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CancerML
File size: 2,992 Bytes
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import streamlit as st
import numpy as np
import pickle
from sklearn.preprocessing import StandardScaler

model = pickle.load(open("model-2.pkl","rb"))

def StandardScalerInput(user_input):
    scaler = StandardScaler()
    scaled_input = scaler.fit_transform(np.array(user_input).reshape(1,-1))
    return scaled_input

st.title("CANCER DETECTION APPLICATION")

radius_mean = st.number_input('radius_mean', value=0.0)
texture_mean = st.number_input('texture_mean', value=0.0)
perimeter_mean = st.number_input('perimeter_mean', value=0.0)
area_mean = st.number_input('area_mean', value=0.0)
smoothness_mean = st.number_input('smoothness_mean', value=0.0)
compactness_mean = st.number_input('compactness_mean', value=0.0)
concavity_mean = st.number_input('concavity_mean', value=0.0)
concave_points_mean = st.number_input('concave points_mean', value=0.0)
symmetry_mean = st.number_input('symmetry_mean', value=0.0)
fractal_dimension_mean = st.number_input('fractal_dimension_mean', value=0.0)
radius_se = st.number_input('radius_se', value=0.0)
texture_se = st.number_input('texture_se', value=0.0)
perimeter_se = st.number_input('perimeter_se', value=0.0)
area_se = st.number_input('area_se', value=0.0)
smoothness_se = st.number_input('smoothness_se', value=0.0)
compactness_se = st.number_input('compactness_se', value=0.0)
concavity_se = st.number_input('concavity_se', value=0.0)
concave_points_se = st.number_input('concave points_se', value=0.0)
symmetry_se = st.number_input('symmetry_se', value=0.0)
fractal_dimension_se = st.number_input('fractal_dimension_se', value=0.0)
radius_worst = st.number_input('radius_worst', value=0.0)
texture_worst = st.number_input('texture_worst', value=0.0)
perimeter_worst = st.number_input('perimeter_worst', value=0.0)
area_worst = st.number_input('area_worst', value=0.0)
smoothness_worst = st.number_input('smoothness_worst', value=0.0)
compactness_worst = st.number_input('compactness_worst', value=0.0)
concavity_worst = st.number_input('concavity_worst', value=0.0)
concave_points_worst = st.number_input('concave points_worst', value=0.0)
symmetry_worst = st.number_input('symmetry_worst', value=0.0)
fractal_dimension_worst = st.number_input('fractal_dimension_worst', value=0.0)

user_input = [
    radius_mean, texture_mean, perimeter_mean, area_mean, smoothness_mean,
    compactness_mean, concavity_mean, concave_points_mean, symmetry_mean,
    fractal_dimension_mean, radius_se, texture_se, perimeter_se, area_se,
    smoothness_se, compactness_se, concavity_se, concave_points_se, symmetry_se,
    fractal_dimension_se, radius_worst, texture_worst, perimeter_worst, area_worst,
    smoothness_worst, compactness_worst, concavity_worst, concave_points_worst,
    symmetry_worst, fractal_dimension_worst
]

if st.button("PREDICT"):
    standardized_input = StandardScalerInput(user_input)
    prediction = model.predict(standardized_input)

    st.write("PREDICTION: ", 'CANCER DETECED' if prediction[0]=='M' else 'No Cancer Detected')