app_api.py

''' Для cmd
curl -X POST http://127.0.0.1:5000/predict_model -H "Content-Type: application/json" -d "{\"text\":\"Я хуйня\"}"
'''
from fastapi import FastAPI, Request, HTTPException
import dill
import pandas as pd
from pydantic import BaseModel

from fastapi.staticfiles import StaticFiles
from fastapi.templating import Jinja2Templates
from fastapi.responses import HTMLResponse

import nltk
import string
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import SnowballStemmer
nltk.download('punkt')
nltk.download('punkt_tab')
nltk.download('stopwords')


app = FastAPI()

# Настройка статических файлов (CSS/JS)
app.mount("/static", StaticFiles(directory="static"), name="static")

# Настройка шаблонов Jinja2 (HTML)
templates = Jinja2Templates(directory="templates")

snowball = SnowballStemmer(language="russian")
russian_stop_words = stopwords.words("russian")

def tokenize_sentence(sentence: str, remove_stop_words: bool = True):
    tokens = word_tokenize(sentence, language="russian")
    tokens = [i for i in tokens if i not in string.punctuation]
    if remove_stop_words:
        tokens = [i for i in tokens if i not in russian_stop_words]
    tokens = [snowball.stem(i) for i in tokens]
    return tokens

# Загрузка модели (с проверкой)
with open('model.pkl', 'rb') as f:
    model = dill.load(f)


# Глобальный счетчик запросов
request_count = 0

# Модель для валидации входных данных
class PredictionInput(BaseModel):
    text: str  # Текст для классификации

@app.get("/", response_class=HTMLResponse)
async def read_root(request: Request):
    return templates.TemplateResponse("index.html", {"request": request})

@app.get("/stats")
def stats():
    return {"request_count": request_count}

@app.get("/health")
def health():
    return {"status": "OK"}

@app.post("/predict_model")
def predict_model(input_data: PredictionInput):
    global request_count
    request_count += 1

    predictions = model.predict([input_data.text])
        
    # Формируем ответ
    return {"Вывод:": "Это плохой комментарий" if predictions[0] == 1 else "Это обычный комментарий"}

if __name__ == '__main__':
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=5000)