Create app.py

app.py

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+import gradio as gr
+from gradio.components import Markdown, Textbox, Button
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.svm import SVR
+from sklearn.pipeline import make_pipeline
+from sunpy.net import Fido
+from sunpy.net import attrs as a
+from sunpy.timeseries import TimeSeries
+
+
+
+
+def process_data():
+    # Define the time range for data retrieval
+    tstart = "2015-06-21 01:00"
+    tend = "2015-06-21 23:00"
+
+    # Query and fetch GOES XRS data
+    result_goes15 = Fido.search(a.Time(tstart, tend), a.Instrument("XRS"), a.goes.SatelliteNumber(15), a.Resolution("flx1s"))
+    files = Fido.fetch(result_goes15)
+
+    # Load the data into a TimeSeries
+    goes_15 = TimeSeries(files, concatenate=True)
+
+    # Extract X-ray flux and time data
+    flux_data = goes_15.quantity("xrsb").value
+    time_data = goes_15.time.datetime
+
+    # Create a feature matrix with time data (as numerical values)
+    X = np.array([(t - time_data[0]).total_seconds() for t in time_data]).reshape(-1, 1)
+
+    # Split the data into training and testing sets
+    X_train, X_test, y_train, y_test = train_test_split(X, flux_data, test_size=0.2, random_state=42)
+
+    # Train a linear regression model
+    linear_model = LinearRegression()
+    linear_model.fit(X_train, y_train)
+
+    # Train a quadratic regression model
+    quadratic_model = make_pipeline(PolynomialFeatures(degree=2), LinearRegression())
+    quadratic_model.fit(X_train, y_train)
+
+    # Train a cubic regression model
+    cubic_model = make_pipeline(PolynomialFeatures(degree=3), LinearRegression())
+    cubic_model.fit(X_train, y_train)
+
+    # Train a support vector regression (SVR) model
+    svr_model = SVR(kernel='linear')
+    svr_model.fit(X_train, y_train)
+
+    # Make predictions using all models
+    y_pred_linear = linear_model.predict(X_test)
+    y_pred_quadratic = quadratic_model.predict(X_test)
+    y_pred_cubic = cubic_model.predict(X_test)
+    y_pred_svr = svr_model.predict(X_test)
+
+    # Plot the actual and predicted data from all models
+    plt.figure(figsize=(12, 6))
+    plt.scatter(X_test, y_test, color='blue', label='Actual Data')
+    plt.plot(X_test, y_pred_linear, color='red', linewidth=2, label='Linear Prediction')
+    plt.plot(X_test, y_pred_quadratic, color='green', linewidth=2, label='Quadratic Prediction')
+    plt.plot(X_test, y_pred_cubic, color='orange', linewidth=2, label='Cubic Prediction')
+    plt.plot(X_test, y_pred_svr, color='purple', linewidth=2, label='SVR Prediction')
+
+    # Include solar flux data as an additional line in the plot
+    plt.plot(X, flux_data, color='cyan', linestyle='dashed', label='Solar Flux')
+
+    plt.title('GOES XRS Space Weather Forecast')
+    plt.xlabel('Time (seconds since start)')
+    plt.ylabel('X-ray Flux / Solar Flux')
+    plt.legend()
+
+    # Save the image
+    plt.savefig('space_weather_forecast.png')
+
+    # Display the plot
+    #plt.show()
+    fig = plt.figure()
+
+
+process_data()
+
+with gr.Blocks(title="Space Weather Forecast", analytics_enabled=False) as spaceml:
+    gr.Markdown("# Space Weather Forecast")
+    gr.Markdown("Welcome to the Space Weather Forecast!")
+    with gr.Row():
+        with gr.Column(scale=1):
+            gradio_plot = gr.Image('space_weather_forecast.png')
+
+spaceml.queue().launch(show_api=True, share=True)
+
+