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import streamlit as st |
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import pandas as pd |
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import numpy as np |
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import pickle |
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from sklearn.preprocessing import LabelEncoder |
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from sklearn.ensemble import RandomForestClassifier |
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import base64 |
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import seaborn as sns |
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st.write(""" |
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# Penguin Prediction App |
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This app predicts the **Palmer Penguin** species! |
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Data obtained from the [palmerpenguins library](https://github.com/allisonhorst/palmerpenguins) in R by Allison Horst. |
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""") |
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st.sidebar.title('File Upload Features') |
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uploaded_file = st.sidebar.file_uploader("Upload your input CSV file", type=["csv"]) |
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if uploaded_file is not None: |
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df = pd.read_csv(uploaded_file) |
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st.dataframe(df) |
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le = LabelEncoder() |
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df.sex = le.fit_transform(df.sex) |
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load_clf = pickle.load(open('penguins_clf.pkl', 'rb')) |
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prediction = load_clf.predict(df) |
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prediction_proba = load_clf.predict_proba(df) |
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st.subheader('Prediction') |
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penguins_species = np.array(['Adelie','Chinstrap','Gentoo']) |
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pp = pd.DataFrame(penguins_species[prediction],columns=["prediction"]) |
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st.write(pp) |
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st.subheader('Prediction Probability') |
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st.dataframe(prediction_proba) |
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ndf = pd.concat([df,pp],axis=1) |
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st.write(ndf) |
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plot = sns.barplot(x ="bill_length_mm",y="bill_depth_mm",data = df ) |
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st.pyplot(plot) |
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def filedownload(df): |
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csv = df.to_csv(index=False) |
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b64 = base64.b64encode(csv.encode()).decode() |
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href = f'<a href="data:file/csv;base64,{b64}" download="penguins_predictions.csv">Download CSV File</a>' |
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return href |
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st.markdown(filedownload(ndf), unsafe_allow_html=True) |
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else: |
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st.sidebar.title("Manual Feature input") |
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def user_input_features(): |
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sex = st.sidebar.selectbox('Sex',('male','female')) |
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bill_length_mm = st.sidebar.slider('Bill length (mm)', 32.1,59.6,43.9) |
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bill_depth_mm = st.sidebar.slider('Bill depth (mm)', 13.1,21.5,17.2) |
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flipper_length_mm = st.sidebar.slider('Flipper length (mm)', 172.0,231.0,201.0) |
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body_mass_g = st.sidebar.slider('Body mass (g)', 2700.0,6300.0,4207.0) |
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data = { |
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'bill_length_mm': bill_length_mm, |
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'bill_depth_mm': bill_depth_mm, |
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'flipper_length_mm': flipper_length_mm, |
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'body_mass_g': body_mass_g, |
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'sex': sex} |
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features = pd.DataFrame(data, index=[0]) |
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return features |
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input_df = user_input_features() |
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st.subheader('User Input features') |
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st.write('Awaiting CSV file to be uploaded. Currently using example input parameters (shown below).') |
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st.write(input_df) |
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le = LabelEncoder() |
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input_df.sex = le.fit_transform(input_df.sex) |
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load_clf = pickle.load(open('penguins_clf.pkl', 'rb')) |
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prediction = load_clf.predict(input_df) |
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prediction_proba = load_clf.predict_proba(input_df) |
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st.subheader('Prediction') |
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penguins_species = np.array(['Adelie','Chinstrap','Gentoo']) |
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st.write(penguins_species[prediction]) |
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st.subheader('Prediction Probability') |
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st.write(prediction_proba) |
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