Update pages/llm.py

pages/llm.py

@@ -92,16 +92,16 @@ def get_llm_answer(user_question):
     # Top 3 Suchergebnisse des Retrievers als Context speichern
     context=""+retrieved_docs[0].page_content+retrieved_docs[1].page_content+retrieved_docs[2].page_content
     # Context bereinigen
-    context=context.replace("\n", " ")  
-    context=context.replace("- ", "")
+    #context=context.replace("\n", " ")  
+    #context=context.replace("- ", "")
 
     # Erstelle die Question Answering-Pipeline für Deutsch
-    qa_pipeline = pipeline("question-answering", model="deutsche-telekom/bert-multi-english-german-squad2", tokenizer="deutsche-telekom/bert-multi-english-german-squad2")
+    #qa_pipeline = pipeline("question-answering", model="deutsche-telekom/bert-multi-english-german-squad2", tokenizer="deutsche-telekom/bert-multi-english-german-squad2")
 
     # Frage beantworten mit Q&A Pipeline
-    answer = qa_pipeline(question=user_question, context=context, max_length=200)
+    #answer = qa_pipeline(question=user_question, context=context, max_length=200)
     #antw = translator.translate(answer["answer"],dest='de')
-    return answer["answer"]#antw
+    return content#answer["answer"]#antw
    
 def main():
     st.set_page_config(