Update app.py

app.py

@@ -107,13 +107,7 @@ with tab1:
 
     # Load the CSV file
 
-    df = pd.read_csv(uploaded_file)
 
-    # Load the fine-tuned news classifier
-    classifier = pipeline("text-classification", model="Prageeth-1/News_classification.2")
-
-    # Classify each article and store the predictions
-    df["predicted_category"] = df["content"].apply(lambda text: classifier(text)[0]["label"])
 
     
         
@@ -123,10 +117,13 @@ with tab1:
         
     
     else:
-            # Show preview
-        st.subheader("File Preview")
-        st.write(df.head())
-            
+        df = pd.read_csv(uploaded_file)
+    
+        # Load the fine-tuned news classifier
+        classifier = pipeline("text-classification", model="Prageeth-1/News_classification.2")
+    
+        # Classify each article and store the predictions
+        df["predicted_category"] = df["content"].apply(lambda text: classifier(text)[0]["label"])
                     
         # Preprocess and classify
                     
@@ -137,12 +134,12 @@ with tab1:
                     
         # Show distribution
         st.subheader("Class Distribution")
-        class_dist = df['class'].value_counts()
+        class_dist = df['predicted_category'].value_counts()
         st.bar_chart(class_dist)
                     
         # Generate word cloud for each class
         st.subheader("Word Clouds by Category")
-        classes = df['class'].unique()
+        classes = df['predicted_category'].unique()
         cols = st.columns(len(classes))