Абалдеть, как будто бы сделал фулл

genetic_algorithm/genetic_algorithm.py

@@ -89,7 +89,7 @@ def path_length_and_time(path, graph):
             return float('inf'), start_time, end_time
         length += graph[path[i]][path[i + 1]][0]
         if i > 0:  # Пропуск первой вершины
-            if end_time + timedelta(minutes=5) > graph[path[i]][path[i + 1]][1]:
+            if end_time + timedelta(minutes=180) > graph[path[i]][path[i + 1]][1]:
                 return float('inf'), start_time, end_time  # Неприемлемый путь
         end_time = graph[path[i]][path[i + 1]][2]
     return length, start_time, end_time

main.py

@@ -6,6 +6,7 @@ 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
+from router_reviews import router as reviews_router
 
 # Настройка логирования
 logging.basicConfig(level=logging.INFO)
@@ -24,4 +25,5 @@ app = FastAPI(lifespan=lifespan)
 app.include_router(questions_router)
 app.include_router(class_router)
 app.include_router(flight_router)
+app.include_router(reviews_router)
 

router_class.py

@@ -1,6 +1,9 @@
+import pandas as pd
 from fastapi import APIRouter
 from typing import List
 
+from enums import TypeMood
+from schemas import Review
 
 router = APIRouter(
     prefix="/class",
@@ -19,3 +22,6 @@ async def get_class_names() -> List[str]:
     with open(".//neural_network/classification/class_names_positive.txt", "r", encoding="utf-8") as file:
         class_names = [line.strip() for line in file.readlines()]
     return class_names
+
+
+

router_reviews.py

@@ -0,0 +1,28 @@
+import pandas as pd
+from fastapi import APIRouter
+from typing import List
+
+from enums import TypeMood
+from schemas import Review
+
+router = APIRouter(
+    prefix="/reviews",
+    tags=["Reviews"],
+)
+
+@router.get("/{rubrics}")
+def get_reviews(rubrics: str,
+    type_mood: TypeMood) -> list[Review]:
+    if type_mood == TypeMood.NEGATIVE:
+        df = pd.read_csv("./sity/sity_negative.csv")
+    else:
+        df = pd.read_csv("./sity/sity_positive.csv")
+    df = df.fillna("")
+    filtered_df = df[df["rubrics"] == rubrics]
+    shuffled_df = filtered_df.sample(frac=1)
+    sorted_df = shuffled_df.sort_values(by="rating", ascending=False)
+    unique_reviews = sorted_df.drop_duplicates(subset="city")
+    unique_reviews = unique_reviews.head(10).to_dict(orient="records")
+    return unique_reviews
+
+

schemas.py

@@ -39,3 +39,8 @@ class TripRequest(BaseModel):
     returnDate: Optional[str] = None
     flightsTo: List[Flight]
     flightsBack: List[Flight]
+
+class Review(BaseModel):
+    city: str
+    address: str
+    name_ru: str