Update src/streamlit_app.py

src/streamlit_app.py

@@ -8,31 +8,38 @@ from nltk.stem import WordNetLemmatizer
 import re
 import string
 
-import os
-from huggingface_hub import login
+st.set_page_config(page_title="Daily Mirror News Classifier", page_icon="📰")
 
-hf_token = os.getenv("HF_TOKEN")
-if hf_token:
-    login(hf_token)
+# ====================== PREPROCESSING ======================
 
-# ====================== PREPROCESSING (Same as Task 2) ======================
 
-# ====================== LOAD FINE-TUNED MODEL ======================
-@st.cache_resource
+# ====================== LOAD MODEL (with better error handling) ======================
+@st.cache_resource(show_spinner=False)
 def load_model():
-    model_name = "Ginidu2003/Distilbert-Base-News-classifier"   # ← Your exact model name
-    return pipeline(
-        "text-classification",
-        model=model_name,
-        device=0 if torch.cuda.is_available() else -1
-    )
+    model_name = "Ginidu2003/Distilbert-Base-News-classifier"   # ← Make sure this is exact
+    try:
+        pipe = pipeline(
+            "text-classification",
+            model=model_name,
+            device=0 if torch.cuda.is_available() else -1
+        )
+        st.success(f"✅ Model loaded successfully: {model_name}")
+        return pipe
+    except Exception as e:
+        st.error(f"❌ Failed to load model: {model_name}")
+        st.error(f"Error: {str(e)}")
+        st.info("Make sure the model is Public and the name is correct.")
+        return None
 
 classifier = load_model()
 
-# ====================== STREAMLIT APP ======================
+# ====================== APP ======================
 st.title("📰 Daily Mirror News Classifier")
 st.subheader("Classify news into Business, Opinion, Political Gossip, Sports, or World News")
 
+if classifier is None:
+    st.stop()
+
 st.markdown("**Upload a CSV file** with a column named `content`")
 
 uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
@@ -46,29 +53,23 @@ if uploaded_file is not None:
     if 'content' not in df.columns:
         st.error("Your CSV must have a column named 'content'")
     else:
-        with st.spinner("Preprocessing and classifying..."):
-            # Apply same preprocessing as Task 2
+        with st.spinner("Classifying news..."):
             #df['clean_content'] = df['content'].apply(preprocess_text)
             
-            # Classify
             predictions = []
             for text in df['content']:
-                if text.strip() == "":
+                if not text.strip():
                     predictions.append("Unknown")
                 else:
                     result = classifier(text)[0]
                     predictions.append(result['label'])
             
             df['class'] = predictions
-            
-            # Drop helper column
             #df = df.drop(columns=['clean_content'], errors='ignore')
             
             st.success("✅ Classification completed!")
-            st.write("### Preview of classified data")
             st.dataframe(df.head())
 
-            # Download button
             csv = df.to_csv(index=False).encode('utf-8')
             st.download_button(
                 label="📥 Download output.csv",