import streamlit as st
import requests

st.title("Sales Forecast Prediction Model") #Complete the code to define the title of the app.

# Input fields for product and store data
Product_Weight = st.number_input("Product Weight (in Kg)", min_value=0.0, value=12.66)
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
Product_Allocated_Area = st.number_input("Product Allocated Area (in fraction of total store area)", min_value=0.0, value=0.075)
Product_MRP = st.number_input("Product MRP ($ value)", min_value=0.0, value=100.00)
Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"])
Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
Store_Type = st.selectbox("Store Type", ["Departmental Store", "Food Mart", "Supermarket Type1","Supermarket Type2"])
# Product_Id_char = st.text_input("Product ID", value="FD100")
Store_Age_Years = st.number_input("Store Age (in years)", min_value=0.0, value=10.0)
# Product_Type_Category = st.selectbox("Product Type", ["Perishables", "Non Perishables"])
Product_Type = st.selectbox(
    "Product Type",
    [
        "Dairy",
        "Meat",
        "Fruits and Vegetables",
        "Breakfast",
        "Breads",
        "Seafood",
        "Snacks",
        "Frozen Foods"
    ]
)

product_data = {
    "Product_Weight": Product_Weight,
    "Product_Sugar_Content": Product_Sugar_Content,
    "Product_Allocated_Area": Product_Allocated_Area,
    "Product_MRP": Product_MRP,
    "Store_Size": Store_Size,
    "Store_Location_City_Type": Store_Location_City_Type,
    "Store_Type": Store_Type,
    # "Product_Id_char": Product_Id_char,
    "Store_Age_Years": Store_Age_Years,
    "Product_Type": Product_Type
}

if st.button("Predict", type='primary'):
    response = requests.post("https://PR118-SalesForecastPrediction-BackendNEW.hf.space/v1/predict", json=product_data)

    if response.status_code == 200:
        result = response.json()
        predicted_sales = result["Sales"]
        st.write(f"Predicted Product Store Sales : ${predicted_sales:.2f}")
    else:
        st.error("Error in API request")



# Section for batch prediction
st.subheader("Batch Prediction")

# Allow users to upload a CSV file for batch prediction
uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])

# Make batch prediction when the "Predict Batch" button is clicked
if uploaded_file is not None:
    if st.button("Predict Batch"):
        response = requests.post(
            "https://PR118-SalesForecastPrediction-BackendNEW.hf.space/v1/predictbatch",
            files={"file": uploaded_file}
            )

        if response.status_code == 200:
            predictions = response.json()
            st.success("Batch predictions completed!")
            st.write(predictions)  # Display the predictions
        else:
            st.error("Error making batch prediction.")