Upload app.py

app.py

@@ -36,9 +36,8 @@ search = TavilySearchResults(
       max_results=2,
     )
 tools = [search]
-memory = MemorySaver()
 
-agent_executor = create_react_agent(llm, tools, checkpointer=memory)
+agent_executor = create_react_agent(llm, tools)
 
 # Initialize the wav2vec2 model for Urdu speech-to-text
 pipe = pipeline("automatic-speech-recognition", model="kingabzpro/wav2vec2-large-xls-r-300m-Urdu")
@@ -69,6 +68,7 @@ def translate(target, text):
   res = response.json()
   return res[0]["translations"][0]["text"]
 
+
 def infer(user_input: str):
     '''
         Returns the translated response from the LLM in response to a user query.
@@ -78,7 +78,7 @@ def infer(user_input: str):
             user_input (string): User query.
 
         Returns:
-            JSON Response (Dictionary): Returns a translated response from the LLM.
+            res (string): Returns a translated response from the LLM.
     '''
 
     user_input = translate("en", user_input) # translate user query to english
@@ -87,17 +87,20 @@ def infer(user_input: str):
         [
             (
                 "system",
-                "You're a compassionate AI virtual Assistant"
+                "You are a compassionate and friendly AI virtual assistant. You will provide helpful answers to user queries using the provided tool to ensure the accuracy and relevance of your responses. Please validate the information before providing answers that are not verified."
             ),
             ("human", "{user_input}")
         ]
     )
 
+    runnable = prompt | agent_executor # define a chain
 
+    conversation = RunnableSequence( # wrap the chain along with chat history and user input
+        runnable,
+    )
 
-    response = agent_executor.invoke( # invoke the chain by giving the user input and the chat history
-        {"messages": [HumanMessage(content=user_input)]},
-        config={"configurable": {"thread_id":user_id}}
+    response = conversation.invoke( # invoke the chain by giving the user input and the chat history
+        {"user_input": user_input},
     )
 
     res = translate("ur", response["messages"][-1].content) # translate the response to Urdu