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app.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Wed Sep 27 17:45:59 2023
+env: dardos2
+@author: forti
+
+-Grafica training y validation sets para comparar eficiencia de pronostico
+- Grafica con plotly y matplotlib.
+-Genera df_reindexed con periodo semanal
+-Calcula sigma y safety stock
+- Calcula MOVING AVERAGE ( descomentar)
+
+"""
+
+import numpy as np
+import pandas as pd
+# import matplotlib.pyplot as plt
+import plotly.io as pio
+import plotly.express as px
+from darts import TimeSeries
+from darts.models import RandomForest
+# from darts.models import NaiveDrift
+# from darts.models import MovingAverageFilter
+import streamlit as st
+# import locale
+from darts.metrics import mae, rmse
+from PIL import Image
+
+
+###########################################################################
+@st.cache_data
+def convert_df(df_new):
+    # IMPORTANT: Cache the conversion to prevent computation on every rerun
+    return df_new.to_csv().encode('utf-8')
+###########################################################################
+
+
+pio.renderers.default = "browser"
+
+st.title(" 🏪 Super Playitas")
+st.header("🤖 Pronósticos con Inteligencia Artificial 🤖")
+df = None
+uploaded_file = st.file_uploader("Seleccione su producto", type='xlsx')
+
+if uploaded_file is not None:
+    df = pd.read_excel(uploaded_file)
+    # st.write(data)
+    
+if df is not None:
+
+    ################ Limpieza de datos ###########################
+    # df = pd.read_excel('marlboro-20.xlsx')
+    # df = pd.read_excel('cocacola-600.xlsx')
+    df.drop([0, 1, 2, 3, 4, 5], inplace=True)
+    df.columns = df.iloc[0]
+    df.drop(6, inplace=True)
+    ###########################################################
+    
+    # Set to Spanish or English locale
+    #locale.setlocale(locale.LC_TIME, "en_US.UTF-8")
+    # locale.setlocale(locale.LC_TIME, "es_US.UTF-8")
+    
+    
+    # Create a dictionary mapping Spanish month abbreviations to English
+    month_dict = {
+        'Ene': 'Jan',
+        'Feb': 'Feb',
+        'Mar': 'Mar',
+        'Abr': 'Apr',
+        'May': 'May',
+        'Jun': 'Jun',
+        'Jul': 'Jul',
+        'Ago': 'Aug',
+        'Sep': 'Sep',
+        'Oct': 'Oct',
+        'Nov': 'Nov',
+        'Dic': 'Dec'
+    }
+
+    # Assume df is your DataFrame and 'date' is the column with dates
+    df['Fecha'] = df['Fecha'].replace(month_dict, regex=True)
+    
+    # convert the date column into datetime
+    df['Fecha'] = pd.to_datetime(df['Fecha'], format='%d/%b/%Y %I:%M %p', errors='coerce')
+    
+    # rename columns
+    df.columns.values[2] = 'Cantidad-1'
+    df.columns.values[5] = 'Cantidad-2'
+    df.columns.values[8] = 'Cantidad-3'
+    df['Cantidad-2'] = pd.to_numeric(df['Cantidad-2'], errors='coerce')
+    
+    # lee el ultimo valor de la columna de cantidad-3
+    df['Cantidad-3'] = pd.to_numeric(df['Cantidad-3'], errors='coerce')
+    inventario_neto = df['Cantidad-3'].iloc[-1]
+    
+    # drop NaN rows from column "Cantidad-2"
+    df.dropna(subset=['Cantidad-2'], inplace=True)
+    
+    # drop negative values from "Cantidad-2"
+    df = df[df['Cantidad-2'] >= 0]
+    
+    # Create a new column 'Fecha_sin_hora' with just the date
+    df['Fecha_sin_hora'] = df['Fecha'].dt.date
+    
+    # Group by 'Fecha_sin_hora' and sum 'Cantidad-2'
+    df_sum = df.groupby('Fecha_sin_hora')['Cantidad-2'].sum().reset_index()
+    
+    ###############################################################
+    # # Create a new column 'Mes' with just the month and year
+    # df['Mes'] = df['Fecha_sin_hora'].dt.to_period('M')
+    
+    # # Group by 'Mes' and sum 'Cantidad-2'
+    # df_sum = df.groupby('Mes')['Cantidad-2'].sum().reset_index()
+    
+    ###############################################################
+    
+    # df_sum.drop(737, inplace=True)
+    # df_sum = df_sum.iloc[699:800]
+    
+    
+    
+    
+    
+    ################################################################
+    ######## Create a DataFrame with a new date range ############################
+    
+    # Create the date range
+    # new_date_range = pd.date_range(start='2023-03-12', end='2023-08-11')
+    new_date_range = pd.date_range(start=df_sum.iloc[0,0], end=df_sum.iloc[-1,0])
+    
+    # Convert the date range to a DataFrame
+    #df_3 = pd.DataFrame({'Fecha': new_date_range})
+    ##################################################################
+    
+    df_sum.set_index('Fecha_sin_hora', inplace=True)
+    
+    
+    # Re-index with a new date-range for each group
+    #df_reindexed = df_sum.apply(lambda x: x.set_index('Fecha_sin_hora').reindex(new_date_range))
+    df_reindexed = df_sum.apply(lambda x: x.reindex(new_date_range))
+    
+    # Reset the index
+    #df_reindexed = df_reindexed.reset_index(level=0, drop=True).reset_index()
+    
+    
+    # Fill missing sales values with 0
+    df_reindexed['Cantidad-2'] = df_reindexed['Cantidad-2'].fillna(0)
+    
+    
+    #################################################################
+    # Create a new column 'Semana'
+    
+    df_reindexed.reset_index(inplace=True)
+    df_reindexed = df_reindexed.rename(columns={'index': 'Fecha'})
+    df_reindexed['Semana'] = df_reindexed['Fecha'].dt.to_period('W')
+    
+    # Group by 'Semana' and sum 'Cantidad-2'
+    df_reindexed = df_reindexed.groupby('Semana')['Cantidad-2'].sum().reset_index()
+    # rename columns
+    df_reindexed.columns.values[0] = 'Fecha'
+    
+    # ax = df_reindexed.plot(label='Historial')
+    # plt.show()
+    
+    
+    #hacer fecha el indice
+    # df_reindexed.set_index('Fecha', inplace=True)
+    df_reindexed['Fecha'] = df_reindexed['Fecha'].astype(str)
+    df_reindexed['Fecha'] = df_reindexed['Fecha'].str.split('/').str[0]
+    
+    
+    
+    
+    # usar para pronostico de las ultimas dos semanas. fecha automatizada
+    # dt = pd.to_datetime(df_sum.index[1])
+    # dt = dt + pd.Timedelta(days=1)
+    #################################################################
+    
+    # Convert datetime to string to be able to use DARTS
+    #df_sum['Fecha_sin_hora'] = df_sum['Fecha_sin_hora'].astype(str)
+    
+    
+    ########## Reset the index.   Use only for daily df_reindexed ##################
+    # df_reindexed['Fecha'] = df_reindexed.index
+    # df_reindexed.reset_index(drop=True, inplace=True)
+    # df_reindexed = df_reindexed[['Fecha', 'Cantidad-2']]
+    
+    
+    # Create a TimeSeries object, specifying the time and value columns
+    series = TimeSeries.from_dataframe(df_reindexed, "Fecha", 'Cantidad-2')
+    # series = TimeSeries.from_dataframe(df_reindexed, freq="W")
+    
+    # Assuming df_reindexed is your DataFrame and it's indexed by date
+    # series = TimeSeries.from_times_and_values(times=df_reindexed, values=df_reindexed['Cantidad-2'])
+    
+    
+    
+    # Set aside the last 16 days as a validation series
+    train, val = series[:-15], series[-15:]
+    
+    #################### Train Forecasting Models ######################
+    # Train baseline model
+    # model_1 = NaiveDrift()
+    # model_1.fit(series)
+    
+    # Train ML model
+    model_2 = RandomForest(lags=20) #25 #55
+    model_2a = RandomForest(lags=20)
+    # model_2.fit(train)
+    model_2.fit(series)
+    model_2a.fit(train)
+    
+    # MOVING AVERAGE
+    # model_3 = MovingAverageFilter(window=15)
+    # filtered_series = model_3.filter(series)
+    ####################################################################
+    
+    ################## Predict with Forecasting Models #################
+    # Predict using baseline model
+    # prediction_1 = model_1.predict(15)
+    
+    # Predict using RandomForest
+    # prediction_2 = model_2.predict(len(val))
+    prediction_2 = model_2.predict(15)
+    prediction_2a = model_2a.predict(15)
+    # df_4 = pd.DataFrame(prediction_2.pd_dataframe())
+    # df_4 = df_4.reset_index(drop=True)
+    ###################################################################
+    
+    ######## plot forecast from validation set ########################
+    
+    # converts datetime objects to pandas dataframes
+    series_df = series.pd_dataframe()
+    prediction_2_df = prediction_2.pd_dataframe()
+    prediction_2a_df = prediction_2a.pd_dataframe()
+    val_df = val.pd_dataframe()
+    
+    
+    # series.plot(label="Historial de Ventas")
+    # prediction_2a.plot(label="Pronóstico", low_quantile=0.05, high_quantile=0.95)
+    
+    # filtered_series.plot(label="Historial de Ventas")
+    # series.plot(label="Historial de Ventas")
+    
+    # plt.xlabel('Fecha')
+    # plt.ylabel('Ventas')
+    # plt.legend()
+    # plt.subplots_adjust(bottom=0.2)
+    # plt.savefig("producto_forecast_2.png", dpi=200)
+    
+    
+    # n=115
+    # series_df = series_df.tail(n)
+    
+    # ax = series_df.plot(label='Historial')
+    # prediction_2_df.plot(ax=ax, label='Pronostico')
+    # plt.legend()
+    # plt.show()
+    
+    
+    ###############################################################################
+    
+    
+    #########################Cacula std del forecast   ############################
+    
+    error = prediction_2a_df["Cantidad-2"] - val_df["Cantidad-2"]
+    mean = error.mean()
+    diff = error - mean
+    diff_squared = diff.apply(lambda x:x**2).sum()
+    std = np.sqrt(diff_squared/(len(diff)-1))
+    safety_stock = 1.96*std
+    
+    
+    ###############################################################################
+    ##########################   metricas  ################################
+
+    mae_1 = mae(val, prediction_2a)
+    # mape_1 = mape(val, prediction_2a)
+    rmse_1 = rmse(val, prediction_2a)
+    
+    
+    ##################### Pronostico final ##################################
+    
+    promedio = prediction_2_df.mean()
+    
+    pronostico_promedio = promedio + safety_stock
+    # semana = prediction_2_df.index[0]
+    
+    #redondea y  los numeros del df. si la borro siguie funcionando todo
+    prediction_2_df = prediction_2_df.round().astype(int)
+    safety_stock = safety_stock.round()
+    
+    ##################Grafica con plotly###########################################
+    # df_borrar = df_sum +1
+    # df_borrar = df_borrar.iloc[800:844]
+    
+    figure_01=px.line(series_df,y="Cantidad-2",)
+    figure_01.layout.update(title_text=None,xaxis_rangeslider_visible=True)
+    
+    figure_02=px.line(prediction_2_df,y="Cantidad-2")
+    figure_02.update_traces(line=dict(color='red'))
+    
+    figure_03=px.scatter(series_df,y="Cantidad-2",)
+    figure_03.update_traces(marker=dict(color='DodgerBlue'))
+    
+    figure_04=px.scatter(prediction_2_df,y="Cantidad-2",)
+    figure_04.update_traces(marker=dict(color='red'))
+    
+    # st.plotly_chart(figure_01)
+    
+    # Add figure_02 data to figure_01
+    for trace in figure_02.data:
+        figure_01.add_trace(trace)
+        
+    # Add figure_03 data to figure_01
+    for trace in figure_03.data:
+        figure_01.add_trace(trace)
+        
+    for trace in figure_04.data:
+        figure_01.add_trace(trace)
+    
+    st.subheader(":blue[Historial] y :red[Pronóstico] de Ventas Semanal")
+    
+
+        
+    st.plotly_chart(figure_01)
+    
+    ###############################################################################
+    
+    
+    
+    if mae_1 < 7:
+    
+        st.subheader("Pronóstico de las proximas 15 semanas:")
+        prediction_2_total = prediction_2_df + safety_stock
+        prediction_2_total.index = pd.to_datetime(prediction_2_total.index).date
+        prediction_2_total.rename(columns={'Cantidad-2': 'Nivel de Inventario'}, inplace=True)
+
+        prediction_2_total['Pronostico'] = prediction_2_df['Cantidad-2']
+        # Create an array with the number repeated the desired number of times
+        stock = np.full((len(prediction_2_total), 1), safety_stock)
+        # Convert the array to a DataFrame
+        stock_df = pd.DataFrame(stock, columns=['safety']).astype(int)
+        
+        prediction_2_total['Stock de Seguridad'] = stock_df['safety'].values
+        prediction_2_total = prediction_2_total[['Pronostico', 'Stock de Seguridad', 'Nivel de Inventario']]
+        prediction_2_total['Nivel de Inventario'] = prediction_2_total['Nivel de Inventario'].round().astype(int)
+        st.table(prediction_2_total)
+
+    
+    else:
+        st.subheader("Pronóstico de las proximas 15 semanas:")
+        pronostico_promedio = pronostico_promedio.iloc[0]
+        
+        # Create an array with the number repeated the desired number of times
+        pronostico_promedio = np.full((len(prediction_2_df), 1), pronostico_promedio)
+        # Convert the array to a DataFrame
+        pronostico_promedio_df = pd.DataFrame(pronostico_promedio, columns=['Nivel de Inventario'])
+        
+        prediction_2_df["Nivel de Inventario"] = pronostico_promedio_df['Nivel de Inventario'].values
+        
+        # Create an array with the number repeated the desired number of times
+        stock = np.full((len(prediction_2_df), 1), safety_stock)
+        # Convert the array to a DataFrame
+        stock_df = pd.DataFrame(stock, columns=['safety']).astype(int)
+        prediction_2_df['Stock de Seguridad'] = stock_df['safety'].values
+        
+        prediction_2_total = prediction_2_df
+        prediction_2_total.index = pd.to_datetime(prediction_2_total.index).date
+        prediction_2_total.rename(columns={'Cantidad-2': 'Pronostico'}, inplace=True)
+        prediction_2_total = prediction_2_total[['Pronostico', 'Stock de Seguridad', 'Nivel de Inventario']]
+        
+        prediction_2_total['Nivel de Inventario'] = prediction_2_total['Nivel de Inventario'].round().astype(int)
+        st.table(prediction_2_total)
+    
+    
+ #################################################################################   
+    
+    csv = convert_df(prediction_2_total)    
+    st.download_button(
+       label="Descargar datos ⤵",
+       data=csv,
+       file_name='pronostico-producto.csv',
+       mime='text/csv',
+       )
+    
+    
+    cantidad = round(prediction_2_total.iloc[0,0] - inventario_neto)
+    
+    if cantidad<0:
+        cantidad =0
+        
+    # st.text("Cantidad =" + str(cantidad))
+    st.subheader("Cantidad de producto a ordenar (por semana): ")
+    # st.text("Cantidad = Pronostico - Inventario Neto")
+    
+
+    st.metric(label=":blue[Cantidad = Nivel de Inventario - Inventario Neto]", value=cantidad)
+    st.write('\n')
+    st.write('\n')
+    st.write('\n')
+    st.write('\n')
+    st.write('\n')
+    st.write('\n')
+    st.write('\n')
+    st.write('\n')
+    st.write('\n')
+
+    image = Image.open("magna.png")
+    st.image(image, caption='Inteligencia Artificial para tu negocio')
+    st.markdown("Contáctanos: www.magna-machina.com")
+    
+
+else:
+    st.subheader("No ha seleccionado su producto.")
+    st.write('\n')
+    st.write('\n')
+    st.write('\n')
+    st.write('\n')
+    st.write('\n')
+    st.write('\n')
+    st.write('\n')
+    st.write('\n')
+    st.write('\n')
+    st.write('\n')
+    st.write('\n')
+    st.write('\n')
+    st.write('\n')
+    st.write('\n')
+    st.write('\n')
+    st.write('\n')
+    image = Image.open("magna.png")
+    st.image(image, caption='Inteligencia Artificial para tu negocio')
+    st.markdown("Contáctanos: www.magna-machina.com")
+
+
+
+
+
+

requirements.txt

@@ -0,0 +1,7 @@
+pandas==1.5.2
+darts==0.24.0
+matplotlib==3.7.1
+streamlit==1.26.0
+numpy==1.24.3
+plotly==5.14.1
+openpyxl==3.1.2