Upload 2 files

app.py

@@ -0,0 +1,144 @@
+import pickle
+
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+import streamlit as st
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+
+st.title("Penguin Classifier")
+
+st.write(
+    """App uses 6 inputs to predict
+     the species of penguin using a model
+     built on the Palmer's Penguins dataset.
+    """
+)
+
+# password 설정
+password_guess = st.text_input("Input Password?")
+if password_guess != "streamlit":
+    st.stop()
+
+# updata csvfile
+penguin_file = st.file_uploader("Upload your own data")
+
+if penguin_file is None:
+    rf_pickle = open("models/rf_penguin.pickle", "rb")
+    map_pickle = open("models/class_penguin.pickle", "rb")
+    rfc = pickle.load(rf_pickle)
+    unique_penguin_mapping = pickle.load(map_pickle)
+    rf_pickle.close()
+    map_pickle.close()
+    penguin_df = pd.read_csv("penguins.csv")
+else:
+    penguin_df = pd.read_csv(penguin_file)
+    penguin_df['sex'].fillna(penguin_df['sex'].mode()[0], inplace=True)
+    penguin_df = penguin_df.dropna()
+    output = penguin_df["species"]
+    features = penguin_df[
+        [
+            "island",
+            "bill_length_mm",
+            "bill_depth_mm",
+            "flipper_length_mm",
+            "body_mass_g",
+            "sex",
+        ]
+    ]
+    features = pd.get_dummies(features)
+    output, unique_penguin_mapping = pd.factorize(output)
+    x_train, x_test, y_train, y_test = train_test_split(features, output, test_size=0.8)
+    rfc = RandomForestClassifier(random_state=15)
+    rfc.fit(x_train.values, y_train)
+    y_pred = rfc.predict(x_test.values)
+    score = round(accuracy_score(y_pred, y_test), 2)
+    
+    rf_pickle = open("models/rf_penguin.pickle", "wb")
+    pickle.dump(rfc, rf_pickle)
+    rf_pickle.close()
+
+    output_pickle = open("models/class_penguin.pickle", "wb")
+    pickle.dump(unique_penguin_mapping, output_pickle)
+    output_pickle.close()
+    st.write(
+        f"""Trained a Random Forest model on these data,
+        it has a score of {score}! """
+    )
+
+# selectbox, button 만들기    
+with st.form("user_inputs"):
+    island = st.selectbox("Penguin Island", options=["Biscoe", "Dream", "Torgerson"])
+    sex = st.selectbox("Sex", options=["Female", "Male"])
+    bill_length = st.number_input("Bill Length (mm)", min_value=0)
+    bill_depth = st.number_input("Bill Depth (mm)", min_value=0)
+    flipper_length = st.number_input("Flipper Length (mm)", min_value=0)
+    body_mass = st.number_input("Body Mass (g)", min_value=0)
+    st.form_submit_button()
+
+island_biscoe, island_dream, island_torgerson = 0, 0, 0
+if island == "Biscoe":
+    island_biscoe = 1
+elif island == "Dream":
+    island_dream = 1
+elif island == "Torgerson":
+    island_torgerson = 1
+
+sex_female, sex_male = 0, 0
+if sex == "Female":
+    sex_female = 1
+elif sex == "Male":
+    sex_male = 1
+
+# Predction    
+new_prediction = rfc.predict(
+    [
+        [
+            bill_length,
+            bill_depth,
+            flipper_length,
+            body_mass,
+            island_biscoe,
+            island_dream,
+            island_torgerson,
+            sex_female,
+            sex_male,
+        ]
+    ]
+)
+st.subheader("Predicting Your Penguin's Species:")
+prediction_species = unique_penguin_mapping[new_prediction][0]
+st.write(f"# Prediction Species: **{prediction_species}")
+st.write(
+    """Machine learning
+    (Random Forest) model to predict the
+    species, the features used in this
+    prediction are ranked by relative
+    importance below."""
+)
+
+st.write(
+    """Below are the histograms for each
+continuous variable separated by penguin species.
+The vertical line represents the inputted value."""
+)
+
+fig, ax = plt.subplots()
+ax = sns.displot(x=penguin_df["bill_length_mm"], hue=penguin_df["species"])
+plt.axvline(bill_length)
+plt.title("Bill Length by Species")
+st.pyplot(ax)
+
+fig, ax = plt.subplots()
+ax = sns.displot(x=penguin_df["bill_depth_mm"], hue=penguin_df["species"])
+plt.axvline(bill_depth)
+plt.title("Bill Depth by Species")
+st.pyplot(ax)
+
+fig, ax = plt.subplots()
+ax = sns.displot(x=penguin_df["flipper_length_mm"], hue=penguin_df["species"])
+plt.axvline(flipper_length)
+plt.title("Flipper Length by Species")
+st.pyplot(ax)

penguins.csv

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+species,island,bill_length_mm,bill_depth_mm,flipper_length_mm,body_mass_g,sex,year
+Adelie,Torgersen,39.1,18.7,181.0,3750.0,male,2007
+Adelie,Torgersen,39.5,17.4,186.0,3800.0,female,2007
+Adelie,Torgersen,40.3,18.0,195.0,3250.0,female,2007
+Adelie,Torgersen,,,,,,2007
+Adelie,Torgersen,36.7,19.3,193.0,3450.0,female,2007
+Adelie,Torgersen,39.3,20.6,190.0,3650.0,male,2007
+Adelie,Torgersen,38.9,17.8,181.0,3625.0,female,2007
+Adelie,Torgersen,39.2,19.6,195.0,4675.0,male,2007
+Adelie,Torgersen,34.1,18.1,193.0,3475.0,,2007
+Adelie,Torgersen,42.0,20.2,190.0,4250.0,,2007
+Adelie,Torgersen,37.8,17.1,186.0,3300.0,,2007
+Adelie,Torgersen,37.8,17.3,180.0,3700.0,,2007
+Adelie,Torgersen,41.1,17.6,182.0,3200.0,female,2007
+Adelie,Torgersen,38.6,21.2,191.0,3800.0,male,2007
+Adelie,Torgersen,34.6,21.1,198.0,4400.0,male,2007
+Adelie,Torgersen,36.6,17.8,185.0,3700.0,female,2007
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+Adelie,Torgersen,42.5,20.7,197.0,4500.0,male,2007
+Adelie,Torgersen,34.4,18.4,184.0,3325.0,female,2007
+Adelie,Torgersen,46.0,21.5,194.0,4200.0,male,2007
+Adelie,Biscoe,37.8,18.3,174.0,3400.0,female,2007
+Adelie,Biscoe,37.7,18.7,180.0,3600.0,male,2007
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+Adelie,Biscoe,40.6,18.6,183.0,3550.0,male,2007
+Adelie,Biscoe,40.5,17.9,187.0,3200.0,female,2007
+Adelie,Biscoe,37.9,18.6,172.0,3150.0,female,2007
+Adelie,Biscoe,40.5,18.9,180.0,3950.0,male,2007
+Adelie,Dream,39.5,16.7,178.0,3250.0,female,2007
+Adelie,Dream,37.2,18.1,178.0,3900.0,male,2007
+Adelie,Dream,39.5,17.8,188.0,3300.0,female,2007
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+Adelie,Dream,40.8,18.4,195.0,3900.0,male,2007
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+Adelie,Dream,41.1,19.0,182.0,3425.0,male,2007
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