Create app.py

app.py

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+import streamlit as st
+import pandas as pd 
+import seaborn as sns
+import numpy as np
+from sklearn import datasets
+from sklearn.ensemble import RandomForestClassifier
+
+
+iris = datasets.load_iris()
+
+X=iris.data
+y=iris.target
+
+clf= RandomForestClassifier()
+clf.fit(X,y)
+
+
+st.title('Iris Flower Prediction App')
+st.sidebar.header('User input parameters')
+
+def user_input_features():
+    sepal_length = st.sidebar.slider('Sepal length', 4.3,7.9,5.4)
+    sepal_width = st.sidebar.slider('Sepal width', 2.0,4.4,3.4)
+    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.subheader("User Input Parameters")
+st.write(df)
+
+prediction = clf.predict(df)
+prediction_proba = clf.predict_proba(df)
+
+st.subheader('CLass namesand correspondung numbers')
+st.write(iris.target_names)
+
+
+st.header('Prediction')
+st.write(iris.target_names[prediction])
+
+st.header('Prediction Probability')
+st.write(prediction_proba)