first deploy

app.py

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+import streamlit as st
+# import eda
+import prediction
+
+navigation = st.sidebar.selectbox('Pilih halaman:', ('Demand Forecast'))
+
+if navigation == 'Demand Forecast':
+    prediction.run()
+else:
+    prediction.run()

model_forecasting.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:10e6158d8af59fd0e6d054302c4ac322f87e30a118215a0e687511d53c7bba45
+size 12151

prediction.py

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+import streamlit as st
+import pandas as pd
+import pickle
+import json
+# from statsmodels.tsa.holtwinters import ExponentialSmoothing
+
+# Load All Files
+
+with open('model_forecasting.pkl', 'rb') as file_1:
+    model = pickle.load(file_1)
+
+
+def run():
+
+    # create Streamlit app
+    st.title('Demand Forecasting')
+
+    # display prediction
+    # st.write('Prediction:', prediction)
+
+    with st.form(key='form_forecasting_2022'):
+        start_date = st.date_input(
+            'Start date', value=pd.to_datetime('2023-04-16'))
+        end_date = st.date_input(
+            'End date', value=pd.to_datetime('2023-04-30'))
+        st.markdown('---')
+
+        submitted = st.form_submit_button('Predict')
+
+    if submitted:
+
+        prediction = model.predict(start=start_date, end=end_date)
+        st.write("Hasil prediksi pada tanggal ", start_date, ":", prediction)
+
+
+# agar bisa di run sendiri, tanpa dari main
+if __name__ == '__main__':
+    run()

requirements.txt

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+streamlit == 1.20.0
+pandas == 1.5.3
+pickle