diff --git a/FeatureEngineering/DB_FeatureEngineering1.ipynb b/FeatureEngineering/DB_FeatureEngineering1.ipynb
old mode 100644
new mode 100755
index 5d2b8ea..4d428a4
--- a/FeatureEngineering/DB_FeatureEngineering1.ipynb
+++ b/FeatureEngineering/DB_FeatureEngineering1.ipynb
@@ -2,10 +2,21 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "756b4af0",
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'tensorflow'",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_85224/4219686290.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmath\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msqrt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[1;31m# Keras specific\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'"
+     ],
+     "output_type": "error"
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -31,7 +42,6 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "3dc2fae7",
    "metadata": {
     "scrolled": true
    },
@@ -42,22 +52,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "424466af",
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
     "#load data from data files and place into panda dataframes\n",
-    "custOrders = pd.read_csv('data/customer_orders.csv', low_memory=False)\n",
-    "temp2 = pd.read_csv('data/item_ingredients.csv', low_memory=False)\n",
-    "\n",
+    "try:\n",
+    "    custOrders = pd.read_csv('data/customer_orders.csv', low_memory=False)\n",
+    "    temp2 = pd.read_csv('data/item_ingredients.csv', low_memory=False)\n",
+    "except FileNotFoundError:\n",
+    "    c_order_url = \"https://raw.githubusercontent.com/GottaGitGoing/cs180_2/main/FeatureEngineering/data/customer_orders.csv\"\n",
+    "    ingred_url = \"https://raw.githubusercontent.com/GottaGitGoing/cs180_2/main/FeatureEngineering/data/item_ingredients.csv\"\n",
+    "    custOrders = pd.read_csv(c_order_url, low_memory=False)\n",
+    "    temp2 = pd.read_csv(ingred_url, low_memory=False)\n",
     "#temp2.head(10)\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "930462cc",
    "metadata": {},
    "outputs": [
     {
@@ -82,7 +95,6 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "a397d1f3",
    "metadata": {},
    "outputs": [
     {
@@ -123,7 +135,6 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "bcb78d34",
    "metadata": {
     "scrolled": true
    },
@@ -148,7 +159,6 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "51657c7f",
    "metadata": {
     "scrolled": true
    },
@@ -336,7 +346,6 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "41d402ed",
    "metadata": {},
    "outputs": [
     {
@@ -520,7 +529,6 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "1e4230cc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -545,7 +553,6 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "c1f14dd0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -571,7 +578,6 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "ee755b25",
    "metadata": {
     "scrolled": true
    },
@@ -988,7 +994,6 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "471ddcc9",
    "metadata": {},
    "outputs": [
     {
@@ -1010,7 +1015,6 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "69e86727",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1037,7 +1041,6 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "c1e36854",
    "metadata": {
     "scrolled": true
    },
@@ -1331,7 +1334,6 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "556b6059",
    "metadata": {
     "scrolled": true
    },
@@ -1506,7 +1508,6 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "98c25c78",
    "metadata": {},
    "outputs": [
     {
@@ -1555,7 +1556,6 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "736b10a3",
    "metadata": {},
    "outputs": [
     {
@@ -1914,7 +1914,6 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "85955bdd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1939,7 +1938,6 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "a0f9ecd7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1953,7 +1951,6 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "cf656e7d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1968,7 +1965,6 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "eb8a4c34",
    "metadata": {},
    "outputs": [
     {
@@ -1993,7 +1989,6 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "dfb6d45e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2004,7 +1999,6 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "4ac99c03",
    "metadata": {},
    "outputs": [
     {
@@ -2025,7 +2019,6 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "4186cf86",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2041,7 +2034,6 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "26e33f9a",
    "metadata": {
     "scrolled": true
    },
@@ -2280,7 +2272,6 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "f4247c08",
    "metadata": {},
    "outputs": [
     {
@@ -2302,7 +2293,6 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "69297008",
    "metadata": {},
    "outputs": [
     {
@@ -2332,7 +2322,6 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3aeeb1f8",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/SQL/CSVs/itemrestrictions.csv b/SQL/CSVs/itemrestrictions.csv
new file mode 100755
index 0000000..f6d4c91
--- /dev/null
+++ b/SQL/CSVs/itemrestrictions.csv
@@ -0,0 +1,22 @@
+item_id,dairy,nuts,vegan,vegetarian,kosher,halal
+640404923,0,0,0,0,0,0
+640404963,0,0,0,0,0,0
+640405025,0,0,0,0,0,0
+640405058,0,1,1,1,0,0
+640405085,0,0,0,0,0,0
+640405112,0,0,0,0,0,0
+640405172,0,0,0,0,0,0
+640405355,0,0,1,1,1,1
+640405371,0,0,1,1,1,1
+640405380,0,0,1,1,1,1
+640405389,1,1,0,0,1,1
+640405395,1,1,0,0,1,1
+640405399,1,1,0,0,1,1
+640405296,0,0,1,1,1,1
+640405307,0,0,1,1,1,1
+640405315,0,0,1,1,1,1
+640405323,0,0,1,1,1,1
+640405331,0,0,1,1,1,1
+640405339,0,0,1,1,1,1
+640405347,0,0,1,1,1,1
+640405348,0,0,1,1,1,1
\ No newline at end of file
diff --git a/SQL/csv_generator.py b/SQL/csv_generator.py
old mode 100644
new mode 100755
index 6809a3f..253677d
--- a/SQL/csv_generator.py
+++ b/SQL/csv_generator.py
@@ -1,6 +1,6 @@
 from collections import defaultdict
 import random
-
+import csv
 
 NUM_CUSTOMERS = 200
 NUM_ORDERS = 2000
@@ -8,6 +8,8 @@
 
 
 def ingredient():
+    '''Populates ingredient.csv with ingredient_id and ingredient_name
+    given the ingredient_types declared here. Returns csv content as dict().'''
     columns = ['ingredient_id', 'ingredient_name']
     ingredient_types = ['ketchup',
         'mustard',
@@ -43,6 +45,8 @@ def ingredient():
 
 
 def itemingredient(ingredients_dict):
+    '''Populates itemingredient.csv with item_id and ingredient_id
+        given the ingredients for each item.'''
     columns = ['item_id', 'ingredient_id']
     items = {
         640404923: "Ketchup, Mustard, Mayonnaise, Lettuce, Pickles, Onions, Tomatoes",
@@ -79,6 +83,9 @@ def itemingredient(ingredients_dict):
 
 
 def menu():
+    '''Currently assigns menu_id to each item_id with all the
+    same restaurant brand and store_id. (i.e. All items belong
+    to the same store.) Populates menu.csv.'''
     columns = ['menu_id', 'item_id', 'brand', 'store_id']
     item_ids = [
         640404923,
@@ -112,6 +119,10 @@ def menu():
 
 
 def customer():
+    '''Generates {NUM_CUSTOMERS} customers with random attributes except
+    everyone has opt_in = true because we have this information only for
+    opt_in folks as of now. Populates customer.csv.'''
+
     columns = ['customer_id', 'opt_in', 'birthday', 'gender']
     genders = [
         'male',
@@ -128,6 +139,8 @@ def customer():
 
 
 def order():
+    '''Generates {NUM_ORDERS} orders with order_id, a random customer_id,
+    a random timestamp between 2015 and 2020, and weather chosen from a list.'''
     columns = ['order_id', 'customer_id', 'order_timestamp', 'weather']
     weather = [
         'sunny',
@@ -147,6 +160,8 @@ def order():
 
 
 def orderitem():
+    '''Associates random item_id for each order_id.'''
+
     columns = ['order_id', 'item_id']
     item_ids = [
         640404923,
@@ -178,14 +193,102 @@ def orderitem():
         file.write(','.join(columns) + '\n')
 
         for id in range(1, NUM_ORDERS + 1):
-            item_id = random.choice(item_ids)
+            # here check if item id fits the preference
+            # id is order_id
+            c_id = get_customer_id_from_order(id) # need c_id because we need to check the customers preference
+            # now we have customer_id associated with the orderitem. check the
+            # customers restrictions and run random.choice until a valid item is
+            # picked. (or create a separate list that
+            cust_diet_restriction = get_customer_diet_restr(c_id)
+            while True:
+                item_id = random.choice(item_ids)
+                restriction_list = get_item_restrictions(item_id)
+                    # now check restriction list returns and see if it contains
+                    # any of the customer diet restrictions
+                    # NOTE: the list returned will be format
+                    # item_id,dairy,nuts,vegan,vegetarian,kosher,halal
+
+                # GOAL: using restriction_list and cust_diet_restriction
+                # see if the random choice is appropriate. if not appropriate
+                # randomly choose again.
+                rest_map = {"dairy":1,"nuts":2,"vegan":3,
+                            "vegetarian":4,"kosher":5,"halal":6}
+                for pref_id,pref in cust_diet_restriction.items():
+                    if restriction_list[pref] == 1:
+                        # something something
+
+
+                rest_comp_list = ['0', '0', '0', '0', '0', '0']
+
+
             file.write(f'{id},{item_id}\n')
             orders[item_id].append(id)
 
     return orders
 
 
+def get_item_restrictions(item_id:int):
+    '''Given an item_id, it will return a list of restrictions applicable to
+    the specfic item_id.'''
+    with open("CSVs/itemrestrictions.csv", "r") as REST_FILE:
+        data = csv.reader(REST_FILE)
+        # calling next so the data iterator skips the header.
+        # Better than try except imo.
+        next(data)
+        for itm in data:
+            if int(itm[0]) == item_id:
+                return itm
+    return -1 # to indicate not item_id found
+
+
+def get_customer_diet_restr(cust_id: int):
+    '''Returns a dictionary of dietary id:preference of a given customer. If the
+    customer does not exist, or no preference found,
+    then empty dictionary is returned'''
+
+    # loop thru custdiepref.csv and returns
+    # the dietary pref id and actual pref txt
+    preference_id_set = set() # list since cust could be halal and vegan
+    with open("CSVs/customerdietarypreference.csv", "r") as FILE:
+        data = csv.reader(FILE)
+        for i in data:
+            try:
+                if int(i[0]) == cust_id:
+                    print(i[1])
+                    preference_id_set.add(i[1])
+                    # NOTE MIGHT NEED to typecast to int
+            except ValueError: # reasoning is checking str vs int might fail
+                pass
+
+    # now you have the set of preference for a customer. make a dict of
+    # preference_id and preference
+    pref_id_name = dict()
+    if len(preference_id_set) != 0:
+        with open("CSVs/dietarypreference.csv", "r") as FILE:
+            data = csv.reader(FILE)
+            pref_id_name = {id:pref_name for id,pref_name,weight in data
+                            if id in preference_id_set}
+    return pref_id_name  # returns dict of pref_id's and their name of pref
+
+
+def get_customer_id_from_order(id: int):
+    ''' returns c_id by searching through order.csv.  '''
+    with open("CSVs/order.csv", "r") as FILE:
+        data = csv.reader(FILE)
+        # order id 7. expeects 171 back
+        for i in data:
+            try:
+                if int(i[0]) == id:
+                    return i[1]
+            except ValueError:
+                pass # this is because i havent skipped the csv header so
+                # this will just "ignore" the title since strings cant be ints
+
 def customization():
+    '''Menu items with customizable features (ingredients that
+    can be taken out) propagate customization.csv which each
+    menu item - customization pair. (E.g. burger_type1 with no ketchup
+    could be a row and burger_type1 with no mustard could be another.'''
     columns = ['customization_id', 'item_id', 'customization']
     customizations = [
         'no ketchup',
@@ -236,6 +339,8 @@ def customization():
 
 
 def orderitemcustomization(orders, customization_dict):
+    '''Pairs {NUM_ORDER_ITEM_CUSTOMIZATIONS} random orders with customizations
+    where the item_id matches.'''
     columns = ['order_id', 'item_id', 'customization_id']
     order_item_customizations = set()
 
@@ -255,7 +360,10 @@ def orderitemcustomization(orders, customization_dict):
             file.write(f'{row[0]},{row[1]},{row[2]}\n')
 
 
+
 def dietarypreference():
+    '''For each preference, a weight of 1 - 5 is assigned as a pair (not random,
+    so total rows = len(preferences)*5.'''
     columns = ['preference_id', 'preference', 'preference_weight']
     preferences = [
         'dairy',
@@ -282,6 +390,8 @@ def dietarypreference():
 
 
 def customerdietarypreference(preference_weight_pairs):
+    '''Every few customers will have a preference attached to them.'''
+
     columns = ['customer_id', 'preference_id']
     customer_dietary_preference_pairs = set()
 
@@ -296,13 +406,20 @@ def customerdietarypreference(preference_weight_pairs):
 
 
 if __name__ == '__main__':
-    ingredients_dict = ingredient()
-    itemingredient(ingredients_dict)
-    menu()
-    customer()
-    order()
-    orders = orderitem()
-    customization_dict = customization()
-    orderitemcustomization(orders, customization_dict)
-    preference_weight_pairs = dietarypreference()
-    customerdietarypreference(preference_weight_pairs)
+    # ingredients_dict = ingredient()
+    # itemingredient(ingredients_dict)
+    # menu()
+    # customer()
+    # order()
+    # preference_weight_pairs = dietarypreference()
+    # customerdietarypreference(preference_weight_pairs)
+    # orders = orderitem()
+    # customization_dict = customization()
+    # orderitemcustomization(orders, customization_dict)
+
+
+    ## TESTER!!
+    print(get_customer_diet_restr(23))
+    # get_customer_id_from_order(7)
+    print(get_item_restrictions(640404923))
+