Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import time
+from transformers import pipeline
+
+# Set page title and layout
+st.set_page_config(page_title="CSV News Classifier", layout="wide")
+
+# Load the fine-tuned Hugging Face model
+@st.cache_resource
+def load_model():
+    return pipeline("text-classification", model="TAgroup5/daily-mirror-news-classifier")
+
+classifier = load_model()
+
+# Custom CSS for Colors and Styling
+st.markdown(
+    """
+    <style>
+        body { background-color: #f8f9fa; }
+        .stApp { background-color: #ffffff; padding: 20px; border-radius: 10px; box-shadow: 0px 0px 10px rgba(0,0,0,0.1); }
+        
+        .custom-box {
+            background-color: #f0f0f0; 
+            padding: 15px;
+            border-radius: 10px;
+            border: 1px solid #ccc;
+            margin-bottom: 15px;
+        }
+        h1 { color: #ff5733; text-align: center; }  
+        h2, h3 { color: #007bff; }  
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Page Title
+st.title("📊 News Classification from CSV")
+
+# File Uploader
+st.markdown('<div class="custom-box"><h3>📂 Upload a CSV file</h3></div>', unsafe_allow_html=True)
+uploaded_file = st.file_uploader("", type=["csv"])
+
+if uploaded_file is not None:
+    # Read CSV
+    df = pd.read_csv(uploaded_file)
+
+    # Show Preview
+    st.markdown("### 🔍 **Preview of Uploaded File**", unsafe_allow_html=True)
+    st.dataframe(df.head())
+
+    # Assuming the column with news articles is named "news_text"
+    if "news_text" not in df.columns:
+        st.error("❌ The uploaded CSV must contain a 'news_text' column.")
+    else:
+        # Perform classification
+        st.markdown("### 🏷️ **Classifying News Articles...**")
+        with st.spinner("Processing..."):
+            df["class"] = df["news_text"].apply(lambda text: classifier(text)[0]["label"])
+
+        # Show Preview of Results
+        st.markdown("### 📌 **Preview of Classified Data**", unsafe_allow_html=True)
+        st.dataframe(df.head())
+
+        # Download Button for Classified CSV
+        csv = df.to_csv(index=False).encode("utf-8")
+        st.markdown("### 📥 **Download Classified CSV**", unsafe_allow_html=True)
+        st.download_button(
+            label="⬇️ **Download Classified CSV**",
+            data=csv,
+            file_name="classified_news.csv",
+            mime="text/csv",
+            help="Click to download the classified news file"
+        )
+
+# Footer
+st.markdown("---")
+st.markdown('<p style="text-align:center; font-size:14px; color:#6c757d;">👨‍💻 Developed by <b>Ridmi Navodya</b> | Powered by Streamlit 🚀</p>', unsafe_allow_html=True)
+
+