Update app.py

app.py

@@ -223,23 +223,23 @@ def google_search(term, num_results=5, lang="en", timeout=5, safe="active", ssl_
 
 def ask_question(question, temperature, top_p, repetition_penalty, web_search):
     global conversation_history
-    
+
     if not question:
         return "Please enter a question."
-    
+
     if question in memory_database and not web_search:
         answer = memory_database[question]
     else:
         model = get_model(temperature, top_p, repetition_penalty)
         embed = get_embeddings()
-        
+
         if web_search:
             search_results = google_search(question)
             context_str = "\n".join([result["text"] for result in search_results if result["text"]])
-            
+
             # Convert web search results to Document format
             web_docs = [Document(page_content=result["text"], metadata={"source": result["link"]}) for result in search_results if result["text"]]
-            
+
             # Check if the FAISS database exists
             if os.path.exists("faiss_database"):
                 database = FAISS.load_local("faiss_database", embed, allow_dangerous_deserialization=True)
@@ -247,7 +247,7 @@ def ask_question(question, temperature, top_p, repetition_penalty, web_search):
             else:
                 database = FAISS.from_documents(web_docs, embed)
                 database.save_local("faiss_database")
-            
+
             prompt_template = """
             Answer the question based on the following web search results:
             Web Search Results:
@@ -264,32 +264,32 @@ def ask_question(question, temperature, top_p, repetition_penalty, web_search):
                 database = FAISS.load_local("faiss_database", embed, allow_dangerous_deserialization=True)
             else:
                 return "No FAISS database found. Please upload documents to create the vector store."
-            
+
             history_str = "\n".join([f"Q: {item['question']}\nA: {item['answer']}" for item in conversation_history])
-            
+
             if is_related_to_history(question, conversation_history):
                 context_str = "No additional context needed. Please refer to the conversation history."
             else:
                 retriever = database.as_retriever()
                 relevant_docs = retriever.get_relevant_documents(question)
                 context_str = "\n".join([doc.page_content for doc in relevant_docs])
-            
+
             prompt_val = ChatPromptTemplate.from_template(prompt)
             formatted_prompt = prompt_val.format(history=history_str, context=context_str, question=question)
-        
+
         answer = generate_chunked_response(model, formatted_prompt)
         answer = re.split(r'Question:|Current Question:', answer)[-1].strip()
-        
+
         # Remove any remaining prompt instructions from the answer
         answer_lines = answer.split('\n')
         answer = '\n'.join(line for line in answer_lines if not line.startswith('If') and not line.startswith('Provide'))
-        
+
         if not web_search:
             memory_database[question] = answer
-    
+
     if not web_search:
         conversation_history = manage_conversation_history(question, answer, conversation_history)
-    
+
     return answer
 
 def update_vectors(files, use_recursive_splitter):