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def prepare_country_stats(oecd_bli, gdp_per_capita):
oecd_bli = oecd_bli[oecd_bli["INEQUALITY"]=="TOT"]
oecd_bli = oecd_bli.pivot(index="Country", columns="Indicator", values="Value")
gdp_per_capita.rename(columns={"2015": "GDP per capita"}, inplace=True)
gdp_per_capita.set_index("Country", inplace=True)
full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita,
left_index=True, right_index=True)
full_country_stats.sort_values(by="GDP per capita", inplace=True)
remove_indices = [0, 1, 6, 8, 33, 34, 35]
keep_indices = list(set(range(36)) - set(remove_indices))
return full_country_stats[["GDP per capita", 'Life satisfaction']].iloc[keep_indices]
import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.linear_model
import sklearn.neighbors
# define the processing function
def my_function(input):
# replace with your own function code
# Load the data
oecd_bli = pd.read_csv("oecd.csv", thousands=',')
gdp_per_capita = pd.read_csv("gdp.csv",thousands=',',delimiter='\t',
encoding='latin1', na_values="n/a")
# Prepare the data
country_stats = prepare_country_stats(oecd_bli, gdp_per_capita)
X = np.c_[country_stats["GDP per capita"]]
y = np.c_[country_stats["Life satisfaction"]]
# Visualize the data
country_stats.plot(kind='scatter', x="GDP per capita", y='Life satisfaction')
plt.show()
# Select a linear model
model = sklearn.neighbors.KNeighborsRegressor(n_neighbors=3)
# Train the model
model.fit(X, y)
# Make a prediction for Cyprus
X_new = [[35710]] # Cyprus' GDP per capita
#print(model.predict(X_new))
#reseting country index
country_stats = country_stats.reset_index()
k = int(input)
model = sklearn.neighbors.KNeighborsRegressor(n_neighbors=k)
# Train the model
model.fit(X, y)
X_new = [[22587]]
r=model.predict(X_new)
v=float(r)
value = round(v,1)
for i in range(0,29):
if country_stats['Life satisfaction'][i]==value:
b =country_stats['Country'][i]
output = str(b)
return output
# create a Gradio interface
inputs = gr.inputs.Textbox(label="Enter the value of k ")
outputs = gr.outputs.Textbox(label="Output")
interface = gr.Interface(fn=my_function, inputs=inputs, outputs=outputs, title="My Gradio Interface")
# launch the interface
interface.launch()
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