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import gradio as gr | |
import numpy as np | |
import pickle | |
import nltk | |
from nltk.corpus import stopwords | |
from nltk.tokenize import word_tokenize | |
from nltk.stem import WordNetLemmatizer | |
from sklearn.feature_extraction.text import CountVectorizer | |
# Initialize NLTK resources (download if needed) | |
nltk.download('punkt') | |
nltk.download('wordnet') | |
nltk.download('stopwords') | |
# Text preprocessing functions | |
def preprocess_text(text): | |
# Tokenization | |
words = word_tokenize(text.lower()) # Convert to lowercase and tokenize | |
# Remove stopwords | |
stop_words = set(stopwords.words('english')) | |
words = [word for word in words if word not in stop_words] | |
# Lemmatization | |
lemmatizer = WordNetLemmatizer() | |
words = [lemmatizer.lemmatize(word) for word in words] | |
return ' '.join(words) | |
def predict_tags(text): | |
return mlb.classes_[np.where(model.predict(vectorizer.transform([preprocess_text(text)])).flatten() == 1)] | |
# Load the instance back | |
with open('classes.pkl', 'rb') as file: | |
mlb = pickle.load(file) | |
with open('vectorizer.pkl', 'rb') as file: | |
vectorizer = pickle.load(file) | |
with open('model.pkl', 'rb') as file: | |
model = pickle.load(file) | |
# Create a function to predict tags using the ONNX model | |
iface = gr.Interface(fn=predict_tags, inputs="text", outputs="text") | |
iface.launch() |