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import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
import gradio as gr
# Load dataset
data = pd.read_csv('Life-Expectancy-Data-Updated.csv')
# Define the feature columns and target column - 'Country' and 'Region' removed
#feature_cols = [
# 'Infant_deaths', 'Under_five_deaths', 'Adult_mortality', 'Alcohol_consumption',
# 'Hepatitis_B', 'Measles', 'BMI', 'Polio', 'Diphtheria', 'Incidents_HIV', 'GDP_per_capita',
# 'Schooling', 'Economy_status_Developed', 'Economy_status_Developing'
#]
feature_cols = [ # Only 6 selected features used
'Infant_deaths', 'Under_five_deaths', 'Adult_mortality',
'BMI', 'Polio', 'Schooling'
]
target_col = 'Life_expectancy'
X = data[feature_cols]
y = data[target_col]
# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train the model
#rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
# Based on CV tuning
rf_model = RandomForestRegressor(max_depth=10, max_features='sqrt', min_samples_leaf=1, min_samples_split=2, n_estimators=200, random_state=42)
rf_model.fit(X_train, y_train)
#-----------------------------------------------------------------------------------
# Define the feature columns - must match the columns used in the training script
#feature_cols = [
# 'Infant_deaths', 'Under_five_deaths', 'Adult_mortality', 'Alcohol_consumption',
# 'Hepatitis_B', 'Measles', 'BMI', 'Polio', 'Diphtheria', 'Incidents_HIV', 'GDP_per_capita',
# 'Schooling', 'Economy_status_Developed', 'Economy_status_Developing'
#]
feature_cols = [ # Only 6 selected features used
'Infant_deaths', 'Under_five_deaths', 'Adult_mortality',
'BMI', 'Polio', 'Schooling'
]
# Load the trained model
#rf_model = joblib.load('rf_model.pkl')
# Define the prediction function
#def predict_life_expectancy(
# Infant_deaths, Under_five_deaths, Adult_mortality, Alcohol_consumption,
# Hepatitis_B, Measles, BMI, Polio, Diphtheria, Incidents_HIV, GDP_per_capita,
# Schooling, Economy_status
#):
def predict_life_expectancy(
Infant_deaths, Under_five_deaths, Adult_mortality,
BMI, Polio, Schooling
):
# Initialize Economy_status features
#Economy_status_Developed = 0
#Economy_status_Developing = 0
# Set the appropriate status to 1 based on the dropdown selection
#if Economy_status == "Developed":
# Economy_status_Developed = 1
#else: # economy_status == "Developing"
# Economy_status_Developing = 1
# Convert the inputs to a DataFrame
#input_data = pd.DataFrame([[
# Infant_deaths, Under_five_deaths, Adult_mortality, Alcohol_consumption,
# Hepatitis_B, Measles, BMI, Polio, Diphtheria, Incidents_HIV, GDP_per_capita,
# Schooling, Economy_status_Developed, Economy_status_Developing
#]], columns=feature_cols)
input_data = pd.DataFrame([[
Infant_deaths, Under_five_deaths, Adult_mortality,
BMI, Polio, Schooling
]], columns=feature_cols)
# Make the prediction
prediction = rf_model.predict(input_data)[0]
return prediction
# Create Gradio inputs for each feature
inputs = [
#gr.Number(label=col) for col in feature_cols
gr.Slider(0, 1000, value=0, label="Infant_deaths",step=0.1),
gr.Slider(0, 1000, value=0, label="Under_five_deaths",step=0.1),
gr.Slider(0, 1000, value=0, label="Adult_mortality",step=0.1),
#gr.Slider(0, 100, value=0, label="Alcohol_consumption",step=0.1),
#gr.Slider(0, 100, value=99, label="Hepatitis_B", step=0.1),
#gr.Slider(0, 100, value=99, label="Measles", step=0.1),
gr.Slider(0, 50, value=25, label="BMI", step=0.1),
gr.Slider(0, 100, value=99, label="Polio", step=0.1),
#gr.Slider(0, 100, value=99, label="Diphtheria", step=0.1),
#gr.Slider(0, 100, value=0, label="Incidents_HIV", step=0.1),
#gr.Slider(0, 199999, value=0, label="GDP_per_capita", step=1),
gr.Slider(0, 100, value=0, label="Schooling", step=0.1),
#gr.Dropdown(value=0, label="Economy Status", choices=["Developed", "Developing"]) # Dropdown for economy status
]
# Create the Gradio interface
app = gr.Interface(
fn=predict_life_expectancy, # Function to use for predictions
inputs=inputs, # The inputs for the model
outputs=gr.Textbox(label="Life Expectancy Prediction:"), # The output is a single number (life expectancy)
title="Life Expectancy Prediction",
description="Enter values for the features to predict life expectancy.",
theme=gr.themes.Base()
)
# Launch the Gradio app
app.launch() |