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| import pickle | |
| import streamlit as st | |
| import numpy as np | |
| import pandas as pd | |
| from sklearn.preprocessing import StandardScaler | |
| lr_model = "models/LR_model.pkl" | |
| knn_model = "models/KNN_model.pkl" | |
| svm_model = "models/SVM_model.pkl" | |
| abc_model = "models/ABC_model.pkl" | |
| bc_model = "models/BC_model.pkl" | |
| dt_model = "models/DT_model.pkl" | |
| lgbm_model = "models/LGBM_model.pkl" | |
| nb_model = "models/NB_model.pkl" | |
| rf_model = "models/RF_model.pkl" | |
| xg_model = "models/XG_model.pkl" | |
| with open(lr_model, 'rb') as file: | |
| LR_model = pickle.load(file) | |
| with open(knn_model, 'rb') as file: | |
| KNN_model = pickle.load(file) | |
| with open(svm_model, 'rb') as file: | |
| SVM_model = pickle.load(file) | |
| with open(abc_model, 'rb') as file: | |
| ABC_model = pickle.load(file) | |
| with open(bc_model, 'rb') as file: | |
| BC_model = pickle.load(file) | |
| with open(dt_model, 'rb') as file: | |
| DT_model = pickle.load(file) | |
| with open(lgbm_model, 'rb') as file: | |
| LGBM_model = pickle.load(file) | |
| with open(nb_model, 'rb') as file: | |
| NB_model = pickle.load(file) | |
| with open(rf_model, 'rb') as file: | |
| RF_model = pickle.load(file) | |
| with open(xg_model, 'rb') as file: | |
| XG_model = pickle.load(file) | |
| st.title("Parkinson's Disease Prediction Using Machine Learning") | |
| st.header('Fill the form and press the predict button to see the result', | |
| divider='rainbow') | |
| # test_value = [[ 5.29249395e-01, -1.03309592e-01, 1.11583374e+00, | |
| # -5.23716022e-01, -6.82179904e-01, -4.29193964e-01, | |
| # -4.66158519e-01, -4.28206106e-01, -6.17874540e-01, | |
| # -6.05206208e-01, -6.44461440e-01, -5.48107644e-01, | |
| # -5.58034900e-01, -6.44471776e-01, -5.39840575e-01, | |
| # 7.18861885e-01, -1.49332475e+00, 1.18499869e+00, | |
| # -3.23304568e-01, -3.76742214e-01, 3.78931110e-01, | |
| # -3.93143882e-01]] | |
| # Initial values | |
| initial_values = { | |
| 'MDVP:Fo(Hz)': 119.99200, | |
| 'MDVP:Fhi(Hz)': 157.30200, | |
| 'MDVP:Flo(Hz)': 74.99700, | |
| 'MDVP:Jitter(%)': 0.00784, | |
| 'MDVP:Jitter(Abs)': 0.00007, | |
| 'MDVP:RAP': 0.00370, | |
| 'MDVP:PPQ': 0.00554, | |
| 'Jitter:DDP': 0.01109, | |
| 'MDVP:Shimmer': 0.04374, | |
| 'MDVP:Shimmer(dB)': 0.42600, | |
| 'Shimmer:APQ3': 0.02182, | |
| 'Shimmer:APQ5': 0.03130, | |
| 'MDVP:APQ': 0.02971, | |
| 'Shimmer:DDA': 0.06545, | |
| 'NHR': 0.02211, | |
| 'HNR': 21.03300, | |
| 'RPDE': 0.414783, | |
| 'DFA': 0.815285, | |
| 'spread1': -4.813031, | |
| 'spread2': 0.266482, | |
| 'D2': 2.301442, | |
| 'PPE': 0.284654 | |
| } | |
| for i, (key, value) in enumerate(initial_values.items()): | |
| initial_values[key] = st.text_input(key, value=value, key=i) | |
| # print(list(enumerate(initial_values.items()))) | |
| if st.button('Predict', type="primary"): | |
| # Get the values from the text input fields | |
| # values = [float(st.text_input(key, value=value)) for key, value in initial_values.items()] | |
| values = [float(value) for value in initial_values.values()] | |
| # Convert the list of values to a numpy array and store it in 'test_value' | |
| test_value = np.array(values) | |
| sc = StandardScaler() | |
| # Print 'test_value' to the console | |
| st.write("Logistic Regression", str( | |
| LR_model.predict(sc.fit_transform([test_value])))) | |
| st.write("KNN", str(KNN_model.predict(sc.fit_transform([test_value])))) | |
| st.write("SVM", str(SVM_model.predict(sc.fit_transform([test_value])))) | |
| st.write("AdaBoost Classifier", str( | |
| ABC_model.predict(sc.fit_transform([test_value])))) | |
| st.write("Bagging Classifier", str( | |
| BC_model.predict(sc.fit_transform([test_value])))) | |
| st.write("Decision Tree", str( | |
| DT_model.predict(sc.fit_transform([test_value])))) | |
| st.write("LightGBM", str(LGBM_model.predict( | |
| sc.fit_transform([test_value])))) | |
| st.write("Naive Bayes", str( | |
| NB_model.predict(sc.fit_transform([test_value])))) | |
| st.write("Random Forest", str( | |
| RF_model.predict(sc.fit_transform([test_value])))) | |
| st.write("XGBoost", str(XG_model.predict(sc.fit_transform([test_value])))) | |
| st.write("Provided Data: ", test_value) | |
| dataset = pd.read_csv("dataset/parkinsons.data") | |
| st.subheader('Dataset Sample:', divider='rainbow') | |
| st.write(dataset.head()) | |