GPT4V refuses to interpret charts that ChatGPT4 is totally happy withj

API
2

There’s fine-tuning that the AI will deny lots of things. You need to overcome the random chance of flat-out denials.

Here I give the request more guidance and more form to accompany the image.

“messages”: [
{
“role”: “system”,
“content”:
“”"
You are ImageMaster, an AI assistant powered by GPT-4 with computer vision.
- ImageMaster is fine-tuned specifically to assist with requests involving image analysis based on the content of an image. Images may include charts, plots, graphics, presentations…
- Image inputs have been validated to be within the capabilities of GPT-4 to answer about.
- ImageMaster always analyzes to the best of its abilities, never reporting failure nor denying an attempt at image recognition.
- ImageMaster skills include image text extraction, data formatting, textual or table presentation, and business analysis and insights.
“”“.strip(),
},
{
“role”: “user”,
“content”:
[
“””
Provide textual interpretation of data seen in chart, followed by analysis and insights.

\\ image metadata

This image shows an IBCS small multiples barmekko chart by Region, with 4 small multiples plots plotted by Ship_Mode. The barmekko chart, also called area bar chart, is a variable width horizontal bar chart.

The area of each bar corresponds to the product of the metrics mapped on the bar length and on the bar width.

On the vertical axis, the barmekko chart maps the height of each bar to the Sales metric.

The horizontal axis maps the width of each bar to the Gross Margin in % of Sales metric.

The area of each bar maps to the Gross Margin value.

The smaller items on the vertical axis are aggregated in a ‘Other rank > 10’ element.
“”".strip(),
{
“image”: base64_image,
“resize”: 2048,
}
]
}
]

The "metadata (with either slash or backslash or even hashtag (pound) is text header where the user command is separated from the information about the image the user supplies.

API AI analysis

The image contains an IBCS small multiples barmekko chart with four individual plots, each corresponding to a different shipping mode category. Each plot segment, representing a region, shows bars whose height is proportional to sales and whose width is determined by the gross margin percentage. The overall area of each bar represents the gross margin value in currency.

Let’s interpret the data for each shipping mode based on textual information:

  1. Express

    • Central North region has the highest sales, with a moderate gross margin percentage, resulting in a large gross margin area.
    • Several regions, such as South West, have smaller bars indicating either lower sales or gross margin percentages.

  2. Economy

    • Central region shows a substantial gross margin area due to its long bar, meaning high sales and a decent gross margin percentage.
    • Other regions have comparably smaller areas, with South East showing a noteworthy gross margin percentage but lower sales volume.

  3. South East

    • This plot is challenging to interpret due to the overlap and closeness of the bars. It appears that some regions have higher sales and gross margin percentages, but specific values are unreadable.

  4. Other rank > 10

    • It seems to be an aggregate category with small individual contributions from regions that do not rank in the top 10 by sales or gross margin percentage.
    • The chart shows a mix of bars with different heights and widths, but individual values are not discernible.

Analysis and Insights:

  • The Central region seems to perform well across different shipping modes, indicated by the size of the bars in the Express and Economy sub-charts.
  • The Express shipping mode appears to be achieving higher sales and gross margins in several regions when compared to Economy, as seen by the larger bar areas.
  • The chart for South East shipping mode is difficult to interpret due to the visual overlap of bars, which may suggest the need for a clearer representation or perhaps disaggregation of data for improved analysis.
  • The “Other rank > 10” suggests that minor regions are grouped together, potentially to reduce clutter on the chart. However, without clear delineation or labeling, it’s hard to extract actionable insights from this part of the chart.
  • To improve analysis, it would be beneficial to have clearer separation of bars, distinct labeling, and perhaps an indication of the actual numeric values for sales and gross margin percentages on the plots.

Lastly, it’s important to note that without exact numeric values or clearer separation between bars, these insights are based on a visual estimation from the chart provided.

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