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import gradio as gr
import joblib
import numpy as np
import pandas as pd
from sklearn.datasets import load_breast_cancer
# Load the pipeline
pipeline = joblib.load('knn_pipeline.joblib')
# Load the data to get feature names
data = load_breast_cancer()
feature_names = data.feature_names
def predict_cancer(*input_data):
# Convert input data to a DataFrame with feature names
input_array = np.array([input_data])
input_df = pd.DataFrame(input_array, columns=feature_names)
# Use the pipeline to predict
prediction = pipeline.predict(input_df)
# Map prediction to target names
target_names = ['malignant', 'benign']
return target_names[prediction[0]]
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
col1 = [gr.Slider(label=feature, minimum=0, maximum=10000, step=0.1) for feature in feature_names[:10]]
with gr.Column():
col2 = [gr.Slider(label=feature, minimum=0, maximum=10000, step=0.1) for feature in feature_names[10:20]]
with gr.Column():
col3 = [gr.Slider(label=feature, minimum=0, maximum=10000, step=0.1) for feature in feature_names[20:]]
outputs = gr.Textbox(label="Prediction")
btn = gr.Button("Predict")
btn.click(fn=predict_cancer, inputs=col1 + col2 + col3, outputs=outputs)
demo.launch()
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