move tensorflow as a lazyload and wrap in main

main.py

@@ -1,59 +1,64 @@
-import tensorflow as tf
-import keras
 import sqlite3
 from datetime import date, datetime
+from datapuller import DataPuller
 
-# Initilize SqLite
-dbconn = sqlite3.connect("./data/appdata.db")
-dbcursor = dbconn.cursor()
+def main():
+    # Initilize SqLite
+    dbconn = sqlite3.connect("./data/appdata.db")
+    dbcursor = dbconn.cursor()
 
-# Create keystore table if it doesnt exist already
-dbcursor.execute('''
-CREATE TABLE IF NOT EXISTS keystore (
+    # Create keystore table if it doesnt exist already
+    dbcursor.execute('''
+    CREATE TABLE IF NOT EXISTS keystore (
         key TEXT PRIMARY KEY,
         value TEXT
-)
-''')
+    )
+    ''')
 
-def SetKey(Key, Value):
+    def SetKey(Key, Value):
         dbcursor.execute('INSERT OR REPLACE INTO keystore (key, value) VALUES (?, ?)', (Key, Value))
         dbconn.commit()
 
-def GetKey(Key):
+    def GetKey(Key):
         dbcursor.execute('SELECT value FROM keystore WHERE key = ?', (Key, ))
         result = dbcursor.fetchone()
         return result[0] if result else None
 
-# Pull the last run data
-lastrundata = GetKey("LastRun")
-lastrun = None
-if lastrundata != None:
+    # Pull the last run data
+    lastrundata = GetKey("LastRun")
+    lastrun = None
+    if lastrundata != None:
         lastrun = datetime.strptime( lastrundata, "%Y-%m-%d" )
-else:
+    else:
         lastrun = datetime.strptime( "2000-01-01", "%Y-%m-%d" )
 
-# If the strings dont match becuase were only checking day it has to be a new day so update the data
-if lastrun != str(date.today()):
+    # If the strings dont match becuase were only checking day it has to be a new day so update the data
+    if lastrun != str(date.today()):
         # This will update the data store in the data folder
-    import datapuller
+        DataPuller.pull()
         # Update the last run to today
         SetKey("LastRun", str(date.today()))
 
-# Load in the AI algorithm
-from keras.layers import Dense, Flatten, Conv2D
-from keras import Model
+    # Load in the AI algorithm late so yfinance works properly
+    import tensorflow as tf
+    import keras
+    from keras.layers import Dense, Flatten, Conv2D
+    from keras import Model
 
-mnist = keras.datasets.mnist
+    mnist = keras.datasets.mnist
 
-(x_train, y_train), (x_test, y_test) = mnist.load_data()
-x_train, x_test = x_train / 255.0, x_test / 255.0
+    (x_train, y_train), (x_test, y_test) = mnist.load_data()
+    x_train, x_test = x_train / 255.0, x_test / 255.0
 
-# Add a channels dimension
-x_train = x_train[..., tf.newaxis].astype("float32")
-x_test = x_test[..., tf.newaxis].astype("float32")
+    # Add a channels dimension
+    x_train = x_train[..., tf.newaxis].astype("float32")
+    x_test = x_test[..., tf.newaxis].astype("float32")
 
-# batch and shuffle the dataset
-train_ds = tf.data.Dataset.from_tensor_slices(
+    # batch and shuffle the dataset
+    train_ds = tf.data.Dataset.from_tensor_slices(
         (x_train, y_train)).shuffle(10000).batch(32)
 
-test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)
+    test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)
+
+if __name__ == "__main__":
+    main()