Create app.py
Browse filesAdd application code.
app.py
ADDED
@@ -0,0 +1,82 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
"""
|
2 |
+
This is a streamlit application that allows user to query multiple csv
|
3 |
+
files and return the answer based on the text in the csv files.
|
4 |
+
|
5 |
+
It uses streamlit to create a web application to load the csv files and
|
6 |
+
query them.
|
7 |
+
|
8 |
+
Langchain and OpenAI API are used to generate the answer based on the
|
9 |
+
text in the csv files.
|
10 |
+
"""
|
11 |
+
|
12 |
+
from langchain.agents import create_csv_agent
|
13 |
+
from langchain.chat_models import ChatOpenAI
|
14 |
+
from langchain.agents.agent_types import AgentType
|
15 |
+
from dotenv import load_dotenv
|
16 |
+
import os
|
17 |
+
import pandas as pd
|
18 |
+
import streamlit as st
|
19 |
+
|
20 |
+
|
21 |
+
def main():
|
22 |
+
# set the page title
|
23 |
+
st.set_page_config("Data Analysis Helper \U0001F4CA")
|
24 |
+
st.markdown("# Data Analysis Helper")
|
25 |
+
st.markdown("This tool helps you analyze your CSV files. Please remember to **remove personal information** first, such as names, addresses, phone numbers, emails, etc.")
|
26 |
+
st.markdown("## How to use this tool")
|
27 |
+
st.markdown("1. Upload your CSV file")
|
28 |
+
st.markdown("2. Ask a question about your CSV file")
|
29 |
+
st.markdown("3. Wait for the answer to appear")
|
30 |
+
st.markdown("## Example questions")
|
31 |
+
st.markdown("1. What is the average age?")
|
32 |
+
st.markdown("2. What is the average income?")
|
33 |
+
st.markdown("3. What is the average age of people who live in London?")
|
34 |
+
st.markdown("Go to [this page](https://openai.com/pricing) to get an OpenAI API key.")
|
35 |
+
# text input to ask for openai api key
|
36 |
+
# then hide the input
|
37 |
+
openai_api_key = st.text_input("Enter your OpenAI API key", type="password")
|
38 |
+
|
39 |
+
# load the api key from the .env file
|
40 |
+
load_dotenv()
|
41 |
+
|
42 |
+
# inform the user that the api key is loaded
|
43 |
+
if openai_api_key is not None and openai_api_key != "":
|
44 |
+
st.write("OpenAI API key loaded")
|
45 |
+
|
46 |
+
# inform user that the model being used is the turbo model
|
47 |
+
st.write("Using the gpt-3.5-turbo-0613 model from OpenAI")
|
48 |
+
|
49 |
+
|
50 |
+
# Upload the CSV file
|
51 |
+
csv_file = st.file_uploader("Upload a CSV file", type="csv")
|
52 |
+
|
53 |
+
# if the user has uploaded a csv file then save it to the current directory
|
54 |
+
if csv_file is not None:
|
55 |
+
with open(os.path.join(os.getcwd(), csv_file.name), "wb") as f:
|
56 |
+
f.write(csv_file.getbuffer())
|
57 |
+
st.write("CSV file uploaded to: ", os.path.join(os.getcwd(), csv_file.name))
|
58 |
+
|
59 |
+
# see a preview of the csv file
|
60 |
+
st.write("Preview of the CSV file:")
|
61 |
+
# see a preview of the csv file
|
62 |
+
df = pd.read_csv(os.path.join(os.getcwd(), csv_file.name))
|
63 |
+
st.dataframe(df.head()) # Display the first few rows of the DataFrame
|
64 |
+
|
65 |
+
# create the agent
|
66 |
+
agent = create_csv_agent(
|
67 |
+
ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613"),
|
68 |
+
os.path.join(os.getcwd(), csv_file.name),
|
69 |
+
verbose=True,
|
70 |
+
agent_type=AgentType.OPENAI_FUNCTIONS,
|
71 |
+
)
|
72 |
+
|
73 |
+
# ask the user for a question
|
74 |
+
user_question = st.text_input("Ask a question \U0001F914 about your CSV: ")
|
75 |
+
|
76 |
+
# if the user has asked a question then run the agent
|
77 |
+
if user_question is not None and user_question != "":
|
78 |
+
with st.spinner(text="In progress..."):
|
79 |
+
st.write(agent.run(user_question))
|
80 |
+
|
81 |
+
if __name__ == "__main__":
|
82 |
+
main()
|