Create app.py

app.py

@@ -0,0 +1,46 @@
+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)