Create app.py

app.py

@@ -0,0 +1,41 @@
+import streamlit as st 
+import pandas as pd 
+from sklearn import datasets
+from sklearn.ensemble import RandomForestClassifier
+
+st.title("""Iris App Classifier""")
+st.sidebar.header('User input parameters')
+
+def user_input_features():
+    sepal_length = st.sidebar.slider('Sepal length',4.3,7.8,5.0)
+    sepal_width = st.sidebar.slider('Sepal width',2.0,4.8,3.0)
+    petal_length = st.sidebar.slider('petal length',1.0,6.9,1.3)
+    petal_width = st.sidebar.slider('petal width',0.1,2.5,0.2)
+    
+    data = {'sepal_length':sepal_length,'sepal_width':sepal_width,
+            'petal_length':petal_length,'petal_width':petal_width}
+    
+    features = pd.DataFrame(data,index=[0])
+    return features
+
+df = user_input_features()
+st.write(df)
+
+iris = datasets.load_iris()
+X=iris.data
+y=iris.target
+
+clf = RandomForestClassifier()
+clf.fit(X,y)
+
+prediction = clf.predict(df)
+prediction_proba = clf.predict_proba(df)
+
+st.subheader('Class labels')
+st.write(iris.target_names)
+
+st.subheader('Prediction')
+st.write(iris.target_names[prediction])
+
+st.subheader('Prediction_Proba')
+st.write(prediction_proba)