sketch partial regression chart using dash

i have dataframe, in which we are given x and y columns and between them i want to sketch regression model, main idea is that i should use dash framework, as according chow test , there could be difference between two regression model at different instance value,based on following link : dash models

i wrote following code :

import pandas as pd
from dash import Dash,html,dcc,callback,Output,Input
from sklearn.linear_model import LinearRegression
import plotly.express as px
data =pd.read_csv("regression.csv")
model =LinearRegression()
print(data)
app = Dash()

# Requires Dash 2.17.0 or later
app.layout = [
    html.H1(children='Our regression Model', style={'textAlign':'center'}),
    dcc.Dropdown(data.Year.unique(), '2004', id='dropdown-selection'),
    dcc.Graph(id='graph-content')
]

@callback(
    Output('graph-content', 'figure'),
    Input('dropdown-selection', 'value')
)
def  scatter_graph(value):
    selected =data[data.Year==value]
    return px.scatter(selected,x='x',y='y')
@callback(
    Output('graph-content', 'figure'),
    Input('dropdown-selection', 'value')
)
def  Regression_graph(value):
    selected =data[data.Year==value]
    X =selected['x'].values
    X =X.reshape(-1,1)
    y =selected['y'].values
    model.fit(X,y)
    y_predicted =model.predict(X)
    return px.line(selected,x='x',y=y_predicted)

if __name__ =='__main__':
    app.run(debug=True)

this part works fine :

@callback(
    Output('graph-content', 'figure'),
    Input('dropdown-selection', 'value')
)
def  scatter_graph(value):
    selected =data[data.Year==value]
    return px.scatter(selected,x='x',y='y')

but second decorator for regression plot does not work, here is example :

please help me how to fix it?

It may need to add all code in one function and return both figures

    fig = px.scatter(selected, x=x, y=y)
    fig.add_traces(px.line(selected, x=x, y=y_predicted).data)
    return fig

Full working code with random data.

import pandas as pd
from dash import Dash,html,dcc,callback,Output,Input
from sklearn.linear_model import LinearRegression
import plotly.express as px

from dash.exceptions import PreventUpdate

import random
random.seed(0)   # to get always the same random values
data = pd.DataFrame({
                'x': random.choices(range(0, 100), k=100),
                'y': random.choices(range(0, 100), k=100),
                'Year': ['2025']*50 + ['2024']*50,
})

#data = pd.read_csv("regression.csv")
model = LinearRegression()
print(data)
app = Dash()

# Requires Dash 2.17.0 or later
app.layout = [
    html.H1(children='Our regression Model', style={'textAlign':'center'}),
    dcc.Dropdown(data.Year.unique(), '2004', id='dropdown-selection'),
    dcc.Graph(id='graph-content')
]

@callback(
    Output('graph-content', 'figure'),
    Input('dropdown-selection', 'value')
)
def update_graph(value):
    if value is None:
        raise PreventUpdate

    print(f'{value = }')
    selected = data[data.Year==value]

    x = selected['x'].values
    print(f'{x = }')
    X = x.reshape(-1,1)
    print(f'{X = }')
    y = selected['y'].values
    print(f'{y = }')
    model.fit(X, y)
    y_predicted = model.predict(X)

    # fig = px.line(selected, x=x, y=y_predicted)
    # fig.add_scatter(x=X, y=y)  # doesn't show it

    fig = px.scatter(selected, x=x, y=y)
    fig.add_traces(px.line(selected, x=x, y=y_predicted).data)

    return fig


if __name__ =='__main__':
    app.run(debug=True)