Update app.py

app.py

@@ -1,39 +1,21 @@
-import gradio as gr
-import yfinance as yf
-import plotly.graph_objects as go
-from statsmodels.tsa.arima.model import ARIMA
+import numpy as np
 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
 
-# Setup logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+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:
-        logging.error("Invalid period specified.")
-        return None, None, None
-    
-    data.index = pd.DatetimeIndex(data.index)
-    data = data.asfreq(freq)  # Ensure the data has a consistent frequency
+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, predict_steps, freq
+    return data
 
 def make_predictions(data, predict_steps, freq):
     if data is None or data.empty:
@@ -41,6 +23,12 @@ def make_predictions(data, predict_steps, freq):
         return None
 
     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()
@@ -48,21 +36,20 @@ def make_predictions(data, predict_steps, freq):
     except Exception as e:
         logging.error(f"Model training failed: {e}")
         return None
-    
+
     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
 
-    if forecast.isnull().any():
-        logging.error("Generated predictions contain NaN values.")
-        return None
-
-    future_dates = pd.date_range(start=data.index[-1], periods=predict_steps+1, freq=freq, inclusive='right')
+    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()}")
@@ -70,34 +57,38 @@ def make_predictions(data, predict_steps, freq):
 
     return forecast_df
 
-def plot_eth(period):
-    data, predict_steps, freq = fetch_eth_price(period)
-    if data is None or predict_steps is None or freq is None:
-        logging.error("Failed to fetch data or set up prediction parameters.")
-        return None
-
+def plot_eth(period='1d'):
+    data = fetch_data(period)
+    predict_steps = 5  # Modify as needed
+    freq = 'T'  # 'T' stands for minutes
+    
     forecast_df = make_predictions(data, predict_steps, freq)
-    if forecast_df is None or forecast_df.empty:
+    if forecast_df is None:
         logging.error("Failed to generate predictions.")
         return None
     
-    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')))
-    fig.update_layout(title=f"ETH Price and Predictions ({period})", xaxis_title="Date", yaxis_title="Price (USD)")
+    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.")
-    return fig
+    
+    return plot_filename
 
 def refresh_predictions(period):
-    return plot_eth(period)
+    plot_filename = plot_eth(period)
+    if plot_filename is None:
+        return "Error in generating plot."
+    return plot_filename
 
-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 = gr.Interface(fn=refresh_predictions, inputs="text", outputs="image", live=True)
 iface.launch()