Davisy

# text preprocessing modules
from string import punctuation
# text preprocessing modules
from nltk.tokenize import word_tokenize
import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import re  # regular expression
import os
from os.path import dirname, join, realpath

Davisy

@app.get("/predict-review")
def predict_sentiment(review: str):
    """
    A simple function that receive a review content and predict the sentiment of the content.
    :param review:
    :return: prediction, probabilities
    """
    # clean the review
    cleaned_review = text_cleaning(review)
    

Davisy

def text_cleaning(text, remove_stop_words=True, lemmatize_words=True):
    # Clean the text, with the option to remove stop_words and to lemmatize word
    # Clean the text
    text = re.sub(r"[^A-Za-z0-9]", " ", text)
    text = re.sub(r"\'s", " ", text)
    text = re.sub(r"http\S+", " link ", text)
    text = re.sub(r"\b\d+(?:\.\d+)?\s+", "", text)  # remove numbers
    # Remove punctuation from text
    text = "".join([c for c in text if c not in punctuation])
    # Optionally, remove stop words

Davisy

# load the sentiment model
with open(
    join(dirname(realpath(__file__)), "models/sentiment_model_pipeline.pkl"), "rb"
) as f:
    model = joblib.load(f)

Davisy

app = FastAPI(
    title="Sentiment Model API",
    description="A simple API that use NLP model to predict the sentiment of the movie's reviews",
    version="0.1",
)

Davisy

# text preprocessing modules
from string import punctuation 
# text preprocessing modules
from nltk.tokenize import word_tokenize
import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import re  # regular expression
import os
from os.path import dirname, join, realpath

Davisy

sentence = "I hope that, when I've built up my savings, I'll be able to travel to Mexico"

print(detect_and_translate(sentence,target_lang='sw'))

Davisy

from langdetect import detect
from google_trans_new import google_translator  

#simple function to detect and translate text 
def detect_and_translate(text,target_lang):
    
    result_lang = detect(text)
    
    if result_lang == target_lang:
        return text 

Davisy

from google_trans_new import google_translator

translator = google_translator()

sentence = "Tanzania ni nchi inayoongoza kwa utalii barani afrika"
translate_text = translator.translate(sentence,lang_tgt='en') 

print(translate_text)

Davisy

from langdetect import DetectorFactory
DetectorFactory.seed = 0