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