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import sqlite3
import streamlit as st
import pandas as pd
import os
from sqlalchemy import create_engine
from sqlalchemy.pool import StaticPool
from langchain_experimental.sql import SQLDatabaseChain
from langchain import OpenAI, SQLDatabase
#####################################
# FUNCTIONS #
#####################################
@st.cache_data()
def load_data(url):
"""
load data from url
"""
df = pd.read_csv(url)
return df
def prepare_data(df):
"""
lowercase columns
"""
df.columns = [x.replace(' ', '_').lower() for x in df.columns]
return df
#####################################
# LOCALS & CONSTANTS #
#####################################
table_name = 'taxlien'
uri = "file::memory:?cache=shared"
#####################################
# HOME PAGE #
#####################################
st.title('REAL ESTATE ~ ASK THE DATASET - FL :house:')
st.subheader('Upload a file to query')
# read file
uploaded_file = st.file_uploader("Choose a csv file")
if uploaded_file is not None:
df = pd.read_csv(uploaded_file)
st.write(df)
# api key
openai_api_key = os.environ["OPENAI_API_KEY"]
# user query
user_q = st.text_input(
"User Query",
help="Enter a question based on the dataset")
# commit data to sql
data = prepare_data(df)
conn = sqlite3.connect(uri)
data.to_sql(table_name, conn, if_exists='replace', index=False)
# create db engine
eng = create_engine(
url='sqlite:///file:memdb1?mode=memory&cache=shared',
poolclass=StaticPool, # single connection for requests
creator=lambda: conn)
db = SQLDatabase(engine=eng)
# create open AI conn and db chain
if openai_api_key:
llm_chain = OpenAI(
openai_api_key=openai_api_key,
temperature=0, # creative scale
max_tokens=300)
db_chain = SQLDatabaseChain(llm=llm_chain, database=db, verbose=True)
# run query and display result
if openai_api_key and user_q:
result = db_chain.run(user_q)
st.write(result)
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