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import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
from wordcloud import WordCloud
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
# ✅ MUST be first Streamlit command
st.set_page_config(page_title="📰 News Classifier & Q&A App", layout="wide")
# ----------------- Model Loader -----------------
@st.cache_resource
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-base-squad2")
# ----------------- CSS Styling -----------------
st.markdown(
"""
<style>
.main { background-color: #f4f4f4; }
.stTextInput, .stFileUploader { border: 2px solid #ff4b4b; border-radius: 10px; }
.stButton>button { background-color: #ff4b4b; color: white; border-radius: 10px; }
.stDownloadButton>button { background-color: #4CAF50; color: white; border-radius: 10px; }
h1, h2, h3, h4, h5, h6, p { color: #333333; }
</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)
if "content" not in df.columns:
st.error("❌ The uploaded CSV must contain a 'content' column.")
st.stop()
# Preprocess text
df['cleaned_text'] = df['content'].astype(str).str.lower().str.strip()
st.write("📊 Preview of Uploaded Data:", df.head())
# ----------------- Classification -----------------
with st.spinner("🔍 Classifying news articles..."):
df['class'] = df['cleaned_text'].apply(lambda text: classifier(text)[0]['label'])
st.success("✅ Classification Complete!")
st.write("🔎 Classified Results:", df[['content', 'class']].head())
# ----------------- Download -----------------
st.subheader("📥 Download Results")
csv_output = df.to_csv(index=False).encode('utf-8')
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['cleaned_text'].tolist())
with st.spinner("Answering..."):
result = qa_pipeline(question=question, context=context)
st.success(f"📝 **Answer:** {result['answer']}")
# ----------------- Word Cloud -----------------
st.subheader("☁️ Word Cloud of News Text")
text = " ".join(df['cleaned_text'].tolist())
wordcloud = WordCloud(width=800, height=400, background_color="white").generate(text)
fig, ax = plt.subplots()
ax.imshow(wordcloud, interpolation="bilinear")
ax.axis("off")
st.pyplot(fig)
# ----------------- Footer -----------------
st.markdown("---")
st.markdown("<p style='text-align:center; color:#666;'>🚀 Built with using Streamlit & Hugging Face</p>", unsafe_allow_html=True) |