Update app.py

app.py

@@ -32,4 +32,29 @@ data.target_train = data.load_npy_file('train_target_small.npy')
 data.input_val = data.load_npy_file('val_input_small.npy')
 data.target_val = data.load_npy_file('val_target_small.npy')
 
-st.markdown('Streamlit bb')
+
+const_model = data.target_train.mean(axis = 0)
+X = data.input_train
+bias_vector = np.ones((X.shape[0], 1))
+X = np.concatenate((X, bias_vector), axis=1)
+mlr_weights = np.linalg.inv(X.transpose()@X)@X.transpose()@data.target_train
+data.set_pressure_grid(data_split = 'val')
+
+const_pred_val = np.repeat(const_model[np.newaxis, :], data.target_val.shape[0], axis = 0)
+print(const_pred_val.shape)
+
+# Multiple Linear Regression
+X_val = data.input_val
+bias_vector_val = np.ones((X_val.shape[0], 1))
+X_val = np.concatenate((X_val, bias_vector_val), axis=1)
+mlr_pred_val = X_val@mlr_weights
+print(mlr_pred_val.shape)
+
+# Load your prediction here
+
+# Load predictions into data_utils object
+data.model_names = ['const', 'mlr'] # add names of your models here
+preds = [const_pred_val, mlr_pred_val] # add your custom predictions here
+data.preds_val = dict(zip(data.model_names, preds))
+
+st.markdown('Streamlit c')