Update app.py

app.py

@@ -3,13 +3,12 @@ import streamlit as st
 import pandas as pd
 import sqlite3
 import logging
-import textwrap  # Added import for textwrap
 from langchain.llms import OpenAI
 from langchain.chat_models import ChatOpenAI
-from langchain.chains import SQLDatabaseChain
+from langchain.chains.sql_database.base import SQLDatabaseChain  # Updated import
 from langchain.prompts import PromptTemplate
 from langchain.chains import LLMChain
-from langchain.sql_database.base import SQLDatabase
+from langchain.sql_database import SQLDatabase
 
 # Initialize logging
 logging.basicConfig(level=logging.INFO)
@@ -72,7 +71,7 @@ def process_input():
             st.session_state.history.append({"role": "assistant", "content": response})
 
             # Generate insights based on the response
-            insights_template = textwrap.dedent("""
+            insights_template = """
             You are an expert data analyst. Based on the user's question and the response provided below, generate a concise analysis that includes key data insights and actionable recommendations. Limit the response to a maximum of 150 words.
 
             User's Question: {question}
@@ -81,7 +80,7 @@ def process_input():
             {response}
 
             Concise Analysis:
-            """)
+            """
             insights_prompt = PromptTemplate(template=insights_template, input_variables=['question', 'response'])
             insights_chain = LLMChain(llm=llm, prompt=insights_prompt)