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Update app.py
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import os
import gradio as gr
from langchain.agents.agent_types import AgentType
from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
from langchain_openai import ChatOpenAI
from sklearn.datasets import fetch_openml
gpt35 = ChatOpenAI(
model_name="gpt-3.5-turbo",
api_key=os.environ["OPENAI_API_KEY"],
temperature=0
)
bank_data, _ = fetch_openml(data_id=43718, return_X_y=True, parser="auto")
pandas_agent = create_pandas_dataframe_agent(
llm=gpt35,
df=bank_data,
verbose=False,
agent_type=AgentType.OPENAI_FUNCTIONS,
)
def predict(user_input):
try:
response = pandas_agent.invoke(user_input)
prediction = response['output']
except Exception as e:
prediction = e
return prediction
textbox = gr.Textbox(placeholder="Enter your query here", lines=6)
schema = """
The schema of the table you can query on is presented below:
| Column | Type | Description|
|-----------|--------|------------|
| age | int |Age of the customer|
| job | str |type of job (categorical: "admin.","unknown","unemployed","management","housemaid","entrepreneur","student", "blue-collar","self-employed","retired","technician","services") |
| marital | str |marital status (categorical: "married","divorced","single"; note: "divorced" means divorced or widowed)|
| education | str |(categorical: "unknown","secondary","primary","tertiary")|
| default | yes/no |has credit in default? (binary: "yes","no")|
| balance | int |average yearly balance, in euros (numeric)|
| housing | yes/no |has housing loan? (binary: "yes","no")|
| loan | yes/no |has personal loan? (binary: "yes","no")|
| contact | str |last contact communication type (categorical: "unknown","telephone","cellular")|
| day | int |last contact day of the month (numeric)|
| month | str |last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec")|
| duration | int |last contact duration, in seconds (numeric)|
| campaign | int |number of contacts performed during this campaign and for this client (numeric, includes last contact)|
| pdays | int |number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted)|
| poutcome | str |outcome of the previous marketing campaign (categorical: "unknown","other","failure","success")|
| deposit | yes/no |has the client subscribed a term deposit? (binary: "yes","no")|
"""
demo = gr.Interface(
inputs=textbox, fn=predict, outputs="text",
title="Query BFSI customer information",
description="This web API presents an interface to ask questions on customer information stored in a database.",
article=schema,
examples=[["What is the average balance maintained by our customers?", ""],
["How many customers have subscribed to a term deposit?", ""],
["How many customers have defaulted on loans?", ""],
["Do customers who default maintain a low balance?", ""],
["For how many customers was our last marketing campaign a success?", ""],
["What is the most common marital status of our customers?"]
],
concurrency_limit=8
)
demo.queue()
demo.launch(auth=("demouser", os.getenv('PASSWD')))