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import gradio as gr
import joblib as jb
import re
import string
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer, WordNetLemmatizer
import nltk
nltk.download('stopwords')
stop_words = set(stopwords.words('english'))
# downloading the additional resources required by nltk
nltk.download('punkt')
nltk.download('wordnet')
nltk.download('omw-1.4')
cv = jb.load('.//models//transformer_v1.pkl')
model = jb.load('.//models//model_v1.pkl')
def preprocess_text(text):
"""
Runs a set of transformational steps to
preprocess the text of the tweet.
"""
# convert all text to lower case
text = text.lower()
# remove any urls
text = re.sub(r'http\S+|www\S+|https\S+', "", text, flags=re.MULTILINE)
# replace '****' with 'curse'
text = re.sub(r'\*\*\*\*', "gaali", text)
# remove punctuations
text = text.translate(str.maketrans("", "", string.punctuation))
# remove user @ references and hashtags
text = re.sub(r'\@\w+|\#', "", text)
# remove useless characters
text = re.sub(r'[^ -~]', '', text)
# remove stopwords
tweet_tokens = word_tokenize(text)
filtered_words = [word for word in tweet_tokens if word not in stop_words]
# stemming
ps = PorterStemmer()
stemmed_words = [ps.stem(w) for w in filtered_words]
# lemmatizing
lemmatizer = WordNetLemmatizer()
lemma_words = [lemmatizer.lemmatize(w, pos='a') for w in stemmed_words]
return ' '.join(lemma_words)
def sentiment_analysis(text):
# print(text)
text = cv.transform([preprocess_text(text)])
pred_prob = model.predict_proba(text)[0]
output = {"Negative": float(pred_prob[0]), "Neutral": float(pred_prob[1]), "Positive": float(pred_prob[2])}
# print(output)
return output
intfc = gr.Interface(
fn=sentiment_analysis,
inputs=gr.Textbox(label="Input here", lines=2, placeholder="Input your text"),
outputs=gr.Label(label="Sentiment Analysis"),
)
intfc.launch()
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