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| import joblib | |
| import streamlit as st | |
| # Load the model | |
| model = joblib.load('E:\\health\\flask\\models\\model.pkl') | |
| # Example inputs based on typical feature values from the dataset | |
| examples = [ | |
| [13.54, 14.36, 87.46, 566.3, 0.09779, 0.08129, 0.06664, 0.04781, 0.1885, 0.05766, | |
| 0.2699, 0.7886, 2.058, 23.56, 0.008462, 0.0146, 0.02387, 0.01315, 0.0198, 0.0023, | |
| 15.11, 19.26, 99.7, 711.2, 0.144, 0.1773, 0.239, 0.1288, 0.2977, 0.07259], | |
| [15.78, 18.02, 102.5, 751.4, 0.1053, 0.07893, 0.06981, 0.05683, 0.1769, 0.06503, | |
| 0.3198, 0.8909, 2.192, 28.15, 0.01106, 0.01468, 0.01923, 0.01479, 0.01523, 0.00363, | |
| 18.98, 21.94, 126.9, 1132, 0.1674, 0.1553, 0.2032, 0.1249, 0.2722, 0.09556] | |
| ] | |
| # Set page configuration | |
| st.set_page_config(page_title="Breast Cancer Tumor Classification", | |
| layout="wide", | |
| page_icon="🧑⚕️") | |
| # page title | |
| st.title('Breast Cancer Tumor Classification') | |
| # Select example or manual input | |
| use_example = st.selectbox("Choose an example or input manually:", ["Manual Input"] + [f"Example {i+1}" for i in range(len(examples))]) | |
| # Determine default values | |
| if use_example != "Manual Input": | |
| example_idx = int(use_example.split(" ")[-1]) - 1 | |
| defaults = examples[example_idx] | |
| else: | |
| # Empty defaults for manual input | |
| defaults = ["" for _ in range(30)] | |
| # Define columns and input fields | |
| col1, col2, col3, col4, col5, col6 = st.columns(6) | |
| with col1: | |
| mean_radius = st.text_input('mean radius', value=defaults[0]) | |
| mean_texture = st.text_input('mean texture', value=defaults[1]) | |
| mean_perimeter = st.text_input('mean perimeter', value=defaults[2]) | |
| mean_area = st.text_input('mean area', value=defaults[3]) | |
| mean_smoothness = st.text_input('mean smoothness', value=defaults[4]) | |
| with col2: | |
| mean_compactness = st.text_input('mean compactness', value=defaults[5]) | |
| mean_concavity = st.text_input('mean concavity', value=defaults[6]) | |
| mean_concave_points = st.text_input('mean concave points', value=defaults[7]) | |
| mean_symmetry = st.text_input('mean symmetry', value=defaults[8]) | |
| mean_fractal_dimension = st.text_input('mean fractal dimension', value=defaults[9]) | |
| with col3: | |
| radius_error = st.text_input('radius error', value=defaults[10]) | |
| texture_error = st.text_input('texture error', value=defaults[11]) | |
| perimeter_error = st.text_input('perimeter error', value=defaults[12]) | |
| area_error = st.text_input('area error', value=defaults[13]) | |
| smoothness_error = st.text_input('smoothness error', value=defaults[14]) | |
| with col4: | |
| compactness_error = st.text_input('compactness error', value=defaults[15]) | |
| concavity_error = st.text_input('concavity error', value=defaults[16]) | |
| concave_points_error = st.text_input('concave points error', value=defaults[17]) | |
| symmetry_error = st.text_input('symmetry error', value=defaults[18]) | |
| fractal_dimension_error = st.text_input('fractal dimension error', value=defaults[19]) | |
| with col5: | |
| worst_radius = st.text_input('worst radius', value=defaults[20]) | |
| worst_texture = st.text_input('worst texture', value=defaults[21]) | |
| worst_perimeter = st.text_input('worst perimeter', value=defaults[22]) | |
| worst_area = st.text_input('worst area', value=defaults[23]) | |
| worst_smoothness = st.text_input('worst smoothness', value=defaults[24]) | |
| with col6: | |
| worst_compactness = st.text_input('worst compactness', value=defaults[25]) | |
| worst_concavity = st.text_input('worst concavity', value=defaults[26]) | |
| worst_concave_points = st.text_input('worst concave points', value=defaults[27]) | |
| worst_symmetry = st.text_input('worst symmetry', value=defaults[28]) | |
| worst_fractal_dimension = st.text_input('worst fractal dimension', value=defaults[29]) | |
| # Trigger prediction on button press | |
| if st.button('Breast Cancer Test Result'): | |
| # Collect all user inputs | |
| user_input = [ | |
| mean_radius, mean_texture, mean_perimeter, mean_area, mean_smoothness, | |
| mean_compactness, mean_concavity, mean_concave_points, mean_symmetry, | |
| mean_fractal_dimension, radius_error, texture_error, perimeter_error, | |
| area_error, smoothness_error, compactness_error, concavity_error, | |
| concave_points_error, symmetry_error, fractal_dimension_error, | |
| worst_radius, worst_texture, worst_perimeter, worst_area, | |
| worst_smoothness, worst_compactness, worst_concavity, | |
| worst_concave_points, worst_symmetry, worst_fractal_dimension | |
| ] | |
| # Convert inputs to floats | |
| user_input = [float(x) for x in user_input] | |
| # Make prediction | |
| Prediction = model.predict([user_input]) | |
| # Interpret prediction | |
| Prediction = 'Benign' if Prediction[0] == 1 else 'Malignant' | |
| st.write(f"The breast cancer test result is: **{Prediction}**") | |