Create app.py

app.py

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+import streamlit as st # type: ignore
+import pandas as pd
+from sklearn import datasets
+from sklearn.ensemble import RandomForestClassifier
+
+st.title('Iris Flower Prediction App')
+iris = datasets.load_iris()
+X = iris.data
+y = iris.target
+
+clf = RandomForestClassifier()
+clf.fit(X,y)
+
+st.sidebar.header('User Input Parameters')
+
+def user_input_features():
+    sepal_length = st.sidebar.slider('Sepal length' ,4.3,8.0,5.0)
+    sepal_width = st.sidebar.slider('Sepal width' ,2.0,4.4,3.4)
+    pepal_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':pepal_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 names and corresponding numbers')
+st.write(iris.target_names)
+
+st.subheader('Prediction')
+st.write(iris.target_names[prediction])
+
+st.subheader('Prediction Probability')
+st.write(prediction_proba )