Update app.py

app.py

@@ -3,11 +3,14 @@
 ### ----------------------------- ###
 
 import streamlit as st
+import pickle as pkl
 import pandas as pd
 import numpy as np
 from sklearn.model_selection import train_test_split
 from sklearn.linear_model import LogisticRegression
 from sklearn import metrics
+from os.path import exists
+
 
 
 ### ----------------------------- ###
@@ -25,69 +28,84 @@ st.subheader('Feeling like you might need a better coping strategy? Take the qui
 ###       data transformation      ###
 ### ------------------------------ ###
 
-# load dataset
-uncleaned_data = pd.read_csv('data.csv')
-
-# remove timestamp from dataset (always first column)
-uncleaned_data = uncleaned_data.iloc[: , 1:]
-data = pd.DataFrame()
-
-# keep track of which columns are categorical and what 
-# those columns' value mappings are
-# structure: {colname1: {...}, colname2: {...} }
-cat_value_dicts = {}
-final_colname = uncleaned_data.columns[len(uncleaned_data.columns) - 1]
-
-# for each column...
-for (colname, colval) in uncleaned_data.iteritems():
-
-  # check if col is already a number; if so, add col directly
-  # to new dataframe and skip to next column
-  if isinstance(colval.values[0], (np.integer, float)):
-    data[colname] = uncleaned_data[colname].copy()
-    continue
-
-  # structure: {0: "lilac", 1: "blue", ...}
-  new_dict = {}
-  val = 0 # first index per column
-  transformed_col_vals = [] # new numeric datapoints
-
-  # if not, for each item in that column...
-  for (row, item) in enumerate(colval.values):
-    
-    # if item is not in this col's dict...
-    if item not in new_dict:
-      new_dict[item] = val
-      val += 1
+def load_dataset():
+  # load dataset
+  uncleaned_data = pd.read_csv('data.csv')
+  
+  # remove timestamp from dataset (always first column)
+  uncleaned_data = uncleaned_data.iloc[: , 1:]
+  data = pd.DataFrame()
+  
+  # keep track of which columns are categorical and what 
+  # those columns' value mappings are
+  # structure: {colname1: {...}, colname2: {...} }
+  cat_value_dicts = {}
+  final_colname = uncleaned_data.columns[len(uncleaned_data.columns) - 1]
+  
+  # for each column...
+  for (colname, colval) in uncleaned_data.iteritems():
+  
+    # check if col is already a number; if so, add col directly
+    # to new dataframe and skip to next column
+    if isinstance(colval.values[0], (np.integer, float)):
+      data[colname] = uncleaned_data[colname].copy()
+      continue
+  
+    # structure: {0: "lilac", 1: "blue", ...}
+    new_dict = {}
+    val = 0 # first index per column
+    transformed_col_vals = [] # new numeric datapoints
+  
+    # if not, for each item in that column...
+    for (row, item) in enumerate(colval.values):
+      
+      # if item is not in this col's dict...
+      if item not in new_dict:
+        new_dict[item] = val
+        val += 1
+      
+      # then add numerical value to transformed dataframe
+      transformed_col_vals.append(new_dict[item])
     
-    # then add numerical value to transformed dataframe
-    transformed_col_vals.append(new_dict[item])
+    # reverse dictionary only for final col (0, 1) => (vals)
+    if colname == final_colname:
+      new_dict = {value : key for (key, value) in new_dict.items()}
   
-  # reverse dictionary only for final col (0, 1) => (vals)
-  if colname == final_colname:
-    new_dict = {value : key for (key, value) in new_dict.items()}
-
-  cat_value_dicts[colname] = new_dict
-  data[colname] = transformed_col_vals
+    cat_value_dicts[colname] = new_dict
+    data[colname] = transformed_col_vals
 
 
 ### -------------------------------- ###
 ###           model training         ###
 ### -------------------------------- ###
 
-# select features and predicton; automatically selects last column as prediction
-cols = len(data.columns)
-num_features = cols - 1
-x = data.iloc[: , :num_features]
-y = data.iloc[: , num_features:]
+def train_model():
+  # select features and predicton; automatically selects last column as prediction
+  cols = len(data.columns)
+  num_features = cols - 1
+  x = data.iloc[: , :num_features]
+  y = data.iloc[: , num_features:]
+  
+  # split data into training and testing sets
+  x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)
+  
+  # instantiate the model (using default parameters)
+  model = LogisticRegression()
+  model.fit(x_train, y_train.values.ravel())
+  y_pred = model.predict(x_test)
+  
+  # save the model to file
+  pkl.dump(model, 'model.pkl')
+
+### -------------------------------- ###
+###            rerun logic           ###
+### -------------------------------- ###
 
-# split data into training and testing sets
-x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)
+if not os.path.exists('model.pkl'):
+  load_dataset()
+  train_model()
 
-# instantiate the model (using default parameters)
-model = LogisticRegression()
-model.fit(x_train, y_train.values.ravel())
-y_pred = model.predict(x_test)
+model = pkl.load('model.pkl')
 
 
 ### ------------------------------- ###