Фух, добавил файлы по аэропртам, плюс роутеры и прочее и прочее

main.py

@@ -5,6 +5,7 @@ from contextlib import asynccontextmanager
 from database import create_tables, delete_tables
 from router_questions import router as questions_router
 from router_class import router as class_router
+from router_flight import router as flight_router
 
 # Настройка логирования
 logging.basicConfig(level=logging.INFO)
@@ -22,3 +23,5 @@ async def lifespan(app: FastAPI):
 app = FastAPI(lifespan=lifespan)
 app.include_router(questions_router)
 app.include_router(class_router)
+app.include_router(flight_router)
+

neural_network/dataset/sortTest.py

@@ -1,51 +0,0 @@
-import json
-import pandas as pd
-
-# JSON данные
-json_data = '''
-[
-  { "value": "AAQ", "label": "Анапа, Витязево, AAQ", "timezone": "+3", "latitude": 45.0029, "longitude": 37.3473 },
-  { "value": "ARH", "label": "Архангельск, Талаги, ARH", "timezone": "+3", "latitude": 64.6003, "longitude": 40.7168 },
-  { "value": "ASF", "label": "Астрахань, им. Б.Н. Кустодиева, ASF", "timezone": "+4", "latitude": 46.2833, "longitude": 48.0063 },
-  { "value": "BAX", "label": "Барнаул, Михайловка, BAX", "timezone": "+7", "latitude": 53.3638, "longitude": 83.5385 }
-]
-'''
-
-data = json.loads(json_data)
-
-# Извлекаем первое слово перед запятой и первое слово после запятой
-cities_set = set()
-for entry in data:
-    parts = entry['label'].split(',')
-    if len(parts) > 1:
-        city1 = parts[0].strip().split()[0] if parts[0].strip().split() else ''
-        city2 = parts[1].strip().split()[0] if parts[1].strip().split() else ''
-        cities_set.update([city1, city2])
-
-# Прочитаем CSV файл
-csv_file_path = 'geo-reviews-dataset-2023.csv'
-df = pd.read_csv(csv_file_path)
-
-# Функция для проверки совпадения города в адресе
-def find_city(address, cities_set):
-    parts = address.split(',')
-    if len(parts) > 1:
-        word1 = parts[0].strip().split()[0] if parts[0].strip().split() else ''
-        word2 = parts[1].strip().split()[0] if parts[1].strip().split() else ''
-        if word1 in cities_set:
-            return word1
-        elif word2 in cities_set:
-            return word2
-    return None
-
-# Добавим новый столбец на основе первого слова из адреса
-df['city'] = df['address'].apply(lambda x: find_city(x, cities_set))
-
-# Оставим только те строки, где город из CSV файла совпадает с городом из JSON
-df_filtered = df[df['city'].notnull()]
-
-# Сохраним отсортированный DataFrame обратно в CSV
-output_file_path = 'sorted_filtered_geo-reviews-dataset-2023.csv'
-df_filtered.to_csv(output_file_path, index=False)
-
-print(df_filtered[:15])

router_class.py

@@ -1,41 +1,12 @@
-from typing import Annotated
+from fastapi import APIRouter
-from fastapi import APIRouter, Depends, HTTPException
 from typing import List
 
-from pydantic import ValidationError
-
-from enums import TypeMood, TypeModel
-from genetic_algorithm.genetic_algorithm import genetic_algorithm, load_graph_from_request
-from repository import QuestionRepository
-from schemas import SQuestionAdd, SQuestion, SQuestionId, Flight, TripRequest
 
 router = APIRouter(
     prefix="/class",
     tags=["Class"],
 )
 
-
-@router.post("/get_flight/")
-async def get_flight(request: TripRequest):
-    graphTo, graphBack, start_point, end_point, flightsDataTo, flightsDataBack, departure_date, departure_date_return = load_graph_from_request(request)
-    if request.returnDate:
-        resultTo = genetic_algorithm(start=start_point, end=end_point, graph=graphTo, flights_data=flightsDataTo, type = "to", departure_date = departure_date)
-        resulFrom = genetic_algorithm(end=start_point, start=end_point, graph=graphBack, flights_data=flightsDataBack, type = "back", departure_date = departure_date_return)
-        if not resultTo or not resulFrom:
-            raise HTTPException(status_code=404, detail="No valid paths found")
-        return resultTo, resulFrom
-    if not request.returnDate:
-        resultTo = genetic_algorithm(start=start_point, end=end_point, graph=graphTo, flights_data=flightsDataTo,
-                                     type="to", departure_date = departure_date)
-        resultTo.append({
-            "back": []
-        })
-
-        if not resultTo:
-            raise HTTPException(status_code=404, detail="No valid paths found")
-        return resultTo
-
-
 @router.get("/negative")
 async def get_class_names() -> List[str]:
     with open(".//neural_network/classification/class_names_negative.txt", "r", encoding="utf-8") as file:

router_flight.py

@@ -0,0 +1,31 @@
+from fastapi import APIRouter, HTTPException
+
+from genetic_algorithm.genetic_algorithm import genetic_algorithm, load_graph_from_request
+from schemas import TripRequest
+
+router = APIRouter(
+    prefix="/flight",
+    tags=["Flight"],
+)
+
+
+@router.post("/get/")
+async def get_flight(request: TripRequest):
+    graphTo, graphBack, start_point, end_point, flightsDataTo, flightsDataBack, departure_date, departure_date_return = load_graph_from_request(request)
+    if request.returnDate:
+        resultTo = genetic_algorithm(start=start_point, end=end_point, graph=graphTo, flights_data=flightsDataTo, type = "to", departure_date = departure_date)
+        resulFrom = genetic_algorithm(end=start_point, start=end_point, graph=graphBack, flights_data=flightsDataBack, type = "back", departure_date = departure_date_return)
+        if not resultTo or not resulFrom:
+            raise HTTPException(status_code=404, detail="No valid paths found")
+        return resultTo, resulFrom
+    if not request.returnDate:
+        resultTo = genetic_algorithm(start=start_point, end=end_point, graph=graphTo, flights_data=flightsDataTo,
+                                     type="to", departure_date = departure_date)
+
+        if resultTo:
+            resultTo.append({
+                "back": []
+            })
+        else:
+            raise HTTPException(status_code=404, detail="No valid paths found")
+        return resultTo

router_questions.py

@@ -11,7 +11,7 @@ router = APIRouter(
     tags=["Questions"],
 )
 
-@router.post("")
+@router.post("/add")
 async def add_question(
     question: Annotated[SQuestionAdd, Depends()],
     type_mood: TypeMood,  # Добавлен параметр type_mood
@@ -20,13 +20,13 @@ async def add_question(
     question_id, answer = await QuestionRepository.add_one(question, type_mood, type_model)  # Передача параметров type_mood и type_model
     return {"question_id": question_id, "answer": answer}
 
-@router.get("")
+@router.get("/get_all")
 async def get_questions() -> list[SQuestion]:
     questions = await QuestionRepository.find_all()
     return questions
 
 
-@router.get("/{email_user}")
+@router.get("/get/{email_user}")
 async def get_questions_by_email(email_user: str) -> list[SQuestion]:
     questions = await QuestionRepository.find_by_email(email_user)
     return questions

sity/airports.json

@@ -0,0 +1,51 @@
+[
+  { "value": "ABA", "label": "Абакан, Абакан, ABA", "timezone": "+7", "latitude": 53.7400, "longitude": 91.3850 },
+  { "value": "AAQ", "label": "Анапа, Витязево, AAQ", "timezone": "+3", "latitude": 45.0029, "longitude": 37.3473 },
+  { "value": "ARH", "label": "Архангельск, Талаги, ARH", "timezone": "+3", "latitude": 64.6003, "longitude": 40.7168 },
+  { "value": "ASF", "label": "Астрахань, им. Б.Н. Кустодиева, ASF", "timezone": "+4", "latitude": 46.2833, "longitude": 48.0063 },
+  { "value": "BAX", "label": "Барнаул, Михайловка, BAX", "timezone": "+7", "latitude": 53.3638, "longitude": 83.5385 },
+  { "value": "EGO", "label": "Белгород, им. В.Г. Шухова, EGO", "timezone": "+3", "latitude": 50.6439, "longitude": 36.5901 },
+  { "value": "BQS", "label": "Благовещенск, Игнатьево, BQS", "timezone": "+9", "latitude": 50.4250, "longitude": 127.4125 },
+  { "value": "BTK", "label": "Братск, Братск, BTK", "timezone": "+8", "latitude": 56.3706, "longitude": 101.6983 },
+  { "value": "BZK", "label": "Брянск, Брянск, BZK", "timezone": "+3", "latitude": 53.2142, "longitude": 34.1769 },
+  { "value": "VVO", "label": "Владивосток, Кневичи, VVO", "timezone": "+10", "latitude": 43.3989, "longitude": 132.1478 },
+  { "value": "VOZ", "label": "Воронеж, Чертовицкое, VOZ", "timezone": "+3", "latitude": 51.8142, "longitude": 39.2296 },
+  { "value": "YKS", "label": "Якутск, Якутск, YKS", "timezone": "+9", "latitude": 62.0933, "longitude": 129.7708 },
+  { "value": "SVX", "label": "Екатеринбург, Кольцово, SVX", "timezone": "+5", "latitude": 56.7431, "longitude": 60.8027 },
+  { "value": "KZN", "label": "Казань, Казань, KZN", "timezone": "+3", "latitude": 55.6062, "longitude": 49.2787 },
+  { "value": "KUF", "label": "Самара, Курумоч, KUF", "timezone": "+4", "latitude": 53.5049, "longitude": 50.1643 },
+  { "value": "IKT", "label": "Иркутск, Иркутск, IKT", "timezone": "+8", "latitude": 52.2680, "longitude": 104.3880 },
+  { "value": "KGD", "label": "Калининград, Храброво, KGD", "timezone": "+2", "latitude": 54.8900, "longitude": 20.5926 },
+  { "value": "KEJ", "label": "Кемерово, Кемерово, KEJ", "timezone": "+7", "latitude": 55.2700, "longitude": 86.1072 },
+  { "value": "KRR", "label": "Краснодар, Пашковский, KRR", "timezone": "+3", "latitude": 45.0347, "longitude": 39.1705 },
+  { "value": "KJA", "label": "Красноярск, Емельяново, KJA", "timezone": "+7", "latitude": 56.1729, "longitude": 92.4933 },
+  { "value": "MCX", "label": "Махачкала, Уйташ, MCX", "timezone": "+3", "latitude": 42.8168, "longitude": 47.6523 },
+  { "value": "MRV", "label": "Минеральные Воды, Минеральные Воды, MRV", "timezone": "+3", "latitude": 44.2251, "longitude": 43.0819 },
+  { "value": "NBC", "label": "Набережные Челны, Бегишево, NBC", "timezone": "+3", "latitude": 55.5647, "longitude": 52.0938 },
+  { "value": "NNM", "label": "Нарьян-Мар, Нарьян-Мар, NNM", "timezone": "+3", "latitude": 67.6392, "longitude": 53.1219 },
+  { "value": "NJC", "label": "Нижневартовск, Нижневартовск, NJC", "timezone": "+5", "latitude": 60.9493, "longitude": 76.4836 },
+  { "value": "GOJ", "label": "Нижний Новгород, Стригино, GOJ", "timezone": "+3", "latitude": 56.2301, "longitude": 43.7840 },
+  { "value": "NOJ", "label": "Ноябрьск, Ноябрьск, NOJ", "timezone": "+5", "latitude": 63.1101, "longitude": 75.1624 },
+  { "value": "NSK", "label": "Норильск, Алыкель, NSK", "timezone": "+7", "latitude": 69.3111, "longitude": 87.3322 },
+  { "value": "OMS", "label": "Омск, Центральный, OMS", "timezone": "+6", "latitude": 54.9660, "longitude": 73.3105 },
+  { "value": "REN", "label": "Оренбург, Оренбург, REN", "timezone": "+5", "latitude": 51.7958, "longitude": 55.4567 },
+  { "value": "OSW", "label": "Орск, Орск, OSW", "timezone": "+5", "latitude": 51.0725, "longitude": 58.5956 },
+  { "value": "PEE", "label": "Пермь, Большое Савино, PEE", "timezone": "+5", "latitude": 57.9145, "longitude": 56.0212 },
+  { "value": "PKC", "label": "Петропавловск-Камчатский, Елизово, PKC", "timezone": "+12", "latitude": 53.1679, "longitude": 158.4536 },
+  { "value": "PES", "label": "Петрозаводск, Бесовец, PES", "timezone": "+3", "latitude": 61.8852, "longitude": 34.1547 },
+  { "value": "PYJ", "label": "Полярный, Полярный, PYJ", "timezone": "+9", "latitude": 66.4000, "longitude": 112.0333 },
+  { "value": "ROV", "label": "Ростов-на-Дону, Платов, ROV", "timezone": "+3", "latitude": 47.4939, "longitude": 39.9242 },
+  { "value": "RTW", "label": "Саратов, Гагарин, RTW", "timezone": "+4", "latitude": 51.5643, "longitude": 46.0468 },
+  { "value": "AER", "label": "Сочи, Адлер, AER", "timezone": "+3", "latitude": 43.4499, "longitude": 39.9566 },
+  { "value": "SGC", "label": "Сургут, Сургут, SGC", "timezone": "+5", "latitude": 61.3437, "longitude": 73.4010 },
+  { "value": "THX", "label": "Тюмень, Рощино, THX", "timezone": "+5", "latitude": 57.1896, "longitude": 65.3243 },
+  { "value": "UFA", "label": "Уфа, Уфа, UFA", "timezone": "+5", "latitude": 54.5575, "longitude": 55.8744 },
+  { "value": "UCT", "label": "Ухта, Ухта, UCT", "timezone": "+4", "latitude": 63.5668, "longitude": 53.8047 },
+  { "value": "ULY", "label": "Ульяновск, Баратаевка, ULY", "timezone": "+4", "latitude": 54.2683, "longitude": 48.2269 },
+  { "value": "VOG", "label": "Волгоград, Гумрак, VOG", "timezone": "+3", "latitude": 48.7825, "longitude": 44.3456 },
+  { "value": "VKO", "label": "Москва, Внуково, VKO", "timezone": "+3", "latitude": 55.5915, "longitude": 37.2615 },
+  { "value": "SVO", "label": "Москва, Шереметьево, SVO", "timezone": "+3", "latitude": 55.9726, "longitude": 37.4146 },
+  { "value": "DME", "label": "Москва, Домодедово, DME", "timezone": "+3", "latitude": 55.4103, "longitude": 37.9021 },
+  { "value": "LED", "label": "Санкт-Петербург, Пулково, LED", "timezone": "+3", "latitude": 59.8003, "longitude": 30.2625 },
+  { "value": "CEK", "label": "Челябинск, Баландино, CEK", "timezone": "+5", "latitude": 55.3058, "longitude": 61.5033 }
+]

sity/conversion.py

@@ -0,0 +1,53 @@
+import json
+import pandas as pd
+
+class CityFilter:
+    def __init__(self, json_file_path):
+        self.json_file_path = json_file_path
+        self.cities_set = self.load_and_extract_cities()
+
+    def load_and_extract_cities(self):
+        data = self.load_json(self.json_file_path)
+        cities_set = set()
+        for entry in data:
+            parts = entry['label'].split(',')
+            if len(parts) > 1:
+                city1 = parts[0].strip().split()[0] if parts[0].strip().split() else ''
+                city2 = parts[1].strip().split()[0] if parts[1].strip().split() else ''
+                cities_set.update([city1, city2])
+        return cities_set
+
+    @staticmethod
+    def load_json(file_path):
+        with open(file_path, 'r', encoding='utf-8') as file:
+            data = json.load(file)
+        return data
+
+    @staticmethod
+    def find_city(address, cities_set):
+        parts = address.split(',')
+        for part in parts:
+            words = part.strip().split()
+            for word in words:
+                if word in cities_set:
+                    return word
+        return None
+
+    def filter_cities_in_csv(self, csv_file_path, output_path):
+        df = pd.read_csv(csv_file_path)
+        df['city'] = df['address'].apply(lambda x: self.find_city(x, self.cities_set))
+        df = df[df['city'].notnull()]
+        df.to_csv(output_path, index=False)
+        print(f"Filtered entries:\n{df.head(15)}")
+
+
+# Пример использования класса
+json_file_path = 'airports.json'
+csv_file_path_positive = '../neural_network/dataset/filtered/filtered_dataset_positive.csv'
+csv_file_path_negative = '../neural_network/dataset/filtered/filtered_dataset_negative.csv'
+positive_output_path_negative = '../sity/sity_negative.csv'
+negative_output_path_positive = '../sity/sity_positive.csv'
+
+city_filter = CityFilter(json_file_path)
+city_filter.filter_cities_in_csv(csv_file_path_positive, negative_output_path_positive)
+city_filter.filter_cities_in_csv(csv_file_path_negative, positive_output_path_negative)