Creating Slides With Assistants API And DALL E3
Creating slides with the Assistants API (GPT-4), and DALL·E-3
This notebook illustrates the use of the new Assistants API (GPT-4), and DALL·E-3 in crafting informative and visually appealing slides.
Creating slides is a pivotal aspect of many jobs, but can be laborious and time-consuming. Additionally, extracting insights from data and articulating them effectively on slides can be challenging.
This cookbook recipe will demonstrate how you can utilize the new Assistants API to facilitate the end to end slide creation process for you without you having to touch Microsoft PowerPoint or Google Slides, saving you valuable time and effort!
0. Setup
1. Creating the content
In this recipe, we will be creating a brief fictional presentation for the quarterly financial review of our company, NotReal Corporation. We want to highlight some key trends we are seeing that are affecting the profitability of our company.
Let's say we have the some financial data at our disposal. Let's load in the data, and take a look...
As you can see, this data has quarterly revenue, costs and customer data across different distribution channels. Let's create an Assistant that can act as a personal analyst and make a nice visualization for our PowerPoint!
First, we need to upload our file so our Assistant can access it.
Now, we're ready to create our Assistant. We can instruct our assistant to act as a data scientist, and take any queries we give it and run the necessary code to output the proper data visualization. The instructions parameter here is akin to system instructions in the ChatCompletions endpoint, and can help guide the assistant. We can also turn on the tool of Code Interpreter, so our Assistant will be able to code. Finally, we can specifiy any files we want to use, which in this case is just the financial_data file we created above.
Let's create a thread now, and as our first request ask the Assistant to calculate quarterly profits, and then plot the profits by distribution channel over time. The assistant will automatically calculate the profit for each quarter, and also create a new column combining quarter and year, without us having to ask for that directly. We can also specify the colors of each line.
No we can execute the run of our thread
We can now start a loop that will check if the image has been created. Note: This may take a few minutes
Assistant still working... Assistant still working... Assistant still working... Assistant still working... Assistant still working... Assistant still working... Assistant still working... Assistant still working... Assistant still working... Assistant still working... Assistant still working... Assistant still working... Assistant still working... Assistant still working... Assistant still working... Assistant still working... Assistant still working... Plot created!
Let's see the messages the Assistant added.
[MessageContentImageFile(image_file=ImageFile(file_id='file-0rKABLygI02MgwwhpgWdRFY1'), type='image_file'), , MessageContentText(text=Text(annotations=[], value="The profit has been calculated for each distribution channel by quarter and year. Next, I'll create a line plot to visualize these profits. As specified, I will use green for the 'Online Sales', light red for 'Direct Sales', and light blue for 'Retail Partners' channels. Let's create the plot."), type='text'), , MessageContentText(text=Text(annotations=[], value="The JSON data has been successfully restructured into a tabular dataframe format. It includes the year, quarter, distribution channel, revenue, costs, customer count, and a combined 'Time' representation of 'Year Quarter'. Now, we have the necessary information to calculate the profit (revenue minus cost) by quarter and year.\n\nTo visualize the profit across the different distribution channels with a line plot, we will proceed with the following steps:\n\n1. Calculate the profit for each row in the dataframe.\n2. Group the data by 'Time' (which is a combination of Year and Quarter) and 'Distribution channel'.\n3. Aggregate the profit for each group.\n4. Plot the aggregated profits as a line plot with the distribution channels represented in different colors as requested.\n\nLet's calculate the profit for each row and then continue with the visualization."), type='text'), , MessageContentText(text=Text(annotations=[], value='The structure of the JSON data shows that it is a dictionary with "Year", "Quarter", "Distribution channel", and potentially other keys that map to dictionaries containing the data. The keys of the inner dictionaries are indices, indicating that the data is tabular but has been converted into a JSON object.\n\nTo properly convert this data into a DataFrame, I will restructure the JSON data into a more typical list of dictionaries, where each dictionary represents a row in our target DataFrame. Subsequent to this restructuring, I can then load the data into a Pandas DataFrame. Let\'s restructure and load the data.'), type='text'), , MessageContentText(text=Text(annotations=[], value="The JSON data has been incorrectly loaded into a single-row DataFrame with numerous columns representing each data point. This implies the JSON structure is not as straightforward as expected, and a direct conversion to a flat table is not possible without further processing.\n\nTo better understand the JSON structure and figure out how to properly normalize it into a table format, I will print out the raw JSON data structure. We will analyze its format and then determine the correct approach to extract the profit by quarter and year, as well as the distribution channel information. Let's take a look at the JSON structure."), type='text'), , MessageContentText(text=Text(annotations=[], value="It seems that the file content was successfully parsed as JSON, and thus, there was no exception raised. The variable `error_message` is not defined because the `except` block was not executed.\n\nI'll proceed with displaying the data that was parsed from JSON."), type='text'), , MessageContentText(text=Text(annotations=[], value="It appears that the content of the dataframe has been incorrectly parsed, resulting in an empty dataframe with a very long column name that seems to contain JSON data rather than typical CSV columns and rows.\n\nTo address this issue, I will take a different approach to reading the file. I will attempt to parse the content as JSON. If this is not successful, I'll adjust the loading strategy accordingly. Let's try to read the contents as JSON data first."), type='text'), , MessageContentText(text=Text(annotations=[], value="Before we can calculate profits and visualize the data as requested, I need to first examine the contents of the file that you have uploaded. Let's go ahead and read the file to understand its structure and the kind of data it contains. Once I have a clearer picture of the dataset, we can proceed with the profit calculations. I'll begin by loading the file into a dataframe and displaying the first few entries to see the data schema."), type='text'), , MessageContentText(text=Text(annotations=[], value='Calculate profit (revenue minus cost) by quarter and year, and visualize as a line plot across the distribution channels, where the colors of the lines are green, light red, and light blue'), type='text')]
We can see that the last message (latest message is shown first) from the assistant contains the image file we are looking for. An interesting note here is that the Assistant was able to attempt several times to parse the JSON data, as the first parsing was unsuccessful, demonstrating the assistant's adaptability.
Let's load in the plot!
Nice! So, with just one sentence, we were able to have our assistant use code interpreter to
calculate the profitability, and graph the three lineplots of the various distribution channels.
Now we have a nice visual for our slide, but we want some insights to go along with it.
2. Generating insights
To get insights from our image, we simply need to add a new message to our thread. Our Assistant will know to use the message history to give us some concise takeaways from the visual provided.
Run(id='run_NWoygMcBfHUr58fCE4Cn6rxN', assistant_id='asst_3T362kLlTyAq0FUnkvjjQczO', cancelled_at=None, completed_at=None, created_at=1701827074, expires_at=1701827674, failed_at=None, file_ids=['file-piTokyHGllwGITzIpoG8dok3'], instructions='You are a data scientist assistant. When given data and a query, write the proper code and create the proper visualization', last_error=None, metadata={}, model='gpt-4-1106-preview', object='thread.run', required_action=None, started_at=None, status='queued', thread_id='thread_73TgtFoJMlEJvb13ngjTnAo3', tools=[ToolAssistantToolsCode(type='code_interpreter')])Now, once the run has completed, we can see the latest message
The plot reveals a consistent upward trend in profits for all distribution channels, indicating successful business growth over time. Particularly, 'Online Sales' shows a notable increase, underscoring the importance of digital presence in revenue generation.
Cool! So our assistant was able to identify the noteworthy growth in Online Sales profit, and infer that this shows the importance of a large digital presence. Now let's get a compelling title for the slide.
Run(id='run_q6E85J31jCw3QkHpjJKl969P', assistant_id='asst_3T362kLlTyAq0FUnkvjjQczO', cancelled_at=None, completed_at=None, created_at=1701827084, expires_at=1701827684, failed_at=None, file_ids=['file-piTokyHGllwGITzIpoG8dok3'], instructions='You are a data scientist assistant. When given data and a query, write the proper code and create the proper visualization', last_error=None, metadata={}, model='gpt-4-1106-preview', object='thread.run', required_action=None, started_at=None, status='queued', thread_id='thread_73TgtFoJMlEJvb13ngjTnAo3', tools=[ToolAssistantToolsCode(type='code_interpreter')])And the title is:
"Ascending Profits & Digital Dominance"
3. DALL·E-3 title image
Nice, now we have a title, a plot and two bullet points. We're almost ready to put this all on a slide, but as a final step, let's have DALL·E-3 come up with an image to use as the title slide of the presentation.
Note: DALL·E-3 is not yet available within the assistants API but is coming soon!
We'll feed in a brief description of our company (NotRealCorp) and have DALL·E-3 do the rest!
Cool, now we can add this image to our thread. First, we can save the image locally, then upload it to the assistants API using the File upload endpoint. Let's also take a look at our image
4. Creating the slides
We now have all the content we need to create the slides. While we could simply add a message asking for slides, but let's instead give the assistant a slide template, using the python-pptx library, to use. This will ensure we get a deck in the style we want. See the Extensions section at the end of the notebook for notes on creating the template.
Let's set a few quick variables for our slides. We want the company name, NotRealCorp, to be on the title slide, and the title of the presentation should 'Quartlerly financial planning metting, Q3, 2023'.
And for the data slide, we have:
Here we have a template to create a Title Slide. The template below was created by uploading the image of a desirable title slide to GPT-V, and asking for the python-pptx code to create that template. The inputs to the template are the image_path, title_text, and subtitle_text.
Run(id='run_taLrnOnlDhoywgQFFBOLPlg0', assistant_id='asst_3T362kLlTyAq0FUnkvjjQczO', cancelled_at=None, completed_at=None, created_at=1701827118, expires_at=1701827718, failed_at=None, file_ids=['file-piTokyHGllwGITzIpoG8dok3'], instructions='You are a data scientist assistant. When given data and a query, write the proper code and create the proper visualization', last_error=None, metadata={}, model='gpt-4-1106-preview', object='thread.run', required_action=None, started_at=None, status='queued', thread_id='thread_73TgtFoJMlEJvb13ngjTnAo3', tools=[ToolAssistantToolsCode(type='code_interpreter')])Assistant still working on PPTX... Assistant still working on PPTX... Assistant still working on PPTX... Assistant still working on PPTX... Assistant still working on PPTX... Assistant still working on PPTX... Assistant still working on PPTX... Assistant still working on PPTX... Assistant still working on PPTX... Assistant still working on PPTX... Successfully retrieved pptx_id: file-oa0i63qPH4IaJXYj90aA6L4Q
Now, we have a PPTX file saved with all of our created content!.
Let's look at the screenshots of the .pptx we just created using JUST the assistants API and DALL·E-3. We don't have a seed parameter yet in the Assistants API, so the DALL·E-3 image and wordings will be slightly different from what you see when you run this notebook, due to the non-determinism of LLMs, but the outputs should be directionally the same.
The title slide:
And the data slide:
5. Conclusion
Woo! While these slides could use some formatting tweaks, we have made some great content using the Assistants API, GPT-4 and DALL·E-3. We were able to take a .csv file with financial data, and use our assisant to calculate profit by quarter across distribution channels, plot the results, identify insights and key takeaways from the visualization, and create a summarative title. And, given just a description of our company, NotRealCorp, we used DALL·E-3 to make an awesome title image.
While we are still a ways away from entirely automating this process without a human in the loop, hopefully this notebook can make the slide creation process a bit easier for you. More importantly, this notebook can ideally give you a glimpse into the potential of the assistants API! We're excited to see what you build.
6. Extensions
- When DALL·E-3 is incorporated in the Assistants API, we will have the ability to request the generated title image within the thread.
- GPT-4-Vision is not yet supported in the Assistants API, but could have been used to gather insights from the line plot image.
- GPT-4-Vision was used to generate the
python-pptxtemplate included in this recipe, so a potential extension project could be demonstrating best practices around converting images to slide templates.