Update temp.py

temp.py

@@ -1,41 +1,44 @@
-from langchain_community.utilities import SerpAPIWrapper
-from langgraph.prebuilt import create_react_agent
-import getpass
-import os
-import sqlite3
-from langchain_openai import ChatOpenAI
-from langchain_community.tools.tavily_search import TavilySearchResults
-from langchain_core.messages import HumanMessage,AIMessage
-from langchain.agents import initialize_agent
-from langchain.agents.agent_types import AgentType
-from langchain_community.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
-from langchain_community.utilities.sql_database import SQLDatabase
-from langchain_openai import ChatOpenAI
-from langchain_groq import ChatGroq
-
-from dotenv import load_dotenv
-load_dotenv('.env')
-
-class Script:
-    def __init__(self):
-        self.model = ChatOpenAI(model="gpt-4o-mini")
-        self.groq = ChatGroq(model='llama3-70b-8192')
-        self.db = SQLDatabase.from_uri("sqlite:///sample_database.db")
-        self.toolkit = SQLDatabaseToolkit(db=self.db, llm=self.model)
-        search = TavilySearchResults(max_results=2)
-        tools = self.toolkit.get_tools()+[search]
-        self.agent = create_react_agent(self.model, tools)
-
-    def history(self,hist):
-        message = []
-        for i in hist[-5:]:
-            if(i['role']=='user'):
-                message+=[HumanMessage(content=i['content'])]
-            else:
-                message+=[AIMessage(content=i['content'])]
-        return message
-
-    def request(self,message):
-        message = self.history(message)
-        response = self.agent.invoke({"messages": message})
-        return response["messages"][-1].content
+from langchain_community.utilities import SerpAPIWrapper
+from langgraph.prebuilt import create_react_agent
+import getpass
+import os
+import sqlite3
+from langchain_openai import ChatOpenAI
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_core.messages import HumanMessage,AIMessage
+from langchain.agents import initialize_agent
+from langchain.agents.agent_types import AgentType
+from langchain_community.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
+from langchain_community.utilities.sql_database import SQLDatabase
+from langchain_openai import ChatOpenAI
+from langchain_groq import ChatGroq
+
+from dotenv import load_dotenv
+load_dotenv('.env')
+
+class Script:
+    def __init__(self):
+        self.model = ChatOpenAI(model="gpt-4o-mini")
+        self.groq = ChatGroq(model='llama3-70b-8192')
+        self.db = SQLDatabase.from_uri("sqlite:///sample_database.db")
+        self.toolkit = SQLDatabaseToolkit(db=self.db, llm=self.model)
+        search = TavilySearchResults(max_results=2)
+        tools = self.toolkit.get_tools()+[search]
+        self.agent = create_react_agent(self.model, tools)
+
+    def history(self,hist):
+        message = []
+        for i in hist[-5:]:
+            if(i['role']=='user'):
+                message+=[HumanMessage(content=i['content'])]
+            else:
+                message+=[AIMessage(content=i['content'])]
+        return message
+
+    def request(self,message):
+        message = self.history(message)
+        try :
+            response = self.agent.invoke({"messages": message})
+            return response["messages"][-1].content
+        except:
+            return 'Sorry unable to process the request'