Mastering File Upload Analysis with PandasAI, OpenAI, and Streamlit

Mastering File Upload Analysis with PandasAI, OpenAI, and Streamlit

Table of Contents:

1. Introduction
2. Setting Up the Streamlit UI
3. Uploading and Analyzing a CSV File 3.1. Creating the File Uploader Widget 3.2. Reading the CSV File into a Pandas DataFrame
4. Entering a Prompt and Generating Responses 4.1. Creating the Prompt Text Area Widget 4.2. Adding a Generate Button 4.3. Sending the Prompt and DataFrame to OpenAI 4.4. Displaying the Response from OpenAI
5. Adding a Spinner for Response Time
6. Generating Charts with Matplotlib 6.1. Setting Up Matplotlib Backend 6.2. Plotting the Survival Counts for Males and Females
7. Conclusion

Introduction

In this tutorial, we will learn how to build a small Streamlit application that allows users to upload a CSV file and enter Prompts to analyze the data within that file. We will utilize the pandasAI library to send the prompts to a language model and receive responses Based on the data. Streamlit is an open-source Python library that simplifies the creation and sharing of custom web apps for machine learning and data science.

Setting Up the Streamlit UI

To start, we need to set up the Streamlit UI. We will import the Streamlit library and use the st.title function to set the title of our application to "Prompt-driven Analysis with PandasAI". We will then use the Streamlit command line tool to run our script as a Streamlit application.

Uploading and Analyzing a CSV File

Next, we will focus on allowing users to upload a CSV file and analyze its data. We will Create a file uploader widget using the st.file_uploader function and set its label to "Upload a CSV file for analysis". We will limit the allowed file extensions to CSV files.

Once a file is uploaded, we will check if it is not None and Read it into a pandas DataFrame using the pd.read_csv function. We will then display the head of the DataFrame using the st.write function. We will also add a section below the DataFrame to allow users to enter a prompt.

Entering a Prompt and Generating Responses

We will create a text area widget using the st.text_area function for users to enter their prompts. We will set the initial text to "Enter your prompt". We will also add a generate button using the st.button function with the label "Generate".

When the generate button is clicked, we will check if a prompt has been entered. If a prompt exists, we will display a "PandasAI is generating an answer, please wait" message using the st.write function. If no prompt is entered, we will display a warning message using the st.warning function.

To generate a response, we will use the pandasAI.run method. We will pass the DataFrame and the prompt as parameters to the method. We will then display the response using the st.write function.

Adding a Spinner for Response Time

To improve the user experience while waiting for a response, we will add a spinner using the st.spinner Context manager. We will display the spinner with the message "Generating a response" while the request is in flight. Once the response is received, we will display it using the st.write function.

Generating Charts with Matplotlib

If a prompt is entered that asks for a Chart, we will use Matplotlib to generate the chart and display it on a separate window. We will set the Matplotlib backend to TKinter, a Python standard library module for building user interfaces on operating systems.

Conclusion

In this tutorial, we have learned how to build a Streamlit application that allows users to upload a CSV file, enter prompts, and generate responses using PandasAI and OpenAI. We have also explored how to display charts generated from prompts using Matplotlib. By utilizing Streamlit and PandasAI, users can easily analyze data and obtain insights without writing code or having deep knowledge of data science techniques.

Highlights:

• Build a Streamlit application with file upload and prompt-driven analysis capabilities
• Utilize the pandasAI library to send prompts and receive responses from a language model
• Visualize data with charts generated using Matplotlib
• Streamline data analysis for non-technical users

FAQ:

Q: Can I upload any CSV file for analysis? A: Yes, the application allows users to upload any CSV file for analysis.

Q: How long does it take to generate a response? A: The response time may vary depending on the complexity of the prompt and the server load. Please be patient while waiting for a response.

Q: Can I plot different types of charts? A: Currently, the application supports bar charts using the Matplotlib backend. If you have suggestions for other types of charts, please let us know.