Update app.py

app.py

@@ -8,8 +8,9 @@ import s3fs
 load_dotenv('myenvfile.env')
 
 openai_api_key = os.environ['OPENAI_API_KEY']
-aws_access_key_id=os.environ['AWS_ACCESS_KEY_ID'],
-aws_secret_access_key=os.environ['AWS_SECRET_ACCESS_KEY']
+aws_access_key_id = os.environ['AWS_ACCESS_KEY_ID']
+aws_secret_access_key = os.environ['AWS_SECRET_ACCESS_KEY']
+
 from llama_index import GPTListIndex, GPTSimpleVectorIndex
 from langchain.agents import load_tools, Tool, initialize_agent
 from langchain.llms import OpenAI
@@ -18,37 +19,30 @@ from langchain.agents import initialize_agent, Tool
 from langchain import OpenAI, LLMChain
 from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader
 
-
-
 index = GPTSimpleVectorIndex.load_from_disk('index.json')
 
-
 def querying_db(query: str):
-	response = index.query(query)
-	return response
-
+    response = index.query(query)
+    return response
 
 tools = [
-	Tool(
-		name="QueryingDB",
-		func=querying_db,
-		description="This function takes a query string as input and returns the most relevant answer from the documentation as output"
-	)
+    Tool(
+        name="QueryingDB",
+        func=querying_db,
+        description="This function takes a query string as input and returns the most relevant answer from the documentation as output"
+    )
 ]
 
 llm = OpenAI(temperature=0)
 
-
 def get_answer(query_string):
-	agent = initialize_agent(tools, llm, agent="zero-shot-react-description")
-	result = agent.run(query_string)
-	return result
-
+    agent = initialize_agent(tools, llm, agent="zero-shot-react-description")
+    result = agent.run(query_string)
+    return result
 
 def qa_app(query):
-	answer = get_answer(query)
-	return answer
-
+    answer = get_answer(query)
+    return answer
 
 inputs = gr.inputs.Textbox(label="Enter your question:")
 output = gr.outputs.Textbox(label="Answer:")