Update app.py

app.py

@@ -4,6 +4,7 @@ from langchain.text_splitter import CharacterTextSplitter
 from langchain.chains.question_answering import load_qa_chain
 from langchain.llms import OpenAI
 from dotenv import load_dotenv
+import logging
 import os
 import subprocess
 
@@ -61,55 +62,8 @@ doc_retriever = vectordb.as_retriever()
 from langchain.chains import RetrievalQA
 shakespeare_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=doc_retriever)
 
-# from langchain.utilities import SerpAPIWrapper
-# search = SerpAPIWrapper()
-
-# from langchain.agents import initialize_agent, Tool
-# from langchain.agents import AgentType
-# from langchain.tools import BaseTool
-# from langchain.llms import OpenAI
-# from langchain import LLMMathChain, SerpAPIWrapper
-
-# tools = [
-#     Tool(
-#         name = "Shakespeare QA System",
-#         func=shakespeare_qa.run,
-#         description="useful for when you need to answer questions about Shakespeare's works. Input should be a fully formed question."
-#     ),
-#     Tool(
-#         name = "SERP API Search",
-#         func=search.run,
-#         description="useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question."
-#     ),
-# ]
-
-# from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory
-# memory = ConversationBufferMemory(memory_key="chat_history")
-# readonlymemory = ReadOnlySharedMemory(memory=memory)
-
-# from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
-
-# prefix = """Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:"""
-# suffix = """Begin!"
-
-# {chat_history}
-# Question: {input}
-# {agent_scratchpad}"""
-
-# prompt = ZeroShotAgent.create_prompt(
-#     tools, 
-#     prefix=prefix, 
-#     suffix=suffix, 
-#     input_variables=["input", "chat_history", "agent_scratchpad"]
-# )
-
-
-# from langchain import OpenAI, LLMChain, PromptTemplate
-# llm_chain = LLMChain(llm=llm, prompt=prompt)
-# agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
-# agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)
-
 def make_inference(query):
+    logging.info(query)
     return(shakespeare_qa.run(input=query))
 
 if __name__ == "__main__":