I tried a few helpful applications of Google Bards Image Recognition capabilities for coders. I don’t know about you but I often see beautiful plots (e.g., in research papers or data science reports) and wonder how I could recreate them.

Well, Google Bard to the rescue!

## β Reverse Engineer Exponential Plot in 2D

First, let’s try a simple exponential curve.

**Input Image:**

I passed this chart along with the following prompt in Google Bard:

π§βπ»Prompt:`Reverse engineer the code leading to this graphic`

So Google Bard suggests the following code to generate the graph in Python.

**π€ Bard Output Code:**

import matplotlib.pyplot as plt import numpy as np def exponential_function(x): return np.exp(x) x_values = np.linspace(0, 10, 100) y_values = exponential_function(x_values) plt.plot(x_values, y_values) plt.xlabel('x') plt.ylabel('y') plt.title('Exponential Curve') plt.show()

This code from Google Bard generates the following chart.

Insane! π€― Google Bard figured out the exact exponential curve by looking at the input chart.

This is really advanced if you think about it. I was particularly impressed by the fact that Bard even got the function right leading to the chart — not only the rough code to plot a similar chart.

So if you needed one more proof of the deep intelligence of large language models (LLMs) that’s way beyond the trivial probabilistic prediction of the next word or character, here it is.

Let’s try another more complex (higher-order) curve:

## π§ Reverse Engineer Higher-Order Curve

Now let’s use a higher-order cubic function plot and pass it into Google Bard to see if it can generate the code that produces it.

**Input Image:**

π§βπ» Prompt:`Give me the code leading to this output`

Google Bard spits out the following code. π

**π€ Bard Output Code:**

import matplotlib.pyplot as plt import numpy as np # Create the data x = np.linspace(-10, 10, 100) y = x**2 # Plot the data plt.plot(x, y) # Add a title plt.title('Plot of a Higher-Order Function') # Add labels to the axes plt.xlabel('X') plt.ylabel('f(x)') # Show the plot plt.show()

This yields the following image:

Some things are correct, such as the x-axis and the labels. Other things are incorrect or imprecise such as the y-axis and the plot — even though it figured out that it’s a higher-order function.

## β 3D Normal Distribution

Next, I tried a more complex 3D plot of a multivariate normal distribution. You can read my blog tutorial on how to do this here.

π‘ **Recommended**: How to Plot a 3D Normal Distribution in Python?

This is the image I passed into Google Bard.

**Input Image:**

And the Bard prompt:

π§βπ»Prompt:`Reproduce the code that generates the image`

**π€ Bard Output Code:**

import plotly.graph_objects as go fig = go.Figure(data=[go.Cone( x=[1, 2, 3], y=[1, 2, 3], z=[1, 2, 3], u=[1, 2, 3], v=[1, 1, 2], w=[4, 4, 1], showscale=False, hoverinfo="u+v+w+name", )]) fig.update_layout( title="3D Cone Plot", scene=dict( camera=dict( eye=dict(x=-1.57, y=1.36, z=0.58), ), domain=dict(x=[0, 1]), ), ) fig.show()

When I run the code, I get the following output:

Fail.

While it captures that the plot is generated with Python and it contains a 3D plot (out of all things possible), it doesn’t produce the correct plot in that case.

## Putting It Together

I’m very impressed with Google Bard’s capabilities, and I’m sure they will grow significantly over the next couple of months. Google Bard can already match the capabilities of an entry-level data scientist.

Soon you’ll be able to pass screenshots of arbitrary plots into it and let it generate the code for you. This is six-figure-per-year work currently done by advanced data scientists.

When I was still a researcher at University, I’d have done almost everything for such a tool — given that most research papers live and die by the high-quality plots they generate and most researchers and data scientists spend huge amounts of time figuring out how others have generated their stunning-looking plots.

π‘ **Recommended**: Google Bard Extracts Code From Images (PNG, JPEG, WebP)