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8a95046
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Create app.py

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  1. app.py +49 -0
app.py ADDED
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+ import streamlit as st
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+ import pandas as pd
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+ from sklearn import datasets
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+ from sklearn.ensemble import RandomForestClassifier
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+
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+ st.write("""
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+ # Simple Iris Flower Prediction App
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+
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+ This app predicts the **Iris flower** type!
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+ """)
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+
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+ st.sidebar.header('User Input Parameters')
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+
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+ def user_input_features():
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+ sepal_length = st.sidebar.slider('Sepal length', 4.3, 7.9, 5.4)
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+ sepal_width = st.sidebar.slider('Sepal width', 2.0, 4.4, 3.4)
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+ petal_length = st.sidebar.slider('Petal length', 1.0, 6.9, 1.3)
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+ petal_width = st.sidebar.slider('Petal width', 0.1, 2.5, 0.2)
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+ data = {'sepal_length': sepal_length,
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+ 'sepal_width': sepal_width,
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+ 'petal_length': petal_length,
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+ 'petal_width': petal_width}
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+ features = pd.DataFrame(data, index=[0])
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+ return features
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+
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+ df = user_input_features()
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+
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+ st.subheader('User Input parameters')
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+ st.write(df)
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+
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+ iris = datasets.load_iris()
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+ X = iris.data
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+ Y = iris.target
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+
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+ clf = RandomForestClassifier()
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+ clf.fit(X, Y)
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+
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+ prediction = clf.predict(df)
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+ prediction_proba = clf.predict_proba(df)
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+
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+ st.subheader('Class labels and their corresponding index number')
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+ st.write(iris.target_names)
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+
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+ st.subheader('Prediction')
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+ st.write(iris.target_names[prediction])
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+ #st.write(prediction)
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+
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+ st.subheader('Prediction Probability')
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+ st.write(prediction_proba)