Update app.py

app.py

@@ -1,94 +1,79 @@
-import numpy as np
+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
-import matplotlib.pyplot as plt
-from statsmodels.tsa.arima.model import ARIMA
-import yfinance as yf
-import gradio as gr
 
 logging.basicConfig(level=logging.INFO)
 
-def fetch_data(period='1d'):
-    logging.info(f"Fetching data for the period {period}...")
-    data = yf.download(tickers='ETH-USD', period=period, interval='1m')
-    if data.empty:
-        logging.error("No data fetched. Check the period or ticker symbol.")
-        return None
-    logging.info(f"Fetched {len(data)} data points for the period {period}.")
-    return data
+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 model training.")
-        return None
+        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...")
-
-    # Check for NaN values in the data
-    if data['Close'].isna().any():
-        logging.error("Data contains NaN values. Please clean the data before model training.")
-        return None
-
-    try:
-        model = ARIMA(data['Close'], order=(5, 1, 0))
-        model_fit = model.fit()
-        logging.info(model_fit.summary())
-    except Exception as e:
-        logging.error(f"Model training failed: {e}")
-        return None
-
+    model = ARIMA(data['Close'], order=(5, 1, 0))
+    model_fit = model.fit()
     logging.info("Model training completed.")
-
-    logging.info("Generating predictions...")
-    try:
-        forecast = model_fit.forecast(steps=predict_steps)
-        if np.isnan(forecast).any():
-            logging.error("Generated predictions contain NaN values. Model might be improperly configured.")
-            return None
-    except Exception as e:
-        logging.error(f"Prediction generation failed: {e}")
-        return None
-
-    future_dates = pd.date_range(start=data.index[-1], periods=predict_steps + 1, freq=freq, inclusive='right')
+    
+    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(f"Forecast Data:\n{forecast_df.head()}")
+    
     logging.info("Predictions generated successfully.")
-
     return forecast_df
 
-def plot_eth(period='1d'):
-    data = fetch_data(period)
-    predict_steps = 5  # Modify as needed
-    freq = 'T'  # 'T' stands for minutes
-    
+def plot_eth(period):
+    data, predict_steps, freq = fetch_eth_price(period)
     forecast_df = make_predictions(data, predict_steps, freq)
-    if forecast_df is None:
-        logging.error("Failed to generate predictions.")
-        return None
     
-    plt.figure(figsize=(10, 5))
-    plt.plot(data['Close'], label='Actual ETH Price')
-    plt.plot(forecast_df['Prediction'], label='Forecasted ETH Price', linestyle='dotted', color='orange')
-    plt.title('ETH Price Prediction')
-    plt.xlabel('Time')
-    plt.ylabel('Price (USD)')
-    plt.legend()
-    plt.grid(True)
-    plt.tight_layout()
-
-    # Save the plot to a file
-    plot_filename = '/home/user/app/eth_price_prediction.png'
-    plt.savefig(plot_filename)
-    logging.info("Plotting completed.")
+    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)")
     
-    return plot_filename
+    logging.info("Plotting completed.")
+    return fig
 
 def refresh_predictions(period):
-    plot_filename = plot_eth(period)
-    if plot_filename is None:
-        return "Error in generating plot."
-    return plot_filename
+    return plot_eth(period)
 
-iface = gr.Interface(fn=refresh_predictions, inputs="text", outputs="image", live=True)
+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()