Generating graphical user interfaces

In this notebook we ask bob to create a user interface that allows interacting with a plot.

import os
from bia_bob import bob
bob.initialize(endpoint="https://llm.scads.ai/v1",
               api_key=os.environ.get('SCADSAI_API_KEY'), 
               model="deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct")

This notebook may contain text, code and images generated by artificial intelligence. Used model: deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct, vision model: None, endpoint: https://llm.scads.ai/v1, bia-bob version: 0.25.3.. Do not enter sensitive or private information and verify generated contents according to good scientific practice. Read more: https://github.com/haesleinhuepf/bia-bob#disclaimer

In this example we use a videogame sales dataset by Gregory Smith which is licensed CC-BY 4.0.

%%bob
write python code that
* opens the file vgsales.csv using pandas
* if the file doesn't exist, open it from https://zenodo.org/records/5898311/files/vgsales.csv?download=1 
  and save it as vgsales.csv

import pandas as pd
import os

try:
    df = pd.read_csv('vgsales.csv')
except FileNotFoundError:
    url = 'https://zenodo.org/records/5898311/files/vgsales.csv?download=1'
    df = pd.read_csv(url)
    df.to_csv('vgsales.csv', index=False)

# Display the first few rows of the DataFrame to verify the operation
print(df.head())

   Rank                      Name Platform    Year         Genre Publisher  \
0     1                Wii Sports      Wii  2006.0        Sports  Nintendo   
1     2         Super Mario Bros.      NES  1985.0      Platform  Nintendo   
2     3            Mario Kart Wii      Wii  2008.0        Racing  Nintendo   
3     4         Wii Sports Resort      Wii  2009.0        Sports  Nintendo   
4     5  Pokemon Red/Pokemon Blue       GB  1996.0  Role-Playing  Nintendo   

   NA_Sales  EU_Sales  JP_Sales  Other_Sales  Global_Sales  
0     41.49     29.02      3.77         8.46         82.74  
1     29.08      3.58      6.81         0.77         40.24  
2     15.85     12.88      3.79         3.31         35.82  
3     15.75     11.01      3.28         2.96         33.00  
4     11.27      8.89     10.22         1.00         31.37  

%%bob
Build a graphical user interface using ipywidgets where the user can select the publisher. 
After the publisher is selected, draw a bar plot showing the NA_Sales, EU_Sales, JP_Sales and Other_Sales.
The bar-plot should be shown right below the pulldown.
Make sure the plot is updated - and not multiple plots are shown.

import pandas as pd
import ipywidgets as widgets
from IPython.display import display
import matplotlib.pyplot as plt
import seaborn as sns

# Sample data creation (replace this with actual data loading)
data = {
    'Publisher': ['Nintendo', 'Sony', 'Nintendo', 'Sony', 'Sony', 'Microsoft', 'Microsoft'],
    'NA_Sales': [10, 5, 8, 3, 6, 12, 9],
    'EU_Sales': [8, 7, 6, 5, 4, 10, 8],
    'JP_Sales': [5, 3, 4, 2, 3, 7, 6],
    'Other_Sales': [2, 1, 3, 1, 2, 4, 5]
}
df = pd.DataFrame(data)

# List of unique publishers
publishers = df['Publisher'].unique()

# Create a dropdown
publisher_dropdown = widgets.Dropdown(options=publishers, description='Select Publisher:')

# Function to plot the sales based on selected publisher
def plot_sales(publisher):
    filtered_df = df[df['Publisher'] == publisher]
    sales_data = filtered_df.drop(columns=['Publisher']).sum().reset_index()
    sales_data.columns = ['Sales Type', 'Sales']
    
    plt.figure(figsize=(10, 5))
    sns.barplot(x='Sales Type', y='Sales', data=sales_data)
    plt.title(f'Sales by Region for {publisher}')
    plt.show()

# Display the dropdown
display(publisher_dropdown)

# Link the dropdown to the plot function
widgets.interact(plot_sales, publisher=publisher_dropdown)

<function __main__.plot_sales(publisher)>