我正在做一个情绪分析项目，可以用于任何语言。下面是它的工作原理：在代码的最后部分，“result”将一个句子翻译成英语。然后，predict_函数（result.text）将英语文本分为肯定、否定或中性。
如果我单独运行代码，它就可以正常工作。现在我正在尝试制作前端，唯一的问题是我不知道如何将预测函数与它联系起来。翻译功能在那里工作，但唯一剩下的是在前端对翻译文本进行分类。我是新来的，我做了很多改变，但没能让它工作。
这是我的全部代码：（我想不必看全部代码，因为我觉得问题在@app.route（'/'，methods=['post']）行之后的最后部分）

from flask import Flask, request, render_template
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import nltk
import pandas as pd
import numpy as np
import seaborn as sns
import regex as re
import math

import googletrans

from googletrans import Translator
from nltk.tokenize import word_tokenize

app = Flask(__name__)

@app.route('/')
def my_form():
    return render_template('form.html')

df = pd.read_csv('C:/Users/path/file.csv')
df = df.rename(columns = {'clean_text':'Comment'})
df.head()

df.describe()

cat = []
for val in df['category'].values:
  if val not in cat:
    cat.append(val)
print(cat)

index_arr = []
for index, val in df.iterrows():
    if val['category'] not in [-1.0, 0.0, 1.0]:
        index_arr.append(index)
print(index_arr)
df.drop(index_arr, axis = 0, inplace = True)

sns.countplot(x='category',data=df)

def clean_comments(comment):
    comment = re.sub(r'\$\w*', '', str(comment))
    comment = re.sub(r'^RT[\s]+', '', str(comment))
    comment = re.sub(r'https?:\/\/.*[\r\n]*', '', str(comment))
    comment = re.sub(r'#', '', str(comment))
    comment = re.sub(r"@[^\s]+[\s]?",'',comment)
    comment = re.sub('[^ a-zA-Z0-9]', '', comment)
    comment = re.sub('[0-9]', '', comment)
    return comment

df['Comment'] = df['Comment'].apply(clean_comments)
df.head()

nltk.download('stopwords')
from nltk.corpus import stopwords

stop_words = stopwords.words('english')

def removing_stopwords(words):
  cleaned_tokens = []
  for val in words.split(' '):
    val = val.lower()
    if val not in stop_words and val != '':
      cleaned_tokens.append(val)
  return(cleaned_tokens)

df['Comment'] = df['Comment'].apply(removing_stopwords) 
df.head()

from nltk.stem.porter import PorterStemmer

def stem_comments(words):
  ps = PorterStemmer()
  stemmed_review = []
  for review in  words:
    stemmed_review.append(ps.stem(review))
  return stemmed_review

df['Comment'] = df['Comment'].apply(stem_comments)   
df.head()

temp = df.iloc[:,0].values
X = [' '.join(ele) for ele in temp]
X = np.array(X)
Y = df.iloc[:,1].values

from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(max_features=5000)
X = vectorizer.fit_transform(X).toarray()
print(X.shape)

print(Y[:5])
print(Y.shape)

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.01)

print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)

del X
del Y
del temp
del df

from sklearn.naive_bayes import MultinomialNB
classifier = MultinomialNB()
classifier.fit(X_train, y_train)

y_pred = classifier.predict(X_test)

from sklearn.metrics import confusion_matrix, accuracy_score
cm = confusion_matrix(y_test, y_pred)
print(cm)
print("Accuracy = ", accuracy_score(y_pred, y_test))

import seaborn as sn
from matplotlib.figure import Figure
df_cm = pd.DataFrame(cm, index = [0,1,2],columns = [0,1,2])
f = Figure(figsize = (20,10))
sn.heatmap(df_cm, annot=True)

def predict_function(sentence):
    sentence = clean_comments(sentence)
    sentence = removing_stopwords(sentence)
    sentence = stem_comments(sentence)

    X = [' '.join([str(elem) for elem in sentence])]
    X = np.array(X)
    X = vectorizer.transform(X).toarray()

    result = classifier.predict(X)

    if result == -1.0:
        print("Negative")
    elif result == 0.0:
        print("Neutral")
    else:
        print("Positive")

@app.route('/', methods=['POST'])
def my_form_post():
    text1 = request.form['text1'].lower()

    translator = Translator(service_urls=['translate.googleapis.com'])
    result = translator.translate(text1, dest='en')
    senti=predict_function(result.text)

    return render_template('form.html', final=result.text, last=senti, text1=text1)

if __name__ == "__main__":
    app.run(debug=True, host="127.0.0.1", port=5002, threaded=True)

前端的html代码：

<body>
    <h1>Welcome To Sentiment Analyzer</h1>
    <form method="POST">
        <textarea name="text1" placeholder="Say Something: ...." rows="10" cols="109"></textarea><br><br>

        <input class="example_a" type="submit">
    </form>
    {% if final %}
    <div> 
        <h2>The Sentiment of</h2> '{{ text1 }}' <h2>is {{ final }} </h2> <h2>is {{ last }} </h2>
        {% else %}
        <p></p>
        {% endif %}
    </div>
</body>