diff --git a/parser/__main__.py b/parser/__main__.py
index de668c0..5616d83 100644
--- a/parser/__main__.py
+++ b/parser/__main__.py
@@ -3,11 +3,11 @@ import time
 
 import schedule
 
-from . import pipeline
+from . import pipeline, LenenergoParser
 
 
 def job():
-    parser = pipeline()
+    parser = pipeline(LenenergoParser(file_path="./data.csv"))
     parser.save_df(f'./data_{parser.today.strftime("%d-%m-%y_%H.%M")}.csv')
 
 
diff --git a/parser/address.py b/parser/address.py
index 73f53e4..4239bf7 100644
--- a/parser/address.py
+++ b/parser/address.py
@@ -2,6 +2,7 @@ from __future__ import annotations
 
 import re
 from typing import Iterable, List, TypeVar
+from collections.abc import Sequence
 
 import pandas as pd
 
@@ -146,82 +147,177 @@ def cut_address(ad: pd.Series, cl: str) -> pd.Series:
     return ad
 
 
-# TODO: переработать систему из if в нормальный вид
-def split_address(address: str) -> List[str]:
-    if ";" in address:
-        address = address.replace(";", ",")
-    if "," in address:
-        tokens = address.split(",")
+def is_nonempty_str(string: str) -> bool:
+    return string != ""
 
-        t = list(map(str.strip, filter(lambda token: token != "", tokens)))
 
-        tokens = pd.DataFrame()
-        tokens['obj'] = t
-        tokens = tokens[tokens["obj"] != ""]
-        tokens.insert(len(tokens.columns), "class", "")
-        res = []
-        accumulator = pd.Series(data={"address": "", "class": ""})
+def create_token(obj: str = "", token_class: str = ""):
+    return pd.Series(
+        {
+            "obj": obj,
+            "class": token_class,
+        }
+    )
 
-        for i in range(len(tokens)):
-            cur_tk = tokens.iloc[i]
 
-            if i == 0:
-                pre_token = pd.Series(data=["", ""], index=['obj', 'class'])
-            else:
-                pre_token = tokens.iloc[i - 1]
+class AddressSplitter(Sequence):
+    addresses: list[str]
+    tokens: list[pd.Series]
 
-            cur_tk = address_classification(cur_tk, pre_token)
-            tokens.iloc[i] = cur_tk
-            print(tokens.iloc[i])
+    def __init__(self, address: str):
+        self.input = address
 
-            if not accumulator["class"]:
-                accumulator["class"] = cur_tk['class']
-                accumulator["address"] = cur_tk["obj"]
+        self.addresses = self.split()
+
+        if len(self.addresses) == 0:
+            self.addresses = [address]
+
+    # Sequence abstract methods implementation
+
+    def __getitem__(self, key: int):
+        if key < len(self.addresses):
+            return self.addresses[key]
+        else:
+            raise IndexError()
+
+    def __len__(self):
+        return len(self.addresses)
+
+    # Address token class manipulations
+
+    def next_class(self) -> str:
+        return self.token["class"][0]
+
+    def correct_order(self) -> bool:
+        prev_class = self.accumulator["class"][-1]
+
+        return (
+            CLASSES.index(prev_class) < CLASSES.index(self.next_class())
+            and self.accumulator["class"] != "w"
+        )
+
+    def next_class_is(self, comparing_class: str) -> bool:
+        return len(self.token["class"]) > 0 and self.next_class() == comparing_class[0]
+
+    def pop_token_class(self):
+        self.token["class"] = self.token["class"][1:]
+
+    def has_no_class(self, comparing_class: str) -> bool:
+        return comparing_class[0] not in self.accumulator["class"]
+
+    def next_is_street_or_upper(self) -> bool:
+        is_unknown_class = self.accumulator["class"] in ("", "w")
+
+        return (
+            CLASSES.index(self.next_class()) <= CLASSES.index("s") or is_unknown_class
+        )
+
+    # Accumulator manipulation
+
+    def substitue_house(self) -> str:
+        num = re.findall(r"\d{1,4} ?\/?\d* ?", self.token["obj"])[0]
+
+        return re.sub(r"\d{1,4} ?\/*\d* ?", num, self.accumulator["address"])
+
+    def append_building(self, num: int) -> pd.Series:
+        self.accumulator["class"] += "b"
+        self.accumulator["address"] += "к." + num
+
+        return self.accumulator
+
+    def substitue_building(self, num: int) -> str:
+        return re.sub(r"\d$", num, self.accumulator["address"])
+
+    def insert_building(self):
+        number = re.findall(r"\d", self.token["obj"])[-1]
+
+        if number and self.has_no_class("building"):
+            self.accumulator = self.append_building(number)
+        else:
+            self.accumulator["address"] = self.substitue_building(number)
+
+    def without_letter(self) -> str:
+        return re.sub(r"[А-Яа-я]$", "", self.accumulator["address"].strip())
+
+    def substitue_letter(self, letter: str) -> str:
+        address_without_letter = self.without_letter()
+
+        return address_without_letter + letter
+
+    def insert_letter(self):
+        letter = re.findall(r"[А-Яа-я]", self.token["obj"].strip())[-1]
+        self.accumulator["address"] = self.substitue_letter(letter)
+
+        if letter and self.has_no_class("litera"):
+            self.accumulator["class"] += "l"
+
+    def has_letter_in(self) -> bool:
+        return (
+            re.search(r"\d{1,3}([А-Я]|[а-я])( |$)", self.accumulator["address"])
+            is not None
+        )
+
+    # Data preprocessing
+
+    def split_tokens(self) -> list[pd.Series]:
+        address = self.input.replace(";", ",")
+
+        parts = address.split(",")
+        parts = map(str.strip, parts)
+        parts = filter(is_nonempty_str, parts)
+
+        tokens = map(lambda part: create_token(part, ""), parts)
+
+        return list(tokens)
+
+    def split(self):
+        self.tokens = self.split_tokens()
+
+        result = []
+
+        self.accumulator = pd.Series({"address": "", "class": ""})
+
+        prev_token = create_token()
+
+        for cursor in self.tokens:
+            self.token = address_classification(cursor, prev_token)
+            prev_token = self.token.copy()
+
+            if self.accumulator["class"] == "":
+                self.accumulator = self.token.rename({"obj": "address"})
                 continue
 
-            if CLASSES.index(accumulator["class"][-1]) < CLASSES.index(cur_tk["class"][0]) and accumulator["class"]!="w":
-                accumulator["class"] += cur_tk['class']
-                accumulator["address"] += " " + cur_tk["obj"]
+            if self.correct_order():
+                self.accumulator["address"] += " "
+                self.accumulator += self.token.rename({"obj": "address"})
             else:
-                ad_no_ranges = unfold_house_ranges(accumulator["address"])
-                accumulator["address"] = ad_no_ranges[-1]
+                unfolded_address = unfold_house_ranges(self.accumulator["address"])
+                self.accumulator["address"] = unfolded_address[-1]
 
-                res.extend(ad_no_ranges)
+                result.extend(unfolded_address)
 
-                accumulator = cut_address(accumulator, cur_tk["class"])
+                self.accumulator = cut_address(self.accumulator, self.token["class"])
 
-                if not accumulator["class"] or CLASSES.index(cur_tk["class"][0]) <= CLASSES.index("s") or accumulator["class"]=="w":
-                    accumulator["class"] = cur_tk["class"]
-                    accumulator["address"] = cur_tk["obj"]
+                if self.next_is_street_or_upper():
+                    self.accumulator = self.token.rename({"obj": "address"})
 
-                if cur_tk["class"][0] == "h":
-                    num = re.findall("\d{1,4} ?\/?\d* ?", cur_tk['obj'])[0]
-                    accumulator["address"] = re.sub(r"\d{1,4} ?\/*\d* ?", num, accumulator["address"])
-                    cur_tk["class"] =cur_tk["class"][1:]
+                if self.next_class_is("house"):
+                    self.accumulator["address"] = self.substitue_house()
+                    self.pop_token_class()
 
-                if cur_tk["class"] and cur_tk["class"][0] == "b":
-                    num = re.findall("\d", cur_tk["obj"])[-1]
-                    if num and not "b" in accumulator["class"]:
-                        accumulator["class"] += "b"
-                        accumulator["address"] += "к." + num
-                    else:
-                        accumulator["address"] = re.sub(r"\d$", num, accumulator["address"])
-                    cur_tk["class"] = cur_tk["class"][1:]
+                if self.next_class_is("building"):
+                    self.insert_building()
+                    self.pop_token_class()
 
-                if cur_tk["class"] and cur_tk["class"][0] == "l":
-                    num = re.findall("[А-Яа-я]", cur_tk["obj"].strip())[-1]
-                    accumulator["address"] = re.sub(r"[А-Яа-я]$", "", accumulator["address"].strip())
-                    accumulator["address"] += num
-                    if num and not "l" in accumulator["class"]:
-                        accumulator["class"] += "l"
-                else:
-                    if re.search(r"\d{1,3}([А-Я]|[а-я])( |$)",  accumulator["address"]):
-                        accumulator["address"] = re.sub(r"[А-Яа-я]$", "", accumulator["address"].strip())
-        res.extend(unfold_house_ranges(accumulator["address"]))
-        print(res)
-        return res
+                if self.next_class_is("letter"):
+                    self.insert_letter()
+                elif self.has_letter_in():
+                    self.accumulator["address"] = self.without_letter()
+
+        result.extend(unfold_house_ranges(self.accumulator["address"]))
+
+        return result
 
-    return [address]
 
 def split_pesoch_res(address: str) -> List[str]:
     t = re.sub(r",", " ", address)
@@ -242,7 +338,7 @@ def process_row(row: pd.Series[str]) -> pd.Series[str]:
         if row["РЭС"] == "Песочинский РЭС":
             addresses = split_pesoch_res(row["Улица"])
         else:
-            addresses = split_address(row["Улица"])
+            addresses = AddressSplitter(row["Улица"])
         row["Улица"] = addresses
 
     return row
diff --git a/parser/util.py b/parser/util.py
index 983c9b8..9fad768 100644
--- a/parser/util.py
+++ b/parser/util.py
@@ -10,7 +10,7 @@ from . import (
 
 def pipeline(parser: Optional[LenenergoParser] = None) -> LenenergoParser:
     if parser is None:
-        parser = LenenergoParser(file_path = r"C:\Users\Юля\PycharmProjects\machine_learning\lenengro_parser\data_Rosseti.csv")
+        parser = LenenergoParser(parser)
 
     print(parser)