Update app.py

app.py

@@ -23,6 +23,8 @@ st.code('''from data_utils import *''',language='python')
 
 
 st.header('**Step 2:**  Instantiate class')
+st.link_button("Go to original grid_info", "https://github.com/leap-stc/ClimSim/tree/main/grid_info",use_container_width=True)
+st.link_button("Go to original input_mean input_max input_min output_scale", "https://github.com/leap-stc/ClimSim/tree/main/preprocessing/normalizations",use_container_width=True)
 st.code('''#Change the path to your own
 grid_info = xr.open_dataset('ClimSim_low-res_grid-info.nc')
 input_mean = xr.open_dataset('input_mean.nc')
@@ -77,10 +79,10 @@ const_model = data.target_train.mean(axis = 0)
 
 
 st.subheader('Train multiple linear regression model')
-st.latex(r'''\beta=(X^{T}_{train} X_{train})^{-1} X^{T}_{train} y_{train}
-\hat{y}=X^{T}_{train} \beta
-where X_{train} and X_{input} correspond to the training data and the input data you would like to inference on, respectively. 
-X_{train} and X_{input} both have a column of ones concatenated to the feature space for the bias.''')
+st.latex(r'''\beta=(X^{T}_{train} X_{train})^{-1} X^{T}_{train} y_{train} \\
+&\hat{y}=X^{T}_{train} \beta\\
+&text{where} X_{train} text{and} X_{input} text{correspond to the training data and the input data you would like to inference on, respectively.}\\ 
+&X_{train} text{and} X_{input} text{both have a column of ones concatenated to the feature space for the bias.}''')
 st.text('adding bias unit')
 st.code('''X = data.input_train
 bias_vector = np.ones((X.shape[0], 1))