Upload app.py

app.py

@@ -0,0 +1,95 @@
+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()"""
+
+
+
+
+
+