final project ARIA Deployment

app.py

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+import streamlit as st
+import eda
+import prediction
+
+
+# Set Config dan icon
+st.set_page_config(
+        page_title='Plant Nutrition Prediction',
+        layout='wide',
+        )
+
+# Hide Streamlit Style
+hide_streamlit_style = """
+            <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+            </style>
+            """
+st.markdown(hide_streamlit_style, unsafe_allow_html=True) 
+
+# Membuat navigasi
+st.sidebar.markdown("# Evan Derin Ihsanudin - RMT-FTDS-17")
+navigation = st.sidebar.selectbox('Pilih Halaman (Plant Nutrition Prediction/EDA): ', ('Plant Nutrition Prediction','Exploratory Data Analysis'))
+st.sidebar.image("https://imgur.com/FZTgNj9.png", use_column_width=True)
+
+# Run modul dengan if else
+if navigation == 'Plant Nutrition Prediction' :
+    prediction.run()
+else :
+    eda.run()

eda.py

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+import streamlit as st
+import numpy as np
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import plotly.express as px
+from PIL import Image
+
+
+# Load data dengan pandas dan assign ke variabel df
+df = pd.read_excel('aria_data.xlsx')
+
+def run() :
+    # Membuat Title 
+    st.markdown("<h1 style='text-align: center;'>Exploratory Data Analysis</h1>", unsafe_allow_html=True)
+    st.write('Berikut adalah EDA dari setiap feature')
+
+    # Membuat Sub Header
+    st.subheader('**Distribution Plot**')
+    pilihan = st.selectbox('**Silahkan pilih column :** ',('v1', 'v2', 'v3', 'v4', 'v5', 'v6', 'v7', 'v8', 'target'))
+    fig = plt.figure(figsize=(20,10))
+    sns.histplot(df[pilihan],bins=30,kde=True)
+    title = 'Distribution Plot ' + pilihan
+    plt.title(title, fontsize=20)
+    plt.xlabel(pilihan, fontsize=14)
+    plt.ylabel('Counts', fontsize=14)
+    st.pyplot(fig)
+
+    # Membuat Sub Header
+    st.subheader('**Heatmap Correlation**')
+    st.write('Berikut Heatmap Correlation antar feature')
+    fig = plt.figure(figsize=(15,10))
+    sns.heatmap(df.corr(), annot = True, color = 'blue', cmap = 'YlGn')
+    st.pyplot(fig)
+
+    # Membuat Sub Header
+    st.subheader('**Distribusi Sample Type**')
+    st.write('Berikut visualisasi distribusi sample type dengan barchart dan piechart (persentase)')
+    # Visualisasi
+    fig, ax =plt.subplots(1,2,figsize=(15,6))
+    sns.countplot(x='sample_type', data=df, palette="winter", ax=ax[0])
+    ax[0].set_xlabel("Lab", fontsize= 12)
+    ax[0].set_ylabel("# of Tested Plant", fontsize= 12)
+    fig.suptitle('Count of Tested Plant in each Lab', fontsize=18, fontweight='bold')
+    ax[0].set_ylim(0,110)
+
+    ax[0].set_xticks([0,1], ['Lab 1', 'Lab 2'], fontsize = 11)
+    for p in ax[0].patches:
+        ax[0].annotate("%.0f"%(p.get_height()), (p.get_x() + p.get_width() / 2,
+                        p.get_height()+2), ha='center', va='center',fontsize = 11) 
+    df['sample_type'].value_counts().plot(kind='pie', labels = ['Lab 1','Lab 2'],autopct='%1.1f%%',explode = [0,0.05] ,colors = ['indigo','salmon'],textprops = {"fontsize":12})
+    ax[1].set_ylabel("% of Tested Plant", fontsize= 12)
+    st.pyplot(fig)
+
+    # Membuat Sub Header
+    st.subheader('**Distribusi Sample Type berdasarkan target**')
+    st.write('Dari visualisasi dibawah dapat disimpulkan bahwa :')
+    st.markdown('- Lab 1 sering menguji tanaman dengan tingkat nutrisi 4.6 - 4.85')
+    st.markdown('- Lab 2 sering menguji tanaman dengan tingkat nutrisi 4.5 - 5')
+    # Visualisasi
+    fig = plt.figure(figsize=(15,8))
+    sns.boxenplot(y=df['target'], x= df['sample_type'], palette = 'Blues')
+    plt.title('Sample Type vs Target', fontsize = 15)
+    st.pyplot(fig)
+
+if __name__ == '__main__':
+    run()

model_opt.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f2334774d2ec3527a5f91958f7a6ee29596540751066d07181182ebfeef21b6d
+size 66419

prediction.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+import sklearn
+
+# Load Model
+with open('model_opt.pkl', 'rb') as file_1:
+  model_opt = pickle.load(file_1)
+
+def run() :
+    # Membuat Title 
+    st.markdown("<h1 style='text-align: center;'>Plant Nutrition Prediction</h1>", unsafe_allow_html=True)
+    st.write('Page ini berisi model untuk prediksi nutrisi tanaman dengan 8 variable dan sample_type. Mohon persiapkan data terlebih dahulu sebelum melakukan prediksi')
+
+    # Membuat Form
+    with st.form(key= 'form_plant'):
+        st.markdown('## **Variable**')
+        v1 = st.number_input('**V1**', min_value=227.28, max_value= 678.37, value=295.16 ,step=1.,format="%.2f")
+        v2 = st.number_input('**V2**', min_value=178.80, max_value= 422.81, value=204.18 ,step=1.,format="%.2f")
+        v3 = st.number_input('**V3**', min_value=348.93, max_value= 722.31, value=414.38 ,step=1.,format="%.2f")
+        v4 = st.number_input('**V4**', min_value=313.73, max_value= 558.50, value=370.74 ,step=1.,format="%.2f")
+        v5 = st.number_input('**V5**', min_value=373.33, max_value= 721.00, value=456.03 ,step=1.,format="%.2f")
+        v6 = st.number_input('**V6**', min_value=189.20, max_value= 415.37, value=226.06 ,step=1.,format="%.2f")
+        v7 = st.number_input('**V7**', min_value=586.26, max_value= 853.46, value=718.83 ,step=1.,format="%.2f")
+        v8 = st.number_input('**V8**', min_value=3725.66, max_value= 5086.37, value=4554.76 ,step=1.,format="%.2f")
+        st.markdown('---')
+        sample_type = st.selectbox('Sample Type',('lab 1','lab 2'),index=1)
+        submitted = st.form_submit_button('Predict')
+
+    # Membuat Dataframe
+    data_inf = {
+        'v1' : v1, 
+        'v2' : v2, 
+        'v3' : v3, 
+        'v4' : v4, 
+        'v5' : v5, 
+        'v6' : v6,
+        'v7' : v7, 
+        'v8' : v8,
+        'sample_type' : sample_type
+    }
+    data_inf = pd.DataFrame([data_inf])
+    data_inf
+
+    # Prediksi
+    if submitted :
+        # Predict using Random Forest Parameter Tuning
+        y_pred_inf = model_opt.predict(data_inf)
+        st.write('# **Plant Nutrition Prediction :** ',y_pred_inf[0])
+
+if __name__ == '__main__':
+    run()

requirements.txt

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+streamlit
+pandas
+seaborn
+matplotlib
+scikit-learn == 1.0.2
+numpy
+plotly