Update app.py

app.py

@@ -5,9 +5,49 @@ import io
 from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
 from transformers import AutoModelForQuestionAnswering
 
-
-# Load fine-tuned models and tokenizers for both functions 
-model_name_classification = "TAgroup5/news-classification-model" 
+# Streamlit UI
+st.set_page_config(page_title="News Classifier & Q&A", layout="wide")
+st.markdown("""
+    <style>
+        body {
+            background-color: #f4f4f4;
+            color: #333333;
+            font-family: 'Arial', sans-serif;
+        }
+        .stApp {
+            background-color: white;
+            padding: 20px;
+            border-radius: 10px;
+            box-shadow: 2px 2px 10px rgba(0, 0, 0, 0.1);
+        }
+        h1, h2 {
+            color: #ff4b4b;
+        }
+        .stButton>button {
+            background-color: #ff4b4b !important;
+            color: white;
+            font-size: 16px;
+            border-radius: 5px;
+        }
+        .stDownloadButton>button {
+            background-color: #28a745 !important;
+            color: white;
+            font-size: 16px;
+            border-radius: 5px;
+        }
+        .stTextInput>div>div>input {
+            border-radius: 5px;
+            border: 1px solid #ccc;
+        }
+        .stTextArea>div>textarea {
+            border-radius: 5px;
+            border: 1px solid #ccc;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+# Load fine-tuned models and tokenizers
+model_name_classification = "TAgroup5/news-classification-model"
 model = AutoModelForSequenceClassification.from_pretrained(model_name_classification)
 tokenizer = AutoTokenizer.from_pretrained(model_name_classification)
 
@@ -19,66 +59,59 @@ tokenizer_qa = AutoTokenizer.from_pretrained(model_name_qa)
 text_classification_pipeline = pipeline("text-classification", model=model, tokenizer=tokenizer)
 qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer)
 
-
 # Streamlit App
-st.title("News Classification and Q&A")
+st.title("📰 News Classification and 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.")
+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")
+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
+        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("Error: The uploaded CSV must contain a 'content' column.")
+        st.error("❌ Error: The uploaded CSV must contain a 'content' column.")
     else:
-        st.write("Preview of uploaded data:")
+        st.write("✅ Preview of uploaded data:")
         st.dataframe(df.head())
 
-        # Preprocessing function to clean the text
+        # Preprocessing function
         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
-            # You don't need tokenization here, as the model tokenizer will handle it
+            text = text.lower()
+            text = re.sub(r'\s+', ' ', text)
+            text = re.sub(r'[^a-z\s]', '', text)
             return text
 
-
-        # Apply preprocessing and classification
         df['processed_content'] = df['content'].apply(preprocess_text)
-        
-        # Classify each record into one of the five classes
         df['class'] = df['processed_content'].apply(lambda x: text_classification_pipeline(x)[0]['label'] if x.strip() else "Unknown")
 
         # Show results
-        st.write("Classification 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="output.csv", mime="text/csv")
+        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.")
+st.header("💬 Ask a Question About the News")
+st.markdown("Enter a question and provide a news article to get an AI-generated answer.")
 
-question = st.text_input("Ask a question:")
-context = st.text_area("Provide the news article or content for the Q&A:", height=150)
+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"  # Example of a common Q&A model
+    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)
     
-    # Check if the result contains an answer
     if 'answer' in result and result['answer']:
-        st.write("Answer:", result['answer'])
+        st.success(f"✅ Answer: {result['answer']}")
     else:
-        st.write("No answer found in the provided content.")
+        st.warning("⚠️ No answer found in the provided content.")