Update app.py

app.py

@@ -58,8 +58,7 @@ def classify_csv(file):
         text_column = df.columns[0]  # Assume first column is the text column
         
         df[text_column] = df[text_column].astype(str).apply(clean_text)  # Clean text column
-        df["Encoded Prediction"] = df[text_column].apply(lambda x: news_classifier(x)[0]['label'])
-        df["Decoded Prediction"] = df["Encoded Prediction"].map(label_mapping)
+        df["Decoded Prediction"] = df[text_column].apply(lambda x: label_mapping.get(news_classifier(x)[0]['label'], "Unknown"))
         df["Confidence"] = df[text_column].apply(lambda x: round(news_classifier(x)[0]['score'] * 100, 2))
 
         # Store all text as a single context for QA
@@ -73,29 +72,20 @@ def classify_csv(file):
         return None, f"Error: {str(e)}"
 
 def chatbot_response(history, user_input, text_input=None, file_input=None):
-    # Always use the user input as a question for the QA pipeline
-    user_input = user_input.lower()  # Optionally make it lowercase
-    
-    # Initialize context as empty
+    user_input = user_input.lower()
     context = ""
     
-    # Combine the context from text_input and file_input
     if text_input:
-        context += text_input  # Add the single article text as context
+        context += text_input
     
     if file_input:
-        # Process the CSV file and extract the content for bulk context
-        df, _ = classify_csv(file_input)  # Assuming classify_csv processes the file and gets the context
-        context += context_storage["bulk_context"]  # Append bulk context to the overall context
+        df, _ = classify_csv(file_input)
+        context += context_storage["bulk_context"]
     
-    # Make sure there's context for the QA pipeline
     if context:
         with st.spinner("Finding answer..."):
-            # Run QA pipeline with the user's question and combined context from both text_input and file_input
             result = qa_pipeline(question=user_input, context=context)
             answer = result["answer"]
-            
-            # Append the question and answer to the chat history
             history.append([user_input, answer])
     
     return history, answer