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import shap |
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import streamlit as st |
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import streamlit.components.v1 as components |
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import numpy as np |
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import pickle |
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import pandas as pd |
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import time |
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def impute_missing_values(df_,dict_impute): |
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return df_.fillna(dict_impute) |
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to_rescale_ = ['diff_MS','MS_d','pr_pre_tavi', |
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'surface_systole','MS_s','age', |
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'ncc_calcif_n','calc_risque_n'] |
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to_encode_ = ['syncope_Oui', 'lcc_calc_1.0', 'bloc_branche_pre_bbd'] |
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st.title("Prédiction du risque d'IPM post TAVI, Pre Opération") |
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cols = ['diff_MS','MS_d','pr_pre_tavi', |
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'surface_systole','MS_s','age', |
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'ncc_calcif_n','calc_risque_n', |
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'syncope_Oui', 'lcc_calc_1.0', 'bloc_branche_pre_bbd'] |
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def st_shap(plot, height=None): |
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shap_html = f"<head>{shap.getjs()}</head><body>{plot.html()}</body>" |
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components.html(shap_html, height=height) |
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with st.form(key='cols_form'): |
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c1,c2,c3 = st.columns(3) |
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c4,c5,c6 = st.columns(3) |
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c7,c8,c9 = st.columns(3) |
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c10,c11 = st.columns(2) |
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with c1: |
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diff_MS = st.number_input('Différence SM (mm)',value=1.218) |
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with c2: |
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MS_d = st.number_input('Mesure diastolique SM (mm)',value=5.3) |
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with c3: |
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pr_pre_tavi = st.number_input('PR Pre TAVI (ms)',value=240) |
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with c4: |
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surface_systole = st.number_input("Surface systolique de l'anneau (mm²) ",value=518) |
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with c5: |
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MS_s = st.number_input('Mesure systolique SM (mm)',value=4.082) |
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with c6: |
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age = st.number_input('Age (année)',value=85.775) |
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with c7: |
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ncc_calcif_n = st.number_input('Degré calcification CNC',value=2) |
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with c8: |
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calc_risque_n = st.number_input('Degré calcificaton zone à risque',value=4) |
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with c9: |
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syncope_Oui = st.number_input('Syncope',value=0) |
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with c10: |
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lcc_calc_1 = st.number_input('Calcification CCG',value=1) |
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with c11: |
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bloc_branche_pre_bbd = st.number_input('Présence bloc de branche droit pre tavi ',value=0) |
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submitButton = st.form_submit_button(label = 'Predict') |
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@st.cache_resource() |
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def load_model(): |
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with open(r"svm_pre.pkl", "rb") as input_file: |
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model = pickle.load(input_file) |
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return model |
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def load_scaler(): |
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with open(r"scaler_pre.pkl", "rb") as input_file: |
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scaler = pickle.load(input_file) |
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return scaler |
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@st.cache_resource() |
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def load_impute(): |
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with open(r"dict_impute_pre.pkl", "rb") as input_file: |
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dict_impute = pickle.load(input_file) |
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return dict_impute |
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with st.spinner("Loading Model...."): |
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model = load_model() |
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dict_impute = load_impute() |
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scaler = load_scaler() |
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with st.spinner("Prediction..."): |
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age = age*365 |
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pred_arr = np.array([[diff_MS,MS_d,pr_pre_tavi, |
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surface_systole,MS_s,age, |
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ncc_calcif_n,calc_risque_n, |
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syncope_Oui, lcc_calc_1, bloc_branche_pre_bbd]]) |
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pred_df = pd.DataFrame(pred_arr,columns=cols) |
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df_scaled_ = scaler.transform(pred_df[to_rescale_]) |
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df_scaled = pd.DataFrame(columns=to_rescale_,data=df_scaled_) |
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pred_df = pd.concat([df_scaled,pred_df[to_encode_]],axis=1) |
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pred=round(model.predict_proba(pred_df)[0][1]*100,2) |
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print(model.feature_names_in_) |
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print(pred_df.columns) |
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print(pred_df.iloc[0]) |
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print(pred) |
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st.write("Probabilité de risque d'IPM :",pred,' %') |
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