Deployment final project

app.py

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+import streamlit as st
+import prediction
+
+prediction.run()

mod_train.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6c39fe538b89d73af8b564dbd868bd81b562f127445d3b2bcdd1b8ed01cbfa6d
+size 140087

prediction.py

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+import streamlit as st
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from PIL import Image
+import pickle
+
+
+st.set_page_config(
+    page_title = 'Forecasting',
+    layout = 'wide',
+    initial_sidebar_state='expanded'
+)
+
+def run():
+
+    # Membuat file
+    st.title( 'Time Series Forecasting')
+
+    # Membuat sub header
+    st.subheader('Best seller product')
+
+    # Menambahkan gambar
+    image = Image.open('ts.jpg')
+    st.image(image, caption='Time Series Analysis')
+
+if __name__ == '__main__':
+    run()
+
+
+# Load All Files
+
+with open('mod_train.pkl', 'rb') as file_1:
+  mod_train = pickle.load(file_1)
+
+
+
+# bikin fungsi  
+def run():
+
+  with st.form(key='forecast quantity'):
+      year = st.selectbox('Year', (2023,2024), index=0)
+      month = st.selectbox('Month', (1,2,3,4,5,6,7,8,9,10,11,12), index=3)
+      st.markdown('---')
+
+      submitted = st.form_submit_button('Predict')
+
+
+  data_inf = {
+    'year' : year,
+    'month': month
+  }
+
+  data_inf = pd.DataFrame([data_inf])
+  data_inf['date'] = data_inf.apply(lambda x: datetime(x[0], x[1], 1), axis=1)
+  data_inf.drop(['year','month'], axis=1, inplace=True)
+  data_inf=data_inf.set_index('date')
+  st.dataframe(data_inf)
+
+  if submitted:
+      
+      #Predict Inference data
+      y_pred_test=mod_train.predict(start=data_inf.index[0],end=data_inf.index[-1])
+      y_pred_test=pd.DataFrame(y_pred_test)
+      y_pred_test.columns=['quantity_predict']
+
+      st.write('# Forcasted quantity for that month is : ', str(int(y_pred_test)))
+      
+if __name__ == '__main__':
+    run()

requirements.txt

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+# daftar library yang dibutuhkan semua
+streamlit
+pandas
+seaborn
+matplotlib
+numpy
+scikit-learn==1.2.1
+datetime
+statsmodels
+plotly