Add files via upload

streamlit/part4/penguins-app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+from sklearn.ensemble import RandomForestClassifier
+
+st.write("""
+# Penguin Prediction App
+
+This app predicts the **Palmer Penguin** species!
+
+Data obtained from the [palmerpenguins library](https://github.com/allisonhorst/palmerpenguins) in R by Allison Horst.
+""")
+
+st.sidebar.header('User Input Features')
+
+st.sidebar.markdown("""
+[Example CSV input file](https://raw.githubusercontent.com/dataprofessor/data/master/penguins_example.csv)
+""")
+
+# Collects user input features into dataframe
+uploaded_file = st.sidebar.file_uploader("Upload your input CSV file", type=["csv"])
+if uploaded_file is not None:
+    input_df = pd.read_csv(uploaded_file)
+else:
+    def user_input_features():
+        island = st.sidebar.selectbox('Island',('Biscoe','Dream','Torgersen'))
+        sex = st.sidebar.selectbox('Sex',('male','female'))
+        bill_length_mm = st.sidebar.slider('Bill length (mm)', 32.1,59.6,43.9)
+        bill_depth_mm = st.sidebar.slider('Bill depth (mm)', 13.1,21.5,17.2)
+        flipper_length_mm = st.sidebar.slider('Flipper length (mm)', 172.0,231.0,201.0)
+        body_mass_g = st.sidebar.slider('Body mass (g)', 2700.0,6300.0,4207.0)
+        data = {'island': island,
+                'bill_length_mm': bill_length_mm,
+                'bill_depth_mm': bill_depth_mm,
+                'flipper_length_mm': flipper_length_mm,
+                'body_mass_g': body_mass_g,
+                'sex': sex}
+        features = pd.DataFrame(data, index=[0])
+        return features
+    input_df = user_input_features()
+
+# Combines user input features with entire penguins dataset
+# This will be useful for the encoding phase
+penguins_raw = pd.read_csv('penguins_cleaned.csv')
+penguins = penguins_raw.drop(columns=['species'])
+df = pd.concat([input_df,penguins],axis=0)
+
+# Encoding of ordinal features
+# https://www.kaggle.com/pratik1120/penguin-dataset-eda-classification-and-clustering
+encode = ['sex','island']
+for col in encode:
+    dummy = pd.get_dummies(df[col], prefix=col)
+    df = pd.concat([df,dummy], axis=1)
+    del df[col]
+df = df[:1] # Selects only the first row (the user input data)
+
+# Displays the user input features
+st.subheader('User Input features')
+
+if uploaded_file is not None:
+    st.write(df)
+else:
+    st.write('Awaiting CSV file to be uploaded. Currently using example input parameters (shown below).')
+    st.write(df)
+
+# Reads in saved classification model
+load_clf = pickle.load(open('penguins_clf.pkl', 'rb'))
+
+# Apply model to make predictions
+prediction = load_clf.predict(df)
+prediction_proba = load_clf.predict_proba(df)
+
+
+st.subheader('Prediction')
+penguins_species = np.array(['Adelie','Chinstrap','Gentoo'])
+st.write(penguins_species[prediction])
+
+st.subheader('Prediction Probability')
+st.write(prediction_proba)