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| import streamlit as st | |
| import pandas as pd | |
| from sklearn import datasets | |
| from sklearn.ensemble import RandomForestClassifier | |
| st.title('simple iris flower prediction app') | |
| st.sidebar.header('user input parameters') | |
| iris = datasets.load_iris() | |
| X = iris.data | |
| y = iris.target | |
| clf = RandomForestClassifier() | |
| clf.fit(X,y) | |
| def user_input_features(): | |
| sepal_lenght = st.sidebar.slider('sepal lenght',3.5,8.9,5.4) | |
| # valeur de depart : 3.5 | |
| # valeur de fin : 8.9 | |
| # valeur de positionement initial : 5.4 | |
| sepal_width = st.sidebar.slider('sepal width',2.0,4.4,3.4) | |
| petal_lenght = st.sidebar.slider('petal lenght',1.0,8.9,1.3) | |
| petal_width = st.sidebar.slider('petal width',3.5,8.9,5.4) | |
| data = {'sepal_length':sepal_lenght, | |
| 'sepal_width':sepal_width, | |
| 'petal_lenght':petal_lenght, | |
| 'petal_width':petal_width} | |
| features = pd.DataFrame(data,index = [0]) | |
| return(features) | |
| df = user_input_features() | |
| st.subheader('user input features') | |
| st.write(df) | |
| prediction = clf.predict(df) | |
| prediction_proba = clf.predict_proba(df) | |
| st.subheader('class labels and corresponding indexes') | |
| st.write(iris.target_names) | |
| st.subheader('prediction') | |
| st.write(iris.target_names[prediction]) | |
| st.subheader('prediction probability') | |
| st.write(prediction_proba) |