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}")