Update app.py

app.py

@@ -33,7 +33,7 @@ lemmatizer = WordNetLemmatizer()
 # Load models (cache them to avoid reloading on every interaction)
 @st.cache_resource
 def load_classification_model():
-    model_name = "Imasha17/News_classification.3"  # Replace with your model path
+    model_name = "Imasha17/News_classification.4"  # Replace with your model path
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     model = AutoModelForSequenceClassification.from_pretrained(model_name)
     return pipeline("text-classification", model=model, tokenizer=tokenizer)
@@ -153,7 +153,7 @@ with tab1:
     else:
         df = pd.read_csv(uploaded_file)
         # Load the fine-tuned news classifier
-        classifier = pipeline("text-classification", model="Imasha17/News_classification.3")
+        classifier = pipeline("text-classification", model="Imasha17/News_classification.4")
 
         # Preprocessing steps
         df["cleaned_content"] = df["content"].str.lower()
@@ -212,18 +212,20 @@ with tab1:
     
         # Keep only necessary columns
         df = df[['content','Class']]
-
+        
+        #Preview Uploaded Data
         st.subheader("Preview Uploaded Data")
         st.dataframe(df.head(5))
 
-    
+        #show Classification Results
         st.subheader("Classification Results")
         st.write(df)
     
         st.subheader("Class Distribution")
         class_dist = df['Class'].value_counts()
         st.bar_chart(class_dist)
-        
+
+        #download csv file
         st.subheader("Download Results")
         csv = df.to_csv(index=False).encode('utf-8')
         st.download_button(