Update app.py

app.py

@@ -72,16 +72,28 @@ def classify_csv(file):
     except Exception as e:
         return None, f"Error: {str(e)}"
 
-def chatbot_response(history, user_input, source):
+def chatbot_response(history, user_input, source, 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
     
-    # Get the context from the source (single article or bulk content)
-    context = context_storage["context"] if source == "Single Article" else context_storage["bulk_context"]
+    # Determine context based on the source
+    context = ""
     
+    if source == "Single Article":
+        # If the source is single article, use the text_input (single article content)
+        context = text_input if text_input else ""
+    
+    elif source == "Bulk Classification":
+        # If the source is bulk articles, use the file input (context for multiple articles)
+        if file_input:
+            # Assuming `classify_csv` method processes the file and sets the bulk context
+            df, _ = classify_csv(file_input)  # This function should return the dataframe and context
+            context = context_storage["bulk_context"]
+    
+    # Make sure there's context for QA pipeline to run
     if context:
         with st.spinner("Finding answer..."):
-            # Pass the user's question and the content from the source (context) to the QA pipeline
+            # Run QA pipeline with the user's question and context from either text_input or file_input
             result = qa_pipeline(question=user_input, context=context)
             answer = result["answer"]