Duplicate from KwabenaMufasa/Grocery_Store_Time_Series_Forecasting

Co-authored-by: Foster Nana Kwabena Abrefa <KwabenaMufasa@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+Grocery.csv filter=lfs diff=lfs merge=lfs -text

Dockerfile

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+FROM python:3.9
+
+
+WORKDIR /code
+
+# Create a writable directory for the cache
+RUN mkdir -p /.cache/huggingface/hub && chmod -R 777 /.cache
+
+# Set the TRANSFORMERS_CACHE environment variable
+ENV TRANSFORMERS_CACHE /.cache/huggingface/hub
+
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip3 install --upgrade pip
+
+RUN pip3 install -r requirements.txt
+
+COPY . . 
+
+CMD ["streamlit","run","app.py", "--server.address", "0.0.0.0", "--server.port", "7860", "--browser.serverAddress", "kwasiasomani-Docker.hf.space", "--browser.serverAddress","0.0.0.0:7860"]

Grocery.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c2026ebae35dd7285f1ac84c4dd08c760f672e68aee2aefa128bab8ee0aedad8
+size 162805137

README.md

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+---
+title: Grocery Store Time Series Forecasting
+emoji: 🔥
+colorFrom: blue
+colorTo: red
+sdk: docker
+pinned: false
+duplicated_from: KwabenaMufasa/Grocery_Store_Time_Series_Forecasting
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+# 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
+
+
+
+
+# set api endpoint
+URL = 'https://bright1-sales-forecasting-api.hf.space'
+API_ENDPOINT = '/predict'
+
+# Setting the page configurations
+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')
+
+
+image1 = Image.open('images1.jpg')
+image2 = Image.open('image 2.jpg')
+
+def make_prediction(store_id, category_id, onpromotion, year,month, dayofmonth, 
+                    dayofweek, dayofyear,weekofyear, quarter, is_month_start, is_month_end,
+                    is_quarter_start, is_quarter_end, is_year_start, is_year_end, 
+                    year_weekofyear,city, store_type, cluster):
+    
+    
+    parameters = {
+    'store_id':int(store_id), 
+    'category_id':int(category_id), 
+    'onpromotion' :int(onpromotion),
+    'year' : int(year), 
+    'month' : int(month), 
+    'dayofmonth' :int(dayofmonth),
+    'dayofweek' : int(dayofweek),
+    'dayofyear' : int(dayofyear), 
+    'weekofyear' : int(weekofyear), 
+    'quarter' : int(quarter),
+    'is_month_start' : int(is_month_start),
+    'is_month_end' : int(is_month_end), 
+    'is_quarter_start' : int(is_quarter_start), 
+    'is_quarter_end' : int(is_quarter_end), 
+    'is_year_start' : int(is_year_start),
+    'is_year_end' : (is_year_end), 
+    'year_weekofyear' : int(year_weekofyear),
+    'city' : city,
+    'store_type' : int(store_type), 
+    'cluster': int(cluster),
+
+    } 
+
+
+    response = requests.post(url=f'{URL}{API_ENDPOINT}', params=parameters)
+    sales_value = response.json()['sales']
+    sales_value = round(sales_value, 4)
+    return sales_value
+
+
+st.image(image1, width = 700)
+
+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())))
+
+
+
+# make prediction 
+sales_value = make_prediction(store_id, category_id, onpromotion, year,month, dayofmonth, 
+                  dayofweek, dayofyear,weekofyear, quarter, is_month_start, is_month_end, 
+                  is_quarter_start, is_quarter_end, is_year_start, is_year_end, 
+                  year_weekofyear,city, store_type, cluster)
+
+# get predicted value
+if st.button('Predict'):
+            st.success('The predicted target is ' + str(sales_value))
+  
+  

requirements.txt

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+matplotlib==3.3.4
+numpy==1.22.4
+pandas==1.2.4
+pmdarima==2.0.3
+scipy==1.6.2
+seaborn==0.11.1
+scikit-learn==0.24.1
+xgboost==1.7.3
+streamlit==1.23.1