Update app.py

app.py

@@ -1,17 +1,6 @@
 import streamlit as st
 import pandas as pd
 import re
-from nltk.tokenize import word_tokenize
-from nltk.corpus import stopwords
-from nltk.stem import WordNetLemmatizer
-from transformers import pipeline
-from PIL import Image
-
-# Load Models
-news_classifier = pipeline("text-classification", model="Oneli/News_Classification")
-import streamlit as st
-import pandas as pd
-import re
 import string
 import nltk
 from nltk.corpus import stopwords
@@ -24,6 +13,7 @@ from PIL import Image
 nltk.download('stopwords')
 nltk.download('punkt')
 nltk.download('wordnet')
+nltk.download('omw-1.4')
 
 # Load Models
 news_classifier = pipeline("text-classification", model="Oneli/News_Classification")
@@ -41,7 +31,6 @@ label_mapping = {
 # Store classified article for QA
 context_storage = {"context": "", "bulk_context": "", "num_articles": 0}
 
-
 # Text Cleaning Functions
 def clean_text(text):
     text = text.lower()
@@ -53,7 +42,6 @@ def clean_text(text):
     tokens = [lemmatizer.lemmatize(word) for word in tokens]  # Lemmatize tokens
     return " ".join(tokens)
 
-
 # Define the functions
 def classify_text(text):
     cleaned_text = clean_text(text)
@@ -66,12 +54,11 @@ def classify_text(text):
 
     return category, f"Confidence: {confidence}%"
 
-
 def classify_csv(file):
     try:
         df = pd.read_csv(file, encoding="utf-8")
         text_column = df.columns[0]  # Assume first column is the text column
-
+        
         df[text_column] = df[text_column].astype(str).apply(clean_text)  # Clean text column
         df["Encoded Prediction"] = df[text_column].apply(lambda x: news_classifier(x)[0]['label'])
         df["Decoded Prediction"] = df["Encoded Prediction"].map(label_mapping)
@@ -87,23 +74,22 @@ def classify_csv(file):
     except Exception as e:
         return None, f"Error: {str(e)}"
 
-
 def chatbot_response(history, user_input, source):
     user_input = user_input.lower()
     context = context_storage["context"] if source == "Single Article" else context_storage["bulk_context"]
     num_articles = context_storage["num_articles"]
-
+    
     if "number of articles" in user_input or "how many articles" in user_input:
         answer = f"There are {num_articles} articles in the uploaded CSV."
         history.append([user_input, answer])
         return history, ""
-
+    
     if context:
         result = qa_pipeline(question=user_input, context=context)
         answer = result["answer"]
         history.append([user_input, answer])
         return history, ""
-
+    
     responses = {
         "hello": "👋 Hello! How can I assist you with news today?",
         "hi": "😊 Hi there! What do you want to know about news?",
@@ -111,16 +97,14 @@ def chatbot_response(history, user_input, source):
         "thank you": "🙏 You're welcome! Let me know if you need anything else.",
         "news": "📰 I can classify news into Business, Sports, Politics, and more!",
     }
-    response = responses.get(user_input,
-                             "🤔 I'm here to help with news classification and general info. Ask me about news topics!")
+    response = responses.get(user_input, "🤔 I'm here to help with news classification and general info. Ask me about news topics!")
     history.append([user_input, response])
     return history, ""
 
-
 # Streamlit App Layout
 st.set_page_config(page_title="News Classifier", page_icon="📰")
 cover_image = Image.open("cover.png")  # Ensure this image exists
-st.image(cover_image, caption="News Classifier 📢", use_column_width=True)
+st.image(cover_image, caption="News Classifier 📢", use_container_width=True)
 
 # Section for Single Article Classification
 st.subheader("📰 Single Article Classification")
@@ -159,4 +143,4 @@ if st.button("✉ Send"):
     st.write("*Chatbot Response:*")
     for q, a in history:
         st.write(f"*Q:* {q}")
-        st.write(f"*A:* {a}")
+        st.write(f"*A:* {a}")