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import gradio as gr
import pandas as pd
import joblib
# Load the trained model
model_path = "trained_model.pkl"
rf_model = joblib.load(model_path)
# Define feature ranges and labels based on data
numerical_features = ['BMI', 'WeightInKilograms', 'HeightInMeters', 'PhysicalHealthDays', 'SleepHours']
categorical_features = [
'HadAngina_Yes', 'HadHeartAttack_Yes', 'ChestScan_Yes',
'HadStroke_Yes', 'DifficultyWalking_Yes', 'HadDiabetes_Yes',
'PneumoVaxEver_Yes', 'HadArthritis_Yes'
]
# Define sliders for numerical features
sliders = {
"BMI": (0, 50, 1),
"WeightInKilograms": (30, 200, 1),
"HeightInMeters": (1.0, 2.5, 0.01),
"PhysicalHealthDays": (0, 30, 1),
"SleepHours": (0, 24, 1)
}
# Define radio buttons for categorical features
radio_options = ['Yes', 'No']
# Prediction function
def predict_outcome(*inputs):
input_data = dict(zip(numerical_features + categorical_features, inputs))
# Convert categorical inputs to numerical
for feature in categorical_features:
input_data[feature] = 1 if input_data[feature] == "Yes" else 0
# Create input DataFrame
input_df = pd.DataFrame([input_data])
# Predict using the model
prediction = rf_model.predict(input_df)[0]
prediction_label = "High Risk" if prediction == 1 else "Low Risk"
# Display input values for debugging
return prediction_label, input_data
# Build Gradio interface
inputs = [
gr.Slider(sliders[feature][0], sliders[feature][1], sliders[feature][2], label=feature)
for feature in numerical_features
] + [
gr.Radio(radio_options, label=feature) for feature in categorical_features
]
outputs = [
gr.Textbox(label="Prediction"),
gr.JSON(label="Input Values (Debugging)")
]
interface = gr.Interface(
fn=predict_outcome,
inputs=inputs,
outputs=outputs,
title="Health Risk Prediction with Debugging",
description="Predicts health risks based on input parameters using the trained model. Includes input values for debugging."
)
# Launch the app
if __name__ == "__main__":
interface.launch() |