import requests
import streamlit as st
import pandas as pd

st.title ("SuperKart Product & Store Input Form")

st.write ("Predict sales for SuperKart based on product and store details.")

# ==============================
# Section 1: Product Details
# ==============================
st.subheader ("Product Details")

prod_weight       = st.number_input ("Weight in Units (0.0 to 200.0)", min_value=0.0, max_value=200.0, value=23.0, step=0.1)
prod_alloc_area   = st.number_input ("Allocated Area (fraction 0-1)", min_value=0.0, max_value=1.0, value=0.068)
prod_mrp          = st.number_input ("MRP in Rupees (0.0 to 1000.0)", min_value=0.0, max_value=1000.0, value=147.0)
prod_sug_content  = st.selectbox    ("Sugar Content", ['Low Sugar', 'Regular', 'No Sugar'])
prod_type         = st.selectbox    ("Product Type", [
    'Frozen Foods', 'Dairy', 'Canned', 'Baking Goods', 'Health and Hygiene',
    'Snack Foods', 'Meat', 'Household', 'Hard Drinks', 'Fruits and Vegetables',
    'Breads', 'Soft Drinks', 'Breakfast', 'Others', 'Starchy Foods', 'Seafood'
])

# ==============================
# Section 2: Store Details
# ==============================
st.subheader ("Store Details")

store_id          = st.selectbox    ("Store Id", ['OUT001', 'OUT002', 'OUT003', 'OUT004'])
store_size        = st.selectbox    ("Store Size", ['Small', 'Medium', 'High'])
store_city_type   = st.selectbox    ("Type of City", ['Tier 1', 'Tier 2', 'Tier 3'])
store_type        = st.selectbox    ("Store Type", ['Food Mart', 'Departmental Store', 'Supermarket Type1', 'Supermarket Type2'])

# ==========================
# Single Value Prediction
# ==========================
#if st.button("Predict", type='primary'):
if st.button("Predict Single Product"):

    # extract the data collected into a structure
    input_data = {
        'Product_Weight'            : float (prod_weight),
        'Product_Sugar_Content'     : prod_sug_content,
        'Product_Allocated_Area'    : float(prod_alloc_area),
        'Product_Type'              : prod_type,
        'Product_MRP'               : float(prod_mrp),
        'Store_Id'                  : store_id,
        'Store_Size'                : store_size,
        'Store_Location_City_Type'  : store_city_type,
        'Store_Type'                : store_type
    }

    response = requests.post (
        "https://harishsohani-SuperKartBackEnd.hf.space/v1/SuperKartSales",
        json=input_data
        )

    if response.status_code == 200:
        ## get result as json
        result = response.json ()

        ## Get Sales Prediction Value
        sales_prediction = result.get ("SalesPrediction")  # Extract only the value

        ## format and print predicted value with 2 decimals
        st.success (f"The predicted sales for given input is Rupees {sales_prediction:.2f}")
    else:
        st.error (f"Error processing request- Status Code : {response.status_code}")

# ==============================
# Batch Prediction
# ==============================
st.subheader ("Batch Prediction of SuperKart Sales")

file = st.file_uploader ("Upload CSV file", type=["csv"])

if file is not None and st.button("Predict Batch"):

    inputfile = {"file": (file.name, file.getvalue(), "text/csv")}
    response = requests.post(
        "https://harishsohani-SuperKartBackEnd.hf.space/v1/SuperKartBatchSales",
        files=inputfile
    )

    if response.status_code == 200:

        result = response.json ()

        # convert dict to dataframe for better display
        result_df = pd.DataFrame(list(result.items()), columns=["Product_Id", "Predicted_Sales"])
        st.dataframe (result_df)
    else:
        st.error (f"Error in API request {response.status_code}")