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#!/usr/bin/env python
# coding: utf-8
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#!/usr/bin/env python
# coding: utf-8
# In[6]:
# app.py
import gradio as gr
import joblib
import json
import numpy as np
# Load the model and scaler
model = joblib.load('xgboost_breast_cancer_model.joblib')
scaler = joblib.load('scaler.joblib')
# Load feature names
with open('feature_names.json', 'r') as f:
feature_names = json.load(f)
def predict_cancer(*features):
# Convert inputs to numpy array
input_data = np.array(features).reshape(1, -1)
# Scale the input data
scaled_input = scaler.transform(input_data)
# Make prediction
prediction_proba = model.predict_proba(scaled_input)[0, 1]
# Apply threshold
prediction = "Malignant" if prediction_proba >= 0.4 else "Benign"
return f"Prediction: {prediction}\nProbability of being malignant: {prediction_proba:.2f}"
# Create Gradio interface
iface = gr.Interface(
fn=predict_cancer,
inputs=[gr.Number(label=name) for name in feature_names],
outputs="text",
title="Breast Cancer Prediction",
description="Enter the feature values to predict whether a breast mass is benign or malignant."
)
iface.launch()
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