Upload app.py

app.py

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+import streamlit as st
+import pandas as pd
+import joblib
+import re
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+import nltk
+
+# Ensure required NLTK data is available
+nltk.download('stopwords')
+nltk.download('punkt')
+
+# Load the dataset and model
+df = pd.read_csv("C:\\Users\\saipr\\anaconda3\\Projects\\News_Classification\\bbc_data.csv")
+model = joblib.load('model.pkl')  # Load your pre-trained model
+vectorizer = joblib.load('vectorizer.pkl')  # Load pre-trained vectorizer
+
+X = df['data']
+y = df['labels']
+
+# Preprocessing function
+def preprocess_text(text):
+    text = re.sub(r'[^\w\s]', '', text.lower())  # Remove punctuation
+    tokens = word_tokenize(text)  # Tokenize the text
+    stop_words = set(stopwords.words('english'))  # Load stopwords
+    tokens = [word for word in tokens if word not in stop_words]  # Remove stopwords
+    return ' '.join(tokens)
+
+# Title of the app
+st.title('News Classification App')
+
+# User input
+user_input = st.text_area('Enter a headline')
+
+if st.button('Classify'):
+    if user_input:
+        # Preprocess the input text
+        preprocessed_input = preprocess_text(user_input)
+        
+        # Convert preprocessed text to numerical data using the loaded vectorizer
+        input_vector = vectorizer.transform([preprocessed_input])
+        
+        # Make prediction
+        prediction = model.predict(input_vector)
+        
+        # Display the result
+        st.write(f'Predicted Category: {prediction[0]}')
+    else:
+        st.write('Please enter a headline')