update-app-with-api

app.py

@@ -1,149 +1,81 @@
 # Loading key libraries
 import streamlit as st
 import os
-import pickle
 import numpy as np
 import pandas as pd
-import re
-from pathlib import Path
+
 from PIL import Image
 import matplotlib.pyplot as plt
 import seaborn as sns
+import requests
+import datetime 
+
 
+# set api endpoint
+URL = 'https://bright1-grocery-store-sales-forecasting-api.hf.space'
+API_ENDPOINT = '/predict'
+
+# get list/choices for inputs 
+CITIES = ['Accra', 'Aflao', 'Akim Oda', 'Akwatia', 'Bekwai', 'Cape coast', 'Elmina,', 'Gbawe', 'Ho', 'Hohoe', 'intampo', 'Koforidua', 'Kumasi', 'Mampong', 'Obuasi', 'Prestea', 'Suhum', 'Tamale', 'Techiman', 'Tema', 'Teshie', 'Winneba']
+CLUSTER = [ i for i in range(0, 17)]
+STORE_ID = [ i for i in range(1, 55)]
+CATEGORY_ID = [ i for i in range(0, 35)]
 
 
 # Setting the page configurations
-st.set_page_config(page_title= "Prediction Forecasting", layout= "wide", initial_sidebar_state= "auto")
+st.set_page_config(page_title = "Prediction Forecasting", layout= "wide", initial_sidebar_state= "auto")
 
 # Setting the page title
 st.title("Grocery Store Forecasting Prediction")
 
-# Load the saved data
-df = pd.read_csv('Grocery.csv')
-
-
-toolkit = "toolkit_folder"
-@st.cache_resource
-def load_toolkit(filepath = toolkit):
-    with open(toolkit, "rb") as file:
-        loaded_toolkit = pickle.load(file)
-    return loaded_toolkit
-
-
-toolkit = load_toolkit()
-Encoder = toolkit["OneHotEncoder"]
-model = toolkit["model"]
-
 
+# src\app\images1.jpg
+image1 = Image.open('images1.jpg')
 
-# main sections of the app
-menu = st.sidebar.radio('menu',['Home view','Prediction target'])
 
-if menu == 'Home view':
-      st.write('Grocery Store Time Series Forecasting')
-      st.image('images1.jpg',width = 450)
-      st.write('Graphical representation and Data Overview')
-      if st.checkbox('Data Set '):
-            st.table(df.head(15))
-st.title('Charts')
-graph = st.selectbox('Varieties of graphs',['scatter plot','Bar chat','Histogram'])
-if graph == 'scatter plot':
-      fig,ax = plt.subplots(figsize=(10,5))
-      sns.scatterplot(y = 'target',x = 'onpromotion',data = df.iloc[:1000],palette = 'bright',hue = 'city');
-      st.pyplot(fig)
+def make_prediction(store_id, category_id, onpromotion, city, store_type, cluster, date):
+    
+    
+    parameters = {
+    'store_id':int(store_id), 
+    'category_id':int(category_id), 
+    'onpromotion' :int(onpromotion),
+    'city' : city,
+    'store_type' : int(store_type), 
+    'cluster': int(cluster),
+    'date_': date,
+
+    } 
+
+    # make a request to the api
+    response = requests.post(url=f'{URL}{API_ENDPOINT}', params=parameters)
+    
+    sales_value = response.json()['sales']
 
-if graph == 'Bar chat':
-       fig,ax = plt.subplots(figsize=(10,5))
-       t = df.groupby("city")["target"].sum().reset_index().sort_values(by="target",ascending=False).iloc[:10]
-       sns.barplot(data=t[:20] , y="target", x="city", palette='Blues_d')
-       st.pyplot(fig)
+    sales_value = round(sales_value, 4)
+    return sales_value
 
-if graph == 'Histogram':
-        fig,ax = plt.subplots(figsize=(10,5))
-        st.write('Target Categories')
-        sns.distplot(df.target.iloc[:20], kde=True)
-        st.pyplot(fig)
-      
 
+st.image(image1, width = 700)
 
+st.sidebar.markdown('User Input Details and Information')
 
+# Create interface
+date= st.sidebar.date_input("Enter the Date",datetime.date(2023, 6, 30))
+store_id= st.sidebar.selectbox('Store id', options=STORE_ID)
+category_id= st.sidebar.selectbox('categegory_id', options=CATEGORY_ID)
+onpromotion= st.sidebar.number_input('onpromotion', step=1)
+city =  st.sidebar.selectbox("city:", options= CITIES)
+store_type=  st.sidebar.selectbox('type', options=[0, 1, 2, 3, 4])
+cluster = st.sidebar.selectbox('cluster', options = CLUSTER )
 
-if menu == 'Prediction target':
-    st.image('image 2.jpg', width = 460)
-    
-    st.sidebar.markdown('User Input Details and Information')
-
-    store_id= st.sidebar.selectbox('store_id', options = sorted(list(df['store_id'].unique())))
-    category_id= st.sidebar.selectbox('categegory_id',options = sorted(list(df['category_id'].unique())))
-    onpromotion= st.sidebar.number_input('onpromotion', min_value= df["onpromotion"].min(), value= df["onpromotion"].min())
-    year = st.sidebar.selectbox('year', options = sorted(list(df['year'].unique())))
-    month = st.sidebar.selectbox('month', options = sorted(list(df['month'].unique())))
-    dayofmonth= st.sidebar.number_input('dayofmonth', min_value= df["dayofmonth"].min(), value= df["dayofmonth"].min())
-    dayofweek = st.sidebar.number_input('dayofweek', min_value= df["dayofweek"].min(), value= df["dayofweek"].min())
-    dayofyear = st.sidebar.number_input('dayofyear', min_value= df["dayofyear"].min(), value= df["dayofyear"].min())
-    weekofyear = st.sidebar.number_input('weekofyear', min_value= df["weekofyear"].min(), value= df["weekofyear"].min())
-    quarter  = st.sidebar.number_input('quarter', min_value= df["quarter"].min(), value= df["quarter"].min())
-    is_month_start = st.sidebar.number_input('is_month_start', min_value= df["is_month_start"].min(), value= df["is_month_start"].min())
-    is_month_end = st.sidebar.number_input('is_month_end', min_value= df["is_month_end"].min(), value= df["is_month_end"].min())
-    is_quarter_start = st.sidebar.number_input('is_quarter_start', min_value= df["is_quarter_start"].min(), value= df["is_quarter_start"].min())
-    is_quarter_end = st.sidebar.number_input('is_quarter_end', min_value= df["is_quarter_end"].min(), value= df["is_quarter_end"].min())
-    is_year_start = st.sidebar.number_input('is_year_start', min_value= df["is_year_start"].min(), value= df["is_year_start"].min())
-    is_year_end = st.sidebar.number_input('is_year_end', min_value= df["is_year_end"].min(), value= df["is_year_end"].min())
-    year_weekofyear = st.sidebar.number_input('year_weekofyear', min_value= df["year_weekofyear"].min(), value= df["year_weekofyear"].min())
-    city =  st.sidebar.selectbox("city:", options= sorted(set(df["city"])))
-    store_type=  st.sidebar.number_input('type', min_value= df["type"].min(), value= df["type"].min())
-    cluster = st.sidebar.selectbox('cluster', options = sorted(list(df['cluster'].unique())))
-
-
-
-    input_df = {
-            'store_id':[store_id], 
-            'category_id':[category_id], 
-            'onpromotion' :[onpromotion], 
-            'year' : [year], 
-            'month' :[month], 
-            'dayofmonth' :[dayofmonth],
-            'dayofweek' : [dayofweek],
-            'dayofyear' : [dayofyear], 
-            'weekofyear' : weekofyear, 
-            'quarter' : [quarter], 
-            'is_month_start' : [is_month_start],
-            'is_month_end' : [is_month_start], 
-            'is_quarter_start' : [is_quarter_start], 
-            'is_quarter_end' : [is_quarter_end], 
-            'is_year_start' : [is_year_start],
-            'is_year_end' : [is_year_end], 
-            'year_weekofyear' : [year_weekofyear],
-            'city' : [city], 
-            'type' : [store_type], 
-            'cluster': [cluster]
-} 
-
- # Put the input dictionary in a dataset
-    input_data = pd.DataFrame(input_df)
-
-
-
-# defining categories and numeric columns
-
-    col = ['city']
-    columns = list(input_data.columns)
-    encoded_cat = Encoder.transform(input_data[col])
-    encoded_cols = Encoder.get_feature_names()
-    encoded_cat_ = pd.DataFrame(encoded_cat, columns=encoded_cols)
 
 
-    
-    # we dropped the categorical encoder column before we concat 
-    train_enc = input_data.drop(['city'],axis = 1)
-    input_d = pd.concat([train_enc, encoded_cat_], axis=1)
-
-    # convert input_data to a numpy array before flattening to convert it back to a 2D array
-    input_df= input_d.to_numpy()
-    prediction = model.predict(input_df.flatten().reshape(1, -1))
-    
 
-    if st.button('Predict'):
-               st.success('The predicted target is ' + str(round(prediction[0],2)))
+# get predicted value
+if st.sidebar.button('Predict', use_container_width=True, type='primary'):
+            # make prediction 
+            sales_value = make_prediction(store_id, category_id, onpromotion,city, store_type, cluster, date)
+            st.success('The predicted target is ' + str(sales_value))