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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
@st.cache(allow_output_mutation=True)
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()"""
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