Delete app.py

app.py

@@ -1,49 +0,0 @@
-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')