我在一个 flask 应用程序中dockerizin和一切工作，直到容器创建后，有一个错误“名称或服务未知”

停靠文件：

FROM python:3.10.8

COPY requirements.txt .

RUN pip install -r requirements.txt

RUN python -c "import nltk; nltk.download('averaged_perceptron_tagger'); nltk.download('wordnet'); nltk.download('omw-1.4'); nltk.download('stopwords');"

COPY . . 

EXPOSE 5000

CMD ["flask", "run", "--host= 0.0.0.0", "--port=5000"]

停靠-撰写.yml：

version: "3.7"

services:
  mlapp:
    container_name: Container
    image: mlapp
    ports: 
      - "5000:5000"
    build: 
      context: .
      dockerfile: Dockerfile

当我创建我的应用程序的本地服务器时，它给出了正确的答案，并且工作得很完美，我不明白是什么导致了这个问题。

应用程序py：

from flask import Flask, jsonify, request
from util import prediction

app = Flask(__name__)

@app.post('/predict')
def predict():
    data = request.json
    try:
        sample = data['text']     
    except KeyError:
        return jsonify({'error':'No text sent'})
    # sample = [sample]
    pred = prediction(sample)
    try:
        result = jsonify(pred)
    except TypeError as e:
        result = jsonify({'error': str(e)})
    return result
    
if __name__ == '__main__':
    app.run(host='0.0.0.0', debug= True)

利用率py

import nltk
import pandas as pd
from nltk import TweetTokenizer
import numpy as np
import nltk
from nltk.stem.wordnet import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
import csv
import pandas as pd
import time
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report
from nltk.tokenize import TweetTokenizer
from nltk.tag import pos_tag
import re
import string
from nltk.stem.wordnet import WordNetLemmatizer
from nltk.corpus import stopwords
import joblib
import warnings
warnings.filterwarnings("ignore")

# nltk.download('averaged_perceptron_tagger')
# nltk.download('wordnet')
# nltk.download('omw-1.4')
# nltk.download('stopwords')

token = TweetTokenizer()

def lemmatize_sentence(tokens):
    lemmatizer = WordNetLemmatizer()
    lemmatize_sentence = []
    for word, tag in pos_tag(tokens):
        if tag.startswith('NN'):
            pos = 'n'
        elif tag.startswith('VB'):
            pos = 'v'
        else:
            pos = 'a'
        lemmatize_sentence.append(lemmatizer.lemmatize(word, pos))
    return lemmatize_sentence
# print(' '.join(lemmatize_sentence(data[0][0])))

# Data cleaning, getting rid of words not needed for analysis.

stop_words = stopwords.words('english')

def cleaned(token):
    if token == 'u':
        return 'you'
    if token == 'r':
        return 'are'
    if token == 'some1':
        return 'someone'
    if token == 'yrs':
        return 'years'
    if token == 'hrs':
        return 'hours'
    if token == 'mins':
        return 'minutes'
    if token == 'secs':
        return 'seconds'
    if token == 'pls' or token == 'plz':
        return 'please'
    if token == '2morow':
        return 'tomorrow'
    if token == '2day':
        return 'today'
    if token == '4got' or token == '4gotten':
        return 'forget'
    if token == 'amp' or token == 'quot' or token == 'lt' or token == 'gt':
        return ''
    return token

# Noise removal from data, removing links, mentions and words with less than 3 length.

def remove_noise(tokens):
    cleaned_tokens = []
    for token, tag in pos_tag(tokens):
        # using non capturing groups ?:)// and eleminating the token if its a link.
        token = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+#]|[!*\(\),]|(?:%[0-9a-fA-F]))+', '', token)
        token = re.sub('[^a-zA-Z]', ' ', token)
        # eliminating token if its a mention
        token = re.sub("(@[A-Za-z0-9_]+)", "", token)
        if tag.startswith("NN"):
            pos = 'n'
        elif tag.startswith("VB"):
            pos = 'v'
        else:
            pos = 'a'

        lemmatizer = WordNetLemmatizer()
        token = lemmatizer.lemmatize(token, pos)

        cleaned_token = cleaned(token.lower())
        # Eliminating if the length of the token is less than 3, if its a punctuation or if it is a stopword.
        if cleaned_token not in string.punctuation and len(cleaned_token) > 2 and cleaned_token not in stop_words:
            cleaned_tokens.append(cleaned_token)
    return cleaned_tokens

with open ('Models/Sentimenttfpipe', 'rb') as f:
    loaded_pipeline = joblib.load(f)

def prediction(body):    
    # loaded_pipeline = joblib.load('Api/Models/Sentimenttfpipe')
    text= []
    test = token.tokenize(body)
    test = remove_noise(test)
    text.append(" ".join(test))
    test = pd.DataFrame(text, columns=['text'])
    a = loaded_pipeline.predict(test['text'].values.astype('U'))
    final = []
    if a[0] == 0:
        final.append({'Label' : 'Relaxed'})
        return {'Label' : 'Relaxed'}
        
    if a[0] == 1:
        final.append({'Label' : 'Angry'})
        return {'Label' : 'Angry'}
        
    if a[0] == 2:
        final.append({'Label' : 'Fearful'})
        return {'Label' : 'Fearful'}
        
    if a[0] == 3:
        final.append({'Label' : 'Happy'})
        return {'Label' : 'Happy'}
        
    if a[0] == 4:
        final.append({'Label' : 'Sad'})
        return {'Label' : 'Sad'}
        
    if a[0] == 5:
        final.append({'Label' : 'Surprised'})
        return {'Label' : 'Surprised'}
    
if __name__ == '__main__':
    sen = "may the force be with you"
    a = prediction(sen)
    print(a)
    # print(" ")

我已经尝试了相当多的谷歌冲浪，并没有找到解决方案，我试图改变小的代码，我认为可能会影响结果，但它没有。
我运行的命令是“docker compose up --build”，它在附加到容器时给出错误“Name or Service not known”。