shreyasiv commited on
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693f3aa
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1 Parent(s): b1b35e1

Delete app.py

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  1. app.py +0 -56
app.py DELETED
@@ -1,56 +0,0 @@
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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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-
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- def main():
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- load_dotenv()
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-
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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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-
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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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-
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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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-
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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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-
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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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-
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- # Example: Accessing columns from the DataFrame
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- # column_data = df["column_name"]
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-
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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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-
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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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-
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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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-
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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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-
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- if __name__ == "__main__":
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- main()