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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') |