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| import gradio as gr | |
| import yfinance as yf | |
| import plotly.graph_objects as go | |
| from statsmodels.tsa.arima.model import ARIMA | |
| import pandas as pd | |
| import logging | |
| logging.basicConfig(level=logging.INFO) | |
| def fetch_eth_price(period): | |
| eth = yf.Ticker("ETH-USD") | |
| if period == '1d': | |
| data = eth.history(period="1d", interval="1m") | |
| predict_steps = 60 # Next 60 minutes | |
| freq = 'min' # Minute frequency | |
| elif period == '5d': | |
| data = eth.history(period="5d", interval="15m") | |
| predict_steps = 96 # Next 24 hours | |
| freq = '15min' # 15 minutes frequency | |
| elif period == '1wk': | |
| data = eth.history(period="1wk", interval="30m") | |
| predict_steps = 336 # Next 7 days | |
| freq = '30min' # 30 minutes frequency | |
| elif period == '1mo': | |
| data = eth.history(period="1mo", interval="1h") | |
| predict_steps = 720 # Next 30 days | |
| freq = 'H' # Hourly frequency | |
| else: | |
| return None, None, None | |
| data.index = pd.DatetimeIndex(data.index) | |
| data = data.asfreq(freq) # Ensure the data has a consistent frequency | |
| # Limit the data to the last 200 points to reduce prediction time | |
| data = data[-200:] | |
| return data, predict_steps, freq | |
| def make_predictions(data, predict_steps, freq): | |
| if data is None or data.empty: | |
| logging.error("No data available for prediction.") | |
| return pd.DataFrame(index=pd.date_range(start=pd.Timestamp.now(), periods=predict_steps+1, freq=freq)[1:]) | |
| logging.info(f"Starting model training with {len(data)} data points...") | |
| model = ARIMA(data['Close'], order=(5, 1, 0)) | |
| model_fit = model.fit() | |
| logging.info("Model training completed.") | |
| forecast = model_fit.forecast(steps=predict_steps) | |
| future_dates = pd.date_range(start=data.index[-1], periods=predict_steps+1, freq=freq, inclusive='right') | |
| forecast_df = pd.DataFrame(forecast, index=future_dates[1:], columns=['Prediction']) | |
| logging.info("Predictions generated successfully.") | |
| return forecast_df | |
| def plot_eth(period): | |
| data, predict_steps, freq = fetch_eth_price(period) | |
| forecast_df = make_predictions(data, predict_steps, freq) | |
| fig = go.Figure() | |
| fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='ETH Price')) | |
| fig.add_trace(go.Scatter(x=forecast_df.index, y=forecast_df['Prediction'], mode='lines', name='Prediction', line=dict(dash='dash', color='orange'))) | |
| fig.update_layout(title=f"ETH Price and Predictions ({period})", xaxis_title="Date", yaxis_title="Price (USD)") | |
| logging.info("Plotting completed.") | |
| return fig | |
| def refresh_predictions(period): | |
| return plot_eth(period) | |
| with gr.Blocks() as iface: | |
| period = gr.Radio(["1d", "5d", "1wk", "1mo"], label="Select Period") | |
| plot = gr.Plot() | |
| refresh_button = gr.Button("Refresh Predictions and Prices") | |
| period.change(fn=plot_eth, inputs=period, outputs=plot) | |
| refresh_button.click(fn=refresh_predictions, inputs=period, outputs=plot) | |
| iface.launch() | |