Update app.py

app.py

@@ -4,110 +4,93 @@ import re
 import io
 from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
 from transformers import AutoModelForQuestionAnswering
-from streamlit_extras.app_logo import add_logo  # For adding a logo
 
-# Custom Styling
-st.set_page_config(page_title="News Classifier & Q&A", page_icon="📰", layout="wide")
+# Load fine-tuned models and tokenizers for both functions
+model_name_classification = "TAgroup5/news-classification-model"  # Replace with the correct model name
+model = AutoModelForSequenceClassification.from_pretrained(model_name_classification)
+tokenizer = AutoTokenizer.from_pretrained(model_name_classification)
 
-# CSS for styling
+model_name_qa = "distilbert-base-cased-distilled-squad"
+model_qa = AutoModelForQuestionAnswering.from_pretrained(model_name_qa)
+tokenizer_qa = AutoTokenizer.from_pretrained(model_name_qa)
+
+# Initialize pipelines
+text_classification_pipeline = pipeline("text-classification", model=model, tokenizer=tokenizer)
+qa_pipeline = pipeline("question-answering", model=model)
+
+# Streamlit App Styling
+st.set_page_config(page_title="News Classification & Q&A", page_icon="📰", layout="wide")
 st.markdown(
     """
     <style>
-        body {
-            background-color: #f5f5f5;
-        }
-        .stApp {
-            background-color: white;
-            border-radius: 10px;
-            padding: 20px;
-            box-shadow: 2px 2px 10px rgba(0, 0, 0, 0.1);
-        }
-        .stTitle, .stHeader {
-            color: #0073e6;
-            text-align: center;
-        }
-        .stButton>button {
-            background-color: #0073e6 !important;
-            color: white !important;
-            border-radius: 8px !important;
-            font-size: 16px !important;
-        }
-        .stDownloadButton>button {
-            background-color: #28a745 !important;
-            color: white !important;
-            border-radius: 8px !important;
-        }
+        body {background-color: #f4f4f4;}
+        .title {text-align: center; font-size: 36px; font-weight: bold; color: #ff4b4b;}
+        .subheader {font-size: 24px; color: #333; margin-bottom: 20px; text-align: right;}
+        .stTextInput>div>div>input {border-radius: 10px;}
+        .stTextArea>div>div>textarea {border-radius: 10px;}
+        .stButton>button {border-radius: 10px; background-color: #ff4b4b; color: white; font-weight: bold;}
     </style>
     """,
     unsafe_allow_html=True,
 )
 
-# Add a logo (optional, replace with your logo URL)
-# add_logo("https://your-logo-url.png", height=50)
-
-st.title("📰 News Classification & Q&A")
-
-## ====================== Component 1: News Classification ====================== ##
-st.header("📌 Classify News Articles")
-st.markdown("Upload a CSV file with a 'content' column to classify news into categories.")
-
-uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
-
-if uploaded_file is not None:
-    try:
-        df = pd.read_csv(uploaded_file, encoding="utf-8")
-    except UnicodeDecodeError:
-        df = pd.read_csv(uploaded_file, encoding="ISO-8859-1")
-
-    if 'content' not in df.columns:
-        st.error("❌ The uploaded CSV must contain a 'content' column.")
-    else:
-        st.success("✅ File uploaded successfully!")
-        st.write("Preview of uploaded data:")
-        st.dataframe(df.head())
-
-        # Preprocessing function
-        def preprocess_text(text):
-            text = text.lower()
-            text = re.sub(r'\s+', ' ', text)
-            text = re.sub(r'[^a-z\s]', '', text)
-            return text
-
-        # Apply preprocessing
-        df['processed_content'] = df['content'].apply(preprocess_text)
-
-        # Load Model
-        model_name_classification = "TAgroup5/news-classification-model"
-        model = AutoModelForSequenceClassification.from_pretrained(model_name_classification)
-        tokenizer = AutoTokenizer.from_pretrained(model_name_classification)
-        text_classification_pipeline = pipeline("text-classification", model=model, tokenizer=tokenizer)
-
-        # Classify each record
-        df['class'] = df['processed_content'].apply(lambda x: text_classification_pipeline(x)[0]['label'] if x.strip() else "Unknown")
-
-        # Display results
-        st.write("📌 Classification Results:")
-        st.dataframe(df[['content', 'class']])
-
-        # Provide CSV download
-        output = io.BytesIO()
-        df.to_csv(output, index=False, encoding="utf-8-sig")
-        st.download_button(label="📥 Download Classified News", data=output.getvalue(), file_name="classified_news.csv", mime="text/csv")
-
-## ====================== Component 2: Q&A ====================== ##
-st.header("💬 Ask a Question About the News")
-st.markdown("Enter a question and provide a news article to get an answer.")
-
-question = st.text_input("🔍 Ask a question:")
-context = st.text_area("📝 Provide the news article content:", height=150)
-
-if question and context.strip():
-    model_name_qa = "distilbert-base-uncased-distilled-squad"
-    qa_pipeline = pipeline("question-answering", model=model_name_qa, tokenizer=model_name_qa)
-    result = qa_pipeline(question=question, context=context)
-    
-    # Display answer
-    if 'answer' in result and result['answer']:
-        st.success(f"**🗣 Answer:** {result['answer']}")
-    else:
-        st.warning("⚠️ No answer found in the provided content.")
+st.markdown('<h1 class="title">📰 News Classification & Q&A App</h1>', unsafe_allow_html=True)
+
+col1, col2 = st.columns([2, 1])
+with col2:
+    # ====================== Component 1: News Classification ====================== #
+    st.markdown('<h2 class="subheader">📌 Classify News Articles</h2>', unsafe_allow_html=True)
+    st.markdown("Upload a CSV file with a 'content' column to classify news into categories.")
+
+    uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+
+    if uploaded_file is not None:
+        try:
+            df = pd.read_csv(uploaded_file, encoding="utf-8")  # Handle encoding issues
+        except UnicodeDecodeError:
+            df = pd.read_csv(uploaded_file, encoding="ISO-8859-1")
+        
+        if 'content' not in df.columns:
+            st.error("❌ Error: The uploaded CSV must contain a 'content' column.")
+        else:
+            st.success("✅ File successfully uploaded!")
+            st.write("Preview of uploaded data:")
+            st.dataframe(df.head())
+            
+            # Preprocessing function to clean the text
+            def preprocess_text(text):
+                text = text.lower()  # Convert to lowercase
+                text = re.sub(r'\s+', ' ', text)  # Remove extra spaces
+                text = re.sub(r'[^a-z\s]', '', text)  # Remove special characters & numbers
+                return text
+
+            # Apply preprocessing and classification
+            df['processed_content'] = df['content'].apply(preprocess_text)
+            df['class'] = df['processed_content'].apply(lambda x: text_classification_pipeline(x)[0]['label'] if x.strip() else "Unknown")
+            
+            # Show results
+            st.markdown("### 🔹 Classification Results:")
+            st.dataframe(df[['content', 'class']])
+            
+            # Provide CSV download
+            output = io.BytesIO()
+            df.to_csv(output, index=False, encoding="utf-8-sig")
+            st.download_button(label="⬇️ Download classified news", data=output.getvalue(), file_name="classified_news.csv", mime="text/csv")
+
+    # ====================== Component 2: Q&A ====================== #
+    st.markdown('<h2 class="subheader">❓ Ask a Question About the News</h2>', unsafe_allow_html=True)
+    st.markdown("Enter a question and provide a news article to get an answer.")
+
+    question = st.text_input("🔍 Ask a question:")
+    context = st.text_area("📝 Provide the news article or content:", height=150)
+
+    if question and context.strip():
+        model_name_qa = "distilbert-base-uncased-distilled-squad"
+        qa_pipeline = pipeline("question-answering", model=model_name_qa, tokenizer=model_name_qa)
+        result = qa_pipeline(question=question, context=context)
+        
+        # Display Answer
+        if 'answer' in result and result['answer']:
+            st.markdown(f"### ✅ Answer: {result['answer']}")
+        else:
+            st.markdown("### ❌ No answer found in the provided content.")