Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+from sklearn.preprocessing import LabelEncoder
+from sklearn.ensemble import RandomForestClassifier
+import base64
+import seaborn as sns
+
+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.title('File Upload Features')
+
+# 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:
+    df = pd.read_csv(uploaded_file)
+    st.dataframe(df)
+    le = LabelEncoder()
+    df.sex = le.fit_transform(df.sex)
+    load_clf = pickle.load(open('penguins_clf.pkl', 'rb'))
+    prediction = load_clf.predict(df)
+    prediction_proba = load_clf.predict_proba(df)
+    st.subheader('Prediction')
+    penguins_species = np.array(['Adelie','Chinstrap','Gentoo'])
+    pp = pd.DataFrame(penguins_species[prediction],columns=["prediction"])
+    st.write(pp)
+    st.subheader('Prediction Probability')
+    st.dataframe(prediction_proba)
+    ndf = pd.concat([df,pp],axis=1)
+    st.write(ndf)
+    plot = sns.barplot(x ="bill_length_mm",y="bill_depth_mm",data = df )
+    st.pyplot(plot)
+
+    def filedownload(df):
+        csv = df.to_csv(index=False)
+        b64 = base64.b64encode(csv.encode()).decode()  # strings <-> bytes conversions
+        href = f'<a href="data:file/csv;base64,{b64}" download="penguins_predictions.csv">Download CSV File</a>'
+        return href
+
+    st.markdown(filedownload(ndf), unsafe_allow_html=True)
+
+else:
+    st.sidebar.title("Manual Feature input")
+    def user_input_features():
+        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 = {
+                '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()
+    st.subheader('User Input features')
+    st.write('Awaiting CSV file to be uploaded. Currently using example input parameters (shown below).')
+    st.write(input_df)
+    le = LabelEncoder()
+    input_df.sex = le.fit_transform(input_df.sex)
+    load_clf = pickle.load(open('penguins_clf.pkl', 'rb'))
+    prediction = load_clf.predict(input_df)
+    prediction_proba = load_clf.predict_proba(input_df)
+    st.subheader('Prediction')
+    penguins_species = np.array(['Adelie','Chinstrap','Gentoo'])
+    st.write(penguins_species[prediction])
+    st.subheader('Prediction Probability')
+    st.write(prediction_proba)
+