Update app.py

app.py

@@ -11,6 +11,10 @@ import soundfile as sf
 from langchain.agents import AgentExecutor, create_react_agent
 from langchain.tools import BaseTool
 from langchain_groq import ChatGroq
+from langchain.agents import AgentExecutor, initialize_agent, Tool
+from langchain.agents import AgentType
+from langchain_groq import ChatGroq
+from langchain.prompts import PromptTemplate
 from PIL import Image
 from tavily import TavilyClient
 import requests
@@ -130,28 +134,50 @@ def handle_input(user_prompt, image=None, audio=None, websearch=False, document=
         user_prompt = transcription.text
 
     tools = [
-        NumpyCodeCalculator(),
-        ImageGeneration(),
+        Tool(
+            name="Numpy",
+            func=NumpyCodeCalculator()._run,
+            description="Useful for performing numpy computations"
+        ),
+        Tool(
+            name="Image",
+            func=ImageGeneration()._run,
+            description="Useful for generating images based on text descriptions"
+        ),
     ]
 
     # Add the web search tool only if websearch mode is enabled
     if websearch:
-        tools.append(WebSearch())
+        tools.append(Tool(
+            name="Web",
+            func=WebSearch()._run,
+            description="Useful for searching the web for information"
+        ))
 
     # Add the document question answering tool only if a document is provided
     if document:
-        tools.append(DocumentQuestionAnswering(document))
+        tools.append(Tool(
+            name="Document",
+            func=DocumentQuestionAnswering(document)._run,
+            description="Useful for answering questions about a specific document"
+        ))
 
     llm = ChatGroq(model=MODEL, api_key=os.environ.get("GROQ_API_KEY"))
-    agent = create_react_agent(llm, tools)
-    agent_executor = AgentExecutor(agent=agent, tools=tools)
+
+    # Initialize the agent
+    agent = initialize_agent(
+        tools, 
+        llm, 
+        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+        verbose=True
+    )
 
     if image:
         image = Image.open(image).convert('RGB')
         messages = [{"role": "user", "content": [image, user_prompt]}]
         response = vqa_model.chat(image=None, msgs=messages, tokenizer=tokenizer)
     else:
-        response = agent_executor.run(user_prompt)
+        response = agent.run(user_prompt)
 
     return response