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import openai
# Set up the OpenAI API credentials
openai.api_key = "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"
# Code snippet
code = """
from tempfile import NamedTemporaryFile
from langchain.agents import create_csv_agent
from langchain.llms import OpenAI
from dotenv import load_dotenv
import os
import streamlit as st
import pandas as pd
def main():
load_dotenv()
# Load the OpenAI API key from the environment variable
api_key = os.getenv("OPENAI_API_KEY")
if api_key is None or api_key == "":
st.error("OPENAI_API_KEY is not set")
return
st.set_page_config(page_title="Insightly")
st.sidebar.image("/home/oem/Downloads/insightly_wbg.png", use_column_width=True)
st.header("Data Analysis 📈")
csv_files = st.file_uploader("Upload CSV files", type="csv", accept_multiple_files=True)
if csv_files:
llm = OpenAI(temperature=0)
user_input = st.text_input("Question here:")
# Iterate over each CSV file
for csv_file in csv_files:
with NamedTemporaryFile(delete=False) as f:
f.write(csv_file.getvalue())
f.flush()
df = pd.read_csv(f.name)
# Perform any necessary data preprocessing or feature engineering here
# You can modify the code based on your specific requirements
# Example: Accessing columns from the DataFrame
# column_data = df["column_name"]
# Example: Applying transformations or calculations to the data
# transformed_data = column_data.apply(lambda x: x * 2)
# Example: Using the preprocessed data with the OpenAI API
# llm_response = llm.predict(transformed_data)
if user_input:
# Pass the user input to the OpenAI agent for processing
agent = create_csv_agent(llm, f.name, verbose=True)
response = agent.run(user_input)
st.write(f"CSV File: {csv_file.name}")
st.write("Response:")
st.write(response)
if __name__ == "__main__":
main()
"""
# Retrieve the embeddings
response = openai.Completion.create(
model="gpt-3.5-turbo",
documents=[code],
num_completions=1,
return_prompt=True,
return_sequences=False,
expand_prompt=False
)
# Extract the embeddings from the response
embeddings = response.choices[0].embedding
# Print the embeddings
print(embeddings) |