Delete streamlit_app.py

streamlit_app.py

@@ -1,80 +0,0 @@
-import streamlit as st
-import pandas as pd
-import torch
-from transformers import pipeline
-import nltk
-from nltk.corpus import stopwords
-from nltk.stem import WordNetLemmatizer
-import re
-import string
-
-import os
-from huggingface_hub import login
-
-hf_token = os.getenv("HF_TOKEN")
-if hf_token:
-    login(hf_token)
-
-# ====================== PREPROCESSING (Same as Task 2) ======================
-
-# ====================== LOAD FINE-TUNED MODEL ======================
-@st.cache_resource
-def load_model():
-    model_name = "Ginidu2003/Distilbert-Base-News-classifier"   # ← Your exact model name
-    return pipeline(
-        "text-classification",
-        model=model_name,
-        device=0 if torch.cuda.is_available() else -1
-    )
-
-classifier = load_model()
-
-# ====================== STREAMLIT APP ======================
-st.title("📰 Daily Mirror News Classifier")
-st.subheader("Classify news into Business, Opinion, Political Gossip, Sports, or World News")
-
-st.markdown("**Upload a CSV file** with a column named `content`")
-
-uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
-
-if uploaded_file is not None:
-    df = pd.read_csv(uploaded_file)
-    
-    st.write("### Preview of uploaded data")
-    st.dataframe(df.head())
-
-    if 'content' not in df.columns:
-        st.error("Your CSV must have a column named 'content'")
-    else:
-        with st.spinner("Preprocessing and classifying..."):
-            # Apply same preprocessing as Task 2
-            #df['clean_content'] = df['content'].apply(preprocess_text)
-            
-            # Classify
-            predictions = []
-            for text in df['content']:
-                if text.strip() == "":
-                    predictions.append("Unknown")
-                else:
-                    result = classifier(text)[0]
-                    predictions.append(result['label'])
-            
-            df['class'] = predictions
-            
-            # Drop helper column
-            #df = df.drop(columns=['clean_content'], errors='ignore')
-            
-            st.success("✅ Classification completed!")
-            st.write("### Preview of classified data")
-            st.dataframe(df.head())
-
-            # Download button
-            csv = df.to_csv(index=False).encode('utf-8')
-            st.download_button(
-                label="📥 Download output.csv",
-                data=csv,
-                file_name="output.csv",
-                mime="text/csv"
-            )
-
-st.caption("Built for Text Analytics Assignment - Section 02")