File size: 2,602 Bytes
c82e571 992f161 9ef4a35 c82e571 9ef4a35 c82e571 9ef4a35 c82e571 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 |
import streamlit as st
import pandas as pd
import requests
API_ENDPOINT="https://TokenTutor-ProductSalesRevenuePrediction.hf.space/v1/product_sales_revenue"
#product type
product_types = [
"Fruits and Vegetables",
"Snack Foods",
"Frozen Foods",
"Dairy",
"Household",
"Baking Goods",
"Canned",
"Health and Hygiene",
"Meat",
"Soft Drinks",
"Breads",
"Hard Drinks",
"Others",
"Starchy Foods",
"Breakfast",
"Seafood"
]
#store types
store_types = [
"Food Mart",
"Supermarket Type1",
"Supermarket Type2",
"Departmental Store"
]
#Store Id
store_ids = [
"OUT001",
"OUT002",
"OUT003",
"OUT004"
]
store_Location_City_Types=[
"Tier 1",
"Tier 2",
"Tier 3"
]
store_sizes=[
"Small",
"Medium",
"Large"
]
#Set title of the Streamlit app
st.title("Product Revenue prediction")
#Section for online prediction
st.subheader("Online Prediction")
#Collect user input for features
# Product_Weight = st.number_input("Product Weight", min_value=4.0, max_value=25.0, step=0.5)
# Product_Sugar_Content = st.selectbox("Product Sugar Content", ["No Sugar", "Low Sugar", "Regular"])
# Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.001, max_value=0.3)
# Product_Type = st.selectbox("Product Type", product_types)
# Product_MRP = st.number_input("Product MRP", min_value=30.0, max_value=300.0)
# Store_Id = st.selectbox("Store Id", store_ids)
# Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1988, max_value=2010, step=1)
# Store_Size = st.selectbox("Store Size", store_sizes)
# Store_Location_City_Type = st.selectbox("Store Location City Type", store_Location_City_Types)
# Store_Type = st.selectbox("Store Type", store_types)
# payload = {
# 'Product_Weight': Product_Weight,
# 'Product_Sugar_Content': Product_Sugar_Content,
# 'Product_Allocated_Area': Product_Allocated_Area,
# 'Product_Type': Product_Type ,
# 'Product_MRP': Product_MRP,
# 'Store_Id': Store_Id,
# 'Store_Establishment_Year': Store_Establishment_Year,
# 'Store_Size': Store_Size,
# 'Store_Location_City_Type': Store_Location_City_Type,
# 'Store_Type': Store_Type
# }
# if st.button("Predict"):
# response = requests.post(API_ENDPOINT, json=payload)
# if response.status_code == 200:
# json_data= response.json()
# st.write('Predicted Sales revenue ', json_data.get('Prediction'))
# else:
# st.write(f"Error making prediction: {response.status_code}")
|