Update app.py

app.py

@@ -3,7 +3,7 @@ from transformers import pipeline
 from langchain_huggingface import HuggingFacePipeline, ChatHuggingFace
 from langchain.tools import tool
 from langchain_core.output_parsers import JsonOutputParser
-from langchain.agents import AgentExecutor, create_react_agent
+from langchain.agents import AgentExecutor, create_react_agent, initialize_agent, AgentType
 from youtube_transcript_api import YouTubeTranscriptApi
 import chess, chess.engine
 from bs4 import BeautifulSoup
@@ -152,16 +152,16 @@ class BasicAgent:
         prompt = f"{self.system_prompt}Q: {question}\nA:"
         #out = self.generator(prompt, max_new_tokens=16, return_full_text=False)
         #return out[0]["generated_text"].strip()
-        agent = create_react_agent(llm=self.llm, tools=TOOLS, prompt=prompt)
-        agent = AgentExecutor(
-            agent=agent,
-            tools=TOOLS,
-            verbose=True,
-            return_intermediate_steps=False
-        )
+        # build a zero-shot-react-description agent for LLM+tools
+        agent_executor = initialize_agent(tools=TOOLS, llm=self.llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
+        # simply run the agent on the user’s question
+        answer = agent_executor.run(question)
+        return answer.strip()
+        #agent = create_react_agent(llm=self.llm, tools=TOOLS, prompt=prompt)
+        #agent = AgentExecutor(agent=agent, tools=TOOLS, verbose=True, return_intermediate_steps=False)
         #agent = AgentExecutor(agent=self.llm, tools=TOOLS, prompt=prompt, verbose=False, return_intermediate_steps=False)
-        result = agent.invoke({"input": question})
-        return JsonOutputParser().parse(result)
+        #result = agent.invoke({"input": question})
+        #return JsonOutputParser().parse(result)
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """