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d2cafe4 0962feb f1c72a5 672d7d2 d2cafe4 672d7d2 d2cafe4 672d7d2 d2cafe4 672d7d2 d2cafe4 672d7d2 d2cafe4 672d7d2 d2cafe4 672d7d2 d2cafe4 672d7d2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | import gradio as gr
import pandas as pd
import pickle
import numpy as np
import pandas as pd
# Load the model
with open("model.pkl", "rb") as model_file:
model = pickle.load(model_file)
# Define the prediction function
def predict_diabetes_risk(pregnancies, glucose, blood_pressure, insulin, bmi, age):
"""
Predict diabetes risk based on user inputs.
"""
# Prepare input data
input_data = np.array([[pregnancies, glucose, blood_pressure, insulin, bmi, age]])
prediction = model.predict(input_data) # Assuming the model has a `predict` method
probability = model.predict_proba(input_data)[0][1] # If available, get prediction probability
risk_level = "High Risk" if prediction[0] == 1 else "Low Risk"
return f"Risk Level: {risk_level} (Probability: {probability:.2%})"
# Set up the Gradio interface
with gr.Blocks() as demo:
gr.Markdown("## Diabetes Risk Predictor")
gr.Markdown("Enter the following details to predict your risk of diabetes.")
with gr.Row():
pregnancies = gr.Number(label="Pregnancies", value=0)
glucose = gr.Number(label="Glucose Level", value=0)
blood_pressure = gr.Number(label="Blood Pressure Level", value=0)
insulin = gr.Number(label="Insulin Level", value=0)
bmi = gr.Number(label="BMI (Body Mass Index)", value=0)
age = gr.Number(label="Age", value=0)
predict_button = gr.Button("Predict Risk")
output = gr.Textbox(label="Prediction Result")
predict_button.click(
predict_diabetes_risk,
inputs=[pregnancies, glucose, blood_pressure, insulin, bmi, age],
outputs=output,
)
# Launch the app
if __name__ == "__main__":
demo.launch()
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