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import gradio as gr
import numpy as np
import joblib
# Load the model
rf_model = joblib.load("rf_model.pkl")
features = {
"CD4": [0, 500, 1],
"AST/ALT": [0, 100, 1],
"ALT": [0, 2000, 1],
"Hb": [1, 150, 1],
"CRP": [1, 500, 1],
"ALB": [10, 50, 1],
"POAL": [0, 1, 1],
"ALC": [0, 5, 1],
"Age (years)": [12, 100, 1],
"WBC": [0, 20, 1],
"PLT": [1, 800, 1],
"AST": [0, 2000, 1],
}
# Define the inference function
def predict(*args):
try:
# Convert input values to numpy array
input_values = [float(arg) for arg in args]
# Reshape to (1, n_features)
input_array = np.array(input_values).reshape(1, -1)
# Use the model for inference
prediction_proba = rf_model.predict_proba(input_array)
prediction = rf_model.predict(input_array)
# Get the confidence of the prediction being class 1 (probability)
confidence = prediction_proba[0][1]
# Return the result
if prediction[0] == 1:
return f"Prediction: 1\nConfidence: {confidence:.2f}"
else:
return f"Prediction: 0\nConfidence: {confidence:.2f}"
except Exception as e:
return f"Inference error: {str(e)}"
# Create Gradio interface
inputs = [
gr.Number(value=v[0], label=k, minimum=v[0], maximum=v[1], step=v[2])
for k, v in features.items()
] # Dynamically generate input components
outputs = gr.Textbox(label="Inference Result") # Output component
# create queue
interface = gr.Interface(
fn=predict, # Inference function
inputs=inputs, # Dynamically generated input components
outputs=outputs, # Output component
title="Random Forest Model Inference", # Interface title
description="Input feature values to get the model's inference result.", # Interface description
live=False, # Whether to update in real-time (set to False, requires button click)
flagging_mode="never",
)
# Enable queue
interface.queue()
# Launch the app
interface.launch(share=True)
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