=[gr.Plot(label="Stock Closing Prices"), # Output: Plotly chart | |
| gr.Plot(label="30 Days Forecast"), | |
| gr.Image(label="Forecast Components")], # Output: Image | |
| title="Stock Market Forecast", | |
| description="Enter a stock ticker symbol, a start date, and an end date (in YYYY-MM-DD format) to view the historical closing prices, a 30-day forecast, and the forecast components." | |
| ) | |
| # Launch the Gradio app | |
| interface.launch() | |