Leverage Code Interpreter Integration with Hatz AI: Features and Tools Guide

The integration of Code Interpreter and Hatz AI unlocks a wealth of opportunities for users looking to supercharge their workflow with the power of Hatz AI. Whether you're analyzing data, running custom scripts, or managing files in a secure environment, this fusion brings exceptional capabilities to your fingertips.

This guide will walk you through the features and tools provided by the Code Interpreter tool with Hatz AI's capabilities.

What is the Code Interpreter Tool?

The Code Interpreter tool is a feature-rich integration that allows users to execute Python code in a sandboxed environment. It supports tools for running scripts, managing files, and interacting with the terminal. With Hatz AI now connected to this tool, you can automate data analysis, perform complex calculations, and generate visualizations within a secure, cloud-based notebook.

How and What Can a User Access?

The Code Interpreter tool is an AI Powered Search tool that is available in the Tool Selector and does not require user authentication.

Features of the Code Interpreter Integration Tool

1. Secure Sandbox Creation

Create Sandbox: Spin up a secure sandbox environment to execute Python code within a Jupyter notebook cell. This is your personal computer in the cloud, perfect for running scripts and managing files.

2. Advanced Code Execution

Execute Code: Run Python code within the created sandbox. The tool returns the results, standard output, standard error, and any errors, allowing for robust script execution and debugging. For generating charts or images, it is recommended to save them as files rather than displaying them directly.

3. Seamless File Management

Upload File: Easily upload files to the /home/user directory in your sandbox environment.
Get File: Retrieve and download files from the /home/user directory in your sandbox.

4. Terminal Command Execution

Run Terminal Command: Execute terminal commands directly within the sandbox. This tool returns the standard output, standard error, and error code, enabling powerful system-level operations.

Lets Ask Python Code Interpreter what it can do .. try this in chat yourself!

Turn the tool on and say "Hey Python Code Interpreter can you tell me what you what can you do? Can you focus on File Generation."

Of course. I don't have Microsoft Office or Adobe Acrobat installed. Instead, I write and execute Python code that uses specific libraries designed to build these files from scratch.

Here’s a breakdown of how I create each one, including the key libraries and a simplified code example for each.

1. How I Create a Word Document (.docx)

To generate a Word document, I primarily use a library called python-docx. This library allows me to programmatically add and format elements like headings, paragraphs, tables, and images.

The Process:

Initialize a Document: I create a blank Document object.
Add Content: I use commands to add elements to this document, such as doc.add_heading('Title') or doc.add_paragraph('Some text.'). I can also apply formatting like bold or italics.
Save the File: Once the content is added, I save the Document object as a .docx file in my sandbox environment.

Simplified Code Example:

# Import the library from docx import Document  # 1. Initialize a new document document = Document()  # 2. Add content document.add_heading('My Word Document Title', level=1) p = document.add_paragraph('This is a paragraph. ') p.add_run('This text is bold.').bold = True p.add_run(' And this text is italic.').italic = True  # 3. Save the document document.save('/home/user/mydocument.docx')  print("Word document 'mydocument.docx' created successfully.")

2. How I Create a PowerPoint Presentation (.pptx)

For PowerPoint presentations, I use a library named python-pptx. It allows me to create presentations, add slides with different layouts, and populate them with text and images.

The Process:

Initialize a Presentation: I start by creating a Presentation object.
Choose Layouts & Add Slides: I select a slide layout (e.g., "Title Slide" or "Title and Content") and add a new slide to the presentation.
Populate Placeholders: I add text or other content to the specific placeholder shapes on the slide (like the title box or the main content area).
Save the File: Finally, I save the Presentation object as a .pptx file.

Simplified Code Example:

# Import the library from pptx import Presentation from pptx.util import Inches  # 1. Initialize a new presentation prs = Presentation()  # 2. Add a title slide title_slide_layout = prs.slide_layouts[0] # The first layout is 'Title Slide' slide = prs.slides.add_slide(title_slide_layout)  # 3. Populate the title and subtitle title = slide.shapes.title subtitle = slide.placeholders[1] title.text = "My Awesome Presentation" subtitle.text = "Created using Python in Hatz AI"  # 4. Save the presentation prs.save('/home/user/mypresentation.pptx')  print("PowerPoint file 'mypresentation.pptx' created successfully.")

3. How I Create a PDF Document (.pdf)

There are several libraries for PDF generation, with a popular one being fpdf2. This library treats a PDF like a canvas where I can place text, shapes, and images at specific coordinates.

The Process:

Initialize a PDF Object: I create an FPDF object, which represents the document.
Add a Page: I must explicitly add a new page to the document.
Set Font and Add Text: I set the font style and size, and then I write text into "cells" on the page. I control the layout by specifying the position and dimensions of these cells.
Save the File: I output the final result as a .pdf file.

Simplified Code Example:

# Import the library from fpdf import FPDF  # 1. Initialize a new PDF pdf = FPDF()  # 2. Add a page pdf.add_page()  # 3. Set font and add text pdf.set_font('Helvetica', 'B', 16) # Font, Style, Size pdf.cell(0, 10, 'My PDF Document Title', align='C', ln=True)  pdf.set_font('Helvetica', '', 12) pdf.multi_cell(0, 10, 'This is a paragraph in my PDF file. I can write multiple lines of text here. The fpdf2 library gives me control over the layout.')  # 4. Save the PDF pdf.output('/home/user/mypdf.pdf')  print("PDF file 'mypdf.pdf' created successfully.")

Would you like me to create one of these files for you as a demonstration?

Why Use the Code Interpreter Integration with Hatz AI?

Connecting your tasks to and leveraging Hatz AI allows you to:

Automate Complex Data Tasks: Automate data cleaning, analysis, and visualization by executing Python scripts.
Drive Data-Driven Decisions: AI-powered analysis uncovers actionable trends and patterns hidden in your data files.
Enhance Productivity: Use intelligent automation to handle complex workflows and focus your team on high-value activities like strategy and interpretation.
Personalize Data Outputs: Utilize AI to generate custom reports, charts, and files based on your specific needs.
Streamline Development: The seamless integration ensures that you can prototype and run code snippets quickly and efficiently.

Real-Life Use Cases

Scenario 1: Automated Data Analysis and Reporting

A financial analyst needs to process a daily sales CSV file. With the Code Interpreter toolkit, they can upload the CSV file, execute a Python script to perform calculations and generate a summary plot, and then download the resulting image file and a processed CSV. Hatz AI can automate this entire workflow, running it at a scheduled time each day.

Scenario 2: AI-Powered Code Prototyping

A developer is working on a new algorithm. They can use Hatz AI to write and execute Python code snippets in the sandbox, quickly iterating on their ideas without needing to set up a local development environment. The AI can help debug the code by analyzing the output and errors from the Execute Code tool.

Scenario 3: Secure File Processing

A marketing team needs to process a list of user feedback from a text file to identify common themes. They can upload the file, use a terminal command like grep to filter for specific keywords, and then run a Python script to perform sentiment analysis. All of this is done within the secure sandbox, ensuring the data remains confidential.

Hatz AI & Code Interpreter: Use Case List

This list outlines practical applications of the Hatz AI and Code Interpreter integration. It is categorized by the core features available through the tool, providing examples of direct Chat Prompts for quick, ad-hoc tasks and Workflow Automations for more structured, repeatable processes.

1. Data Analysis & Visualization

Use Case	Description	Chat Prompt Example	Workflow for Automation
Quick Data Summary	Get key statistical insights from a data file instantly without writing any code manually.	`Upload this file 'sales_data.csv' and give me a summary of the 'Revenue' column, including mean, median, and total sum.`	Automated Data Profile Workflow: 1. Prompts the user to upload a CSV file. 2. Runs a Python script to load the file into a pandas DataFrame. 3. Generates a descriptive statistics summary for all numerical columns. 4. Presents the summary table to the user.
Chart Generation	Create and save visualizations like bar charts, line graphs, or scatter plots from your data for reports and presentations.	`Create a line chart from 'user_growth.csv' with 'Date' on the x-axis and 'Active Users' on the y-axis. Save it as 'growth_chart.png'.`	Monthly Report Chart Generation Workflow: 1. Prompts the user to upload their monthly data file (e.g., `october_sales.csv`). 2. Executes a Python script using Matplotlib or Seaborn to generate a predefined chart (e.g., a bar chart of sales by product). 3. Saves the chart as an image file (e.g., `october_sales_chart.png`). 4. Provides a download link for the generated image file.
Data Filtering & Export	Extract a specific subset of data from a larger file and save it as a new, clean file for focused analysis.	`Filter 'all_transactions.csv' to only include rows where 'Country' is 'USA' and 'Amount' is over 500. Save the result as 'usa_high_value.csv'.`	Data Segmentation Workflow: 1. Prompts the user to upload a data file and specify a column and value to filter by. 2. Runs a Python script that filters the data based on the user's input. 3. Saves the filtered data to a new CSV file. 4. Presents the new file for download.

2. File Processing & Management

Use Case	Description	Chat Prompt Example	Workflow for Automation
Log File Analysis	Quickly search through large log files to find specific errors or information using powerful terminal commands.	`Upload 'server.log' and search for all lines containing the word 'FATAL_ERROR'.`	Log File Error Check Workflow: 1. User uploads a log file. 2. The workflow runs the terminal command `grep 'ERROR' /home/user/[uploaded_log_file.log]`. 3. It then runs `grep 'WARNING' /home/user/[uploaded_log_file.log]`. 4. Presents a summary of all found error and warning lines.
File Compression	Package multiple files (e.g., reports, charts, data) into a single zip archive for easy download and sharing.	`I've uploaded 'report.pdf' and 'data.csv'. Please compress them into a single file named 'project_archive.zip'.`	Report Packaging Workflow: 1. Prompts the user to upload all files for the report package. 2. Once uploaded, it runs the terminal command `zip report_package.zip ` to compress all files in the directory. 3. Provides a download link for `report_package.zip`. 4. Runs `rm ` to clean the sandbox for the next task.
Text Data Extraction	Process unstructured text files to extract specific information, like email addresses or URLs.	`Scan 'feedback.txt' and pull out all the email addresses into a list.`	Feedback Theme Identification Workflow: 1. User uploads a text file containing user feedback. 2. Runs a terminal command like `grep` to filter for lines containing keywords (e.g., "feature request", "bug"). 3. Runs a Python script to perform basic sentiment analysis on the filtered lines. 4. Presents a summary of themes and sentiments.

3. Code Prototyping & Execution

Use Case	Description	Chat Prompt Example	Workflow for Automation
On-the-Fly Scripting	Test a Python code snippet quickly without setting up a local development environment.	`Run this Python code and show me the output: <br> import math <br> print(math.sqrt(256))`	Code Testing Workflow: 1. Prompts the user to paste their Python code snippet. 2. Executes the code within the secure sandbox. 3. Returns the standard output, standard error, and any execution errors for immediate debugging.
Custom Calculations	Perform complex financial, scientific, or engineering calculations by executing a custom script.	`I've uploaded 'loan_data.csv'. Run a script to calculate the monthly amortization for each loan and add it as a new column.`	Financial Modeling Workflow: 1. User uploads a file with financial parameters. 2. User provides a Python script or selects a predefined model (e.g., 'Mortgage Calculator'). 3. The workflow executes the script on the data file. 4. Generates a new CSV with the calculated results and provides it for download.
Dependency Management	Install and use a specific Python library from PyPI for a specialized task.	`Install the 'Pillow' library, then upload 'logo.png' and resize it to 100x100 pixels. Save the new file as 'logo_small.png'.`	Image Processing Workflow: 1. Prompts user to upload an image. 2. Runs the terminal command `pip install Pillow`. 3. Executes a Python script that opens the uploaded image, resizes it, and saves the new version. 4. Provides a download link for the processed image.