Forecasting.py

import pickle
import streamlit as st
import pandas as pd 
from plotly import graph_objs as go


# loading the trained model
pickle_gulamah = open('model_gulamah.pkl', 'rb') 
pickle_lisong = open('model_lisong.pkl', 'rb') 
pickle_lobster = open('model_lobster.pkl', 'rb') 
pickle_manyung = open('model_manyung.pkl', 'rb') 
pickle_nila = open('model_nila.pkl', 'rb') 
pickle_tengiri = open('model_tengiri.pkl', 'rb') 
pickle_tongkol = open('model_tongkol.pkl', 'rb') 
pickle_udang = open('model_udang.pkl', 'rb') 
model_gulamah = pickle.load(pickle_gulamah)
model_lisong = pickle.load(pickle_lisong)
model_lobster = pickle.load(pickle_lobster)
model_manyung = pickle.load(pickle_manyung)
model_nila = pickle.load(pickle_nila)
model_tengiri = pickle.load(pickle_tengiri)
model_tongkol = pickle.load(pickle_tongkol)
model_udang = pickle.load(pickle_udang)


@st.cache()

# defining the function which will make the prediction using the data which the user inputs 
def prediction(ikan_type, months): 
    # Pre-processing user input    
    if ikan_type == "Gulamah":
        data = pd.read_csv("gulamah.csv", index_col='Produksi Hasil Tangkap Laut', parse_dates=True)
        data.index.freq = 'MS'
        model = model_gulamah
    elif ikan_type == "Lisong":
        data = pd.read_csv("lisong.csv", index_col='Produksi Hasil Tangkap Laut', parse_dates=True)
        data.index.freq = 'MS'
        model = model_lisong
    elif ikan_type == "Lobster":
        data = pd.read_csv("lobster.csv", index_col='Produksi Hasil Tangkap Laut', parse_dates=True)
        data.index.freq = 'MS'
        model = model_lobster
    elif ikan_type == "Manyung":
        data = pd.read_csv("manyung.csv", index_col='Produksi Hasil Tangkap Laut', parse_dates=True)
        data.index.freq = 'MS'
        model = model_manyung
    elif ikan_type == "Nila":
        data = pd.read_csv("nila.csv", index_col='Produksi Hasil Tangkap Laut', parse_dates=True)
        data.index.freq = 'MS'
        model = model_nila
    elif ikan_type == "Tengiri":
        data = pd.read_csv("tengiri.csv", index_col='Produksi Hasil Tangkap Laut', parse_dates=True)
        data.index.freq = 'MS'
        model = model_tengiri
    elif ikan_type == "Tongkol":
        data = pd.read_csv("tongkol.csv", index_col='Produksi Hasil Tangkap Laut', parse_dates=True)
        data.index.freq = 'MS'
        model = model_tongkol
    elif ikan_type == "Udang":
        data = pd.read_csv("udang.csv", index_col='Produksi Hasil Tangkap Laut', parse_dates=True)
        data.index.freq = 'MS'
        model = model_udang
    
    # Making predictions 
    start = len(data)
    end = len(data) + int(round(months))-1
    forecast = model.predict(start=start, end=end, dynamic=False, typ='levels')
    df_forecast = pd.DataFrame(forecast)
    df_forecast.index.freq = 'MS'
    
    return df_forecast, data

	


# this is the main function in which we define our webpage  
def main():       
    # front end elements of the web page 
    html_temp = """ 
    <div style ="background-color:yellow;padding:13px"> 
    <h1 style ="color:black;text-align:center;">Forecasting Kebutuhan Ikan (Ton)</h1> 
    </div> 
    """
      
    # display the front end aspect
    st.markdown(html_temp, unsafe_allow_html = True) 
      
    # following lines create boxes in which user can enter data required to make prediction 
    input_ikan = st.selectbox('Jenis Ikan',("Udang","Lobster","Nila","Tengiri","Manyung","Gulamah","Lisong","Tongkol")) 
    input_months = st.slider('Number of months that will be predicted', min_value=0, max_value=6, step=1)
    # input_months = st.number_input('Number of months that will be predicted', step=1)
    result =""
    data2 =""
      
    # when 'Predict' is clicked, make the prediction and store it 
    if st.button("Predict"): 
        result, data = prediction(input_ikan, input_months) 
        st.success(f'Forecasting results for the next {input_months} months :\n')
        st.write(result)

        data2 = data[[input_ikan]]
        data2 = pd.concat([data2,result])

        fig = go.Figure()
        fig.add_trace(go.Scatter(x=data2.iloc[0:48].index, y=data2[input_ikan].iloc[0:48], name='Data Actual'))
        if input_ikan != "Manyung":
            fig.add_trace(go.Scatter(x=data2.iloc[48:].index, y=data2["predicted_mean"].iloc[48:], name='Forecasts'))
        else:
            fig.add_trace(go.Scatter(x=data2.iloc[48:].index, y=data2[data2.columns[0]].iloc[48:], name='Forecasts'))
            # kenapa ga muncul???? yg buat manyung
        fig.layout.update(title_text=input_ikan, xaxis_rangeslider_visible=True)
        st.plotly_chart(fig)
      

        
if __name__=='__main__': 
    main()