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| import streamlit as st | |
| import pandas as pd | |
| import torch | |
| import matplotlib.pyplot as plt | |
| from wordcloud import WordCloud | |
| from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification | |
| import re | |
| from nltk.corpus import stopwords | |
| from nltk.tokenize import word_tokenize | |
| from nltk.stem import WordNetLemmatizer | |
| import nltk | |
| nltk.download('stopwords') | |
| nltk.download('punkt') | |
| nltk.download('punkt_tab') | |
| nltk.download('wordnet') | |
| nltk.download('omw-1.4') | |
| # Initialize lemmatizer | |
| lemmatizer = WordNetLemmatizer() | |
| st.set_page_config(page_title="📰 News Classifier & Q&A App", layout="wide") | |
| # ----------------- Model Loader ----------------- | |
| def load_text_classifier(): | |
| model_name = "MihanTilk/News_Classifier" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSequenceClassification.from_pretrained( | |
| model_name | |
| ) | |
| return pipeline("text-classification", model=model, tokenizer=tokenizer) | |
| # Load Classifier & QA pipeline | |
| classifier = load_text_classifier() | |
| qa_pipeline = pipeline( | |
| "question-answering", | |
| model="deepset/roberta-large-squad2", | |
| tokenizer="deepset/roberta-large-squad2" | |
| ) | |
| # ----------------- CSS Styling ----------------- | |
| st.markdown( | |
| """ | |
| <style> | |
| /* Main background and text colors */ | |
| .main { | |
| background-color: #f4f4f4; | |
| } | |
| /* Text input boxes - light blue theme */ | |
| .stTextInput>div>div>input, | |
| .stTextArea>div>div>textarea { | |
| background-color: #e6f2ff; | |
| border: 1px solid #b3d1ff; | |
| border-radius: 8px; | |
| color: #003366; | |
| } | |
| /* File uploader - matching style */ | |
| .stFileUploader>div>div { | |
| background-color: #e6f2ff; | |
| border: 1px solid #b3d1ff; | |
| border-radius: 8px; | |
| } | |
| /* Buttons - keeping your original style */ | |
| .stButton>button { | |
| background-color: #ff4b4b; | |
| color: white; | |
| border-radius: 10px; | |
| border: none; | |
| } | |
| .stDownloadButton>button { | |
| background-color: #4CAF50; | |
| color: white; | |
| border-radius: 10px; | |
| border: none; | |
| } | |
| /* Text colors */ | |
| h1, h2, h3, h4, h5, h6 { | |
| color: #003366; /* Dark blue for headers */ | |
| } | |
| p { | |
| color: #336699; /* Medium blue for paragraphs */ | |
| } | |
| /* Dataframe styling */ | |
| .dataframe { | |
| background-color: #e6f2ff; | |
| border: 1px solid #b3d1ff; | |
| } | |
| </style> | |
| """, | |
| unsafe_allow_html=True | |
| ) | |
| # ----------------- App Title ----------------- | |
| st.title("📰 News Classification & Q&A App") | |
| st.markdown("<h4 style='color:#ff4b4b;'>Upload a CSV to classify news headlines and ask questions!</h4>", unsafe_allow_html=True) | |
| # ----------------- Upload CSV ----------------- | |
| st.subheader("📂 Upload a CSV File") | |
| uploaded_file = st.file_uploader("Choose a CSV file...", type=["csv"]) | |
| if uploaded_file: | |
| # Read and preprocess | |
| df = pd.read_csv(uploaded_file, encoding='utf-8') | |
| if "content" not in df.columns: | |
| st.error("❌ The uploaded CSV must contain a 'content' column.") | |
| st.stop() | |
| # Define preprocessing function | |
| def preprocess_text(text): | |
| # Lowercasing | |
| text = text.lower() | |
| # Remove punctuation and special characters | |
| text = re.sub(r'[^\w\s]', '', text) | |
| # Tokenize | |
| tokens = word_tokenize(text) | |
| # Remove stopwords | |
| tokens = [word for word in tokens if word not in stopwords.words('english')] | |
| # Lemmatize tokens | |
| tokens = [lemmatizer.lemmatize(word) for word in tokens] | |
| # Join tokens back to string | |
| return " ".join(tokens) | |
| # Apply preprocessing | |
| df['cleaned_text'] = df['content'].astype(str).apply(preprocess_text) | |
| st.write("📊 Preview of Uploaded Data:", df.head()) | |
| # ----------------- Classification ----------------- | |
| with st.spinner("🔍 Classifying news articles..."): | |
| # Get full classification results (label + score) | |
| classification_results = df['cleaned_text'].apply(lambda text: classifier(text)[0]) | |
| # Store all classification data in the dataframe | |
| df['class'] = classification_results.apply(lambda x: x['label']) | |
| df['confidence'] = classification_results.apply(lambda x: x['score']) | |
| # Cache the categorized articles in session state | |
| def get_category_articles(_df): | |
| return {category: _df[_df['class'] == category] for category in _df['class'].unique()} | |
| st.session_state.category_articles = get_category_articles(df) | |
| st.session_state.classified_df = df # Store the full classified dataframe | |
| st.success("✅ Classification Complete!") | |
| st.write("🔎 Classified Results:", df[['content', 'class']].head()) | |
| # ----------------- Download ----------------- | |
| st.subheader("📥 Download Results") | |
| output_df = df[['content', 'class']] | |
| csv_output = output_df.to_csv(index=False, encoding='utf-8-sig').encode('utf-8-sig') | |
| st.download_button("Download Output CSV", data=csv_output, file_name="output.csv", mime="text/csv") | |
| # ----------------- Q&A Section ----------------- | |
| st.subheader("💬 Ask a Question") | |
| question = st.text_input("🔍 What do you want to know about the content?") | |
| if st.button("Get Answer"): | |
| context = " ".join(df['content'].tolist()) | |
| with st.spinner("Answering..."): | |
| result = qa_pipeline(question=question, context=context) | |
| st.success(f"📝 Answer: {result['answer']}") | |
| # ----------------- Visualization Section ----------------- | |
| st.subheader("📊 Data Visualizations") | |
| # Create two main columns (60/40 split) | |
| main_col1, main_col2 = st.columns([3, 2]) | |
| with main_col1: | |
| # ----------------- Topic Distribution ----------------- | |
| st.markdown("*Topic Distribution*") | |
| # Create sub-columns for the charts | |
| chart_col1, chart_col2 = st.columns(2) | |
| with chart_col1: | |
| # Compact Pie Chart | |
| fig1, ax1 = plt.subplots(figsize=(4, 4)) | |
| df['class'].value_counts().plot.pie( | |
| autopct='%1.1f%%', | |
| startangle=90, | |
| ax=ax1, | |
| colors=['#ff9999', '#66b3ff', '#99ff99', '#ffcc99', '#c2c2f0'], | |
| wedgeprops={'linewidth': 0.5, 'edgecolor': 'white'} | |
| ) | |
| ax1.set_ylabel('') | |
| st.pyplot(fig1, use_container_width=True) | |
| with chart_col2: | |
| # Compact Bar Chart | |
| fig2, ax2 = plt.subplots(figsize=(4, 4)) | |
| df['class'].value_counts().plot.bar( | |
| color=['#ff9999', '#66b3ff', '#99ff99', '#ffcc99', '#c2c2f0'], | |
| ax=ax2, | |
| width=0.7 | |
| ) | |
| ax2.set_xlabel('') | |
| ax2.set_ylabel('Count') | |
| plt.xticks(rotation=45, ha='right') | |
| st.pyplot(fig2, use_container_width=True) | |
| with main_col2: | |
| # ----------------- Compact Word Cloud ----------------- | |
| st.markdown("*Word Cloud*") | |
| text = " ".join(df['cleaned_text'].tolist()) | |
| wordcloud = WordCloud( | |
| width=300, | |
| height=200, | |
| background_color="white", | |
| collocations=False, | |
| max_words=100 | |
| ).generate(text) | |
| fig3, ax3 = plt.subplots(figsize=(4, 3)) | |
| ax3.imshow(wordcloud, interpolation="bilinear") | |
| ax3.axis("off") | |
| st.pyplot(fig3, use_container_width=True) | |
| # ----------------- Detailed Stats (below) ----------------- | |
| with st.expander("📈 Detailed Statistics", expanded=False): | |
| stats_col1, stats_col2 = st.columns(2) | |
| with stats_col1: | |
| st.write("*Category Breakdown:*") | |
| stats_df = df['class'].value_counts().reset_index() | |
| stats_df.columns = ['Category', 'Count'] | |
| stats_df['Percentage'] = (stats_df['Count'] / stats_df['Count'].sum() * 100).round(1) | |
| st.dataframe(stats_df, height=200) | |
| with stats_col2: | |
| if 'date' in df.columns: | |
| try: | |
| st.write("*Monthly Trends*") | |
| df['date'] = pd.to_datetime(df['date']) | |
| trends = df.groupby([df['date'].dt.to_period('M'), 'class']).size().unstack() | |
| st.line_chart(trends) | |
| except: | |
| st.warning("Date parsing failed") | |
| # ----------------- News Category Explorer ----------------- | |
| st.subheader("🔍 Explore News by Category") | |
| if 'category_articles' in st.session_state: | |
| categories = st.session_state.classified_df['class'].unique() | |
| cols = st.columns(min(5, len(categories))) | |
| for i, category in enumerate(categories): | |
| with cols[i % len(cols)]: | |
| if st.button(category, key=f"btn_{category}"): | |
| with st.popover(f"📰 {category} Articles", use_container_width=True): | |
| st.markdown(f"### {category} Articles") | |
| articles = st.session_state.category_articles[category] | |
| with st.container(height=600): | |
| for idx, row in articles.iterrows(): | |
| with st.expander(f"Article {idx + 1}", expanded=False): | |
| st.write(row['content']) | |
| st.caption(f"Confidence: {row['confidence']:.2f}") | |
| else: | |
| st.warning("Classification data not found. Please upload and classify a file first.") | |
| # ----------------- Footer ----------------- | |
| st.markdown("---") | |
| st.markdown("<p style='text-align:center; color:#666;'>🚀 Built with using Streamlit & Hugging Face</p>", unsafe_allow_html=True) |