Refactor LLM integration in app.py: replace RunnableSequence with LLMChain and update method call from invoke() to run() for improved functionality and clarity

app.py

@@ -8,7 +8,7 @@ from langchain.schema import AgentAction, AgentFinish, HumanMessage
 from langchain.prompts import BaseChatPromptTemplate
 from langchain.tools import Tool
 from langchain_huggingface import HuggingFacePipeline
-from langchain import RunnableSequence
+from langchain import LLMChain
 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 from langchain.memory import ConversationBufferWindowMemory
 import torch
@@ -154,8 +154,8 @@ output_parser = CustomOutputParser()
 # Initialize HuggingFace pipeline
 llm = HuggingFacePipeline(pipeline=pipe)
 
-# Runnable sequence
-llm_chain = RunnableSequence(llm=llm, prompt=prompt_with_history)
+# LLM chain
+llm_chain = LLMChain(llm=llm, prompt=prompt_with_history)
 tool_names = [tool.name for tool in tools]
 agent = LLMSingleActionAgent(
     llm_chain=llm_chain, 
@@ -178,7 +178,7 @@ if st.button("Submit"):
         with st.spinner("Processing..."):
             try:
                 # Run the agent and get the response
-                response = agent_executor.invoke(query)  # Correct method is `invoke`
+                response = agent_executor.run(query)  # Correct method is `run()`
                 st.success("Response:")
                 st.write(response)
             except Exception as e: