Spaces:
Build error
Build error
import os | |
import numpy as np | |
import pandas as pd | |
import nltk | |
import pickle | |
from sklearn.feature_extraction.text import TfidfVectorizer | |
from sklearn.pipeline import make_pipeline | |
import streamlit.components.v1 as components | |
from transformers import pipeline | |
from sklearn.svm import SVC | |
from sklearn.preprocessing import LabelEncoder | |
import pickle | |
import streamlit as st | |
# Function to load the pre-trained model | |
def load_pretrained_model(): | |
try: | |
feature_file='tfidf_scorer.pkl' | |
with open(feature_file,'rb') as f: | |
feature_extractor=pickle.load(f) | |
f.close() | |
encoder_file='encoder.pkl' | |
with open(encoder_file,'rb') as f: | |
encoder=pickle.load(f) | |
f.close() | |
model_file='classifier.pkl' | |
with open(model_file,'rb') as f: | |
model=pickle.load(f) | |
f.close() | |
pipe=pipeline("token-classification",model="hatmimoha/arabic-ner",aggregation_strategy='max') | |
return feature_extractor,encoder,model,pipe | |
except FileNotFoundError: | |
st.error("Pre-trained model not found. Please make sure the model file exists.") | |
st.stop() | |
# Streamlit App | |
st.title("Text Classification App") | |
st.write("This app demonstrates text classification using a pre-trained scikit-learn-based machine learning model.") | |
# Information about the app | |
st.sidebar.title("App Information") | |
st.sidebar.info( | |
"""This Streamlit app showcases text classification using a pre-trained scikit-learn-based | |
machine learning model on Arabic texts. The data is sourced is from | |
Arabic news articles organized into 3 balanced categories from www.alkhaleej.ae | |
Labels are categorized in: Medical,Sports,Tech. | |
Enter text in the provided area, and the model will predict the label.""" | |
) | |
# Load the pre-trained model | |
tfidf,encode,trained_model,pipeline_obj = load_pretrained_model() | |
# User input for text classification | |
user_text = st.text_area("Enter text for classification:") | |
# Classify user input | |
if user_text: | |
tokens_new=nltk.wordpunct_tokenize(user_text) | |
tokens_corrected=[i for i in tokens_new if len(i)>1] | |
tfidf_tokens=' '.join(tokens_corrected) | |
x_test=tfidf.transform([tfidf_tokens]) | |
predicted=trained_model.predict(x_test) | |
predicted_class=encode.inverse_transform(predicted)[0] | |
st.write(f"Predicted Label: {predicted_class}") | |
if st.button("Extract entities"): | |
with st.spinner('Calculating...'): | |
entities=pipeline_obj(user_text) | |
if len(entities)>0: | |
entity_df=pd.DataFrame(entities) | |
st.table(entity_df[["entity_group","word"]]) | |
else: | |
st.write("No entities found") | |
"""if st.button("Perform explainability analysis"): | |
: | |
c=make_pipeline(tfidf,trained_model) | |
explainer = LimeTextExplainer(class_names=np.array(["Medical","Sports","Tech,Others"]),random_state=42) | |
exp = explainer.explain_instance(user_text, c.predict_proba, num_features=20, top_labels=3) | |
components.html(exp.as_html(), height=800) | |
#top_labels=exp.available_labels()""" | |