import streamlit as st
import pandas as pd
import requests

# Set page configuration
st.set_page_config(page_title="SuperKart Sales Forecasting App", layout="centered")

# App title and description
st.title("SuperKart Sales Forecasting App")
st.write(
    "This tool predicts the sales revenue of products across SuperKart stores "
    "based on product and store attributes."
)

# Section for online prediction
st.subheader("Online Prediction (Single Product-Store Entry)")

# Collect user input for product & store features
product_weight = st.number_input("Product Weight (kg)", min_value=4.0, max_value=22.0, value=12.65, step=0.1)
product_sugar_content = st.selectbox("Sugar Content", ["Low Sugar", "Regular", "No Sugar", "Non-edible"])
product_allocated_area = st.number_input("Allocated Area (ratio)", min_value=0.004, max_value=0.298, value=0.068, step=0.001)
product_type = st.selectbox("Product Type", [
    "Fruits and Vegetables", "Snack Foods", "Dairy", "Canned", "Soft Drinks",
    "Health and Hygiene", "Baking Goods", "Bread", "Breakfast", "Frozen Foods",
    "Household", "Seafood", "Starchy Foods", "Meat", "Hard Drinks", "Others"
])
product_mrp = st.number_input("Product MRP", min_value=31.0, max_value=266.0, value=147.0, step=1.0)

store_id = st.selectbox("Store ID", ["OUT001","OUT002","OUT003","OUT004"])
store_establishment_year = st.number_input("Store Establishment Year", min_value=1987, max_value=2025, value=2002, step=1)
store_size = st.selectbox("Store Size", ["High", "Medium", "Low"])
store_location_city_type = st.selectbox("Store City Type", ["Tier 1", "Tier 2", "Tier 3"])
store_type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Supermarket Type3", "Food Mart"])

# Input DataFrame with EXACT column names as training
input_data = pd.DataFrame([{
    "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
}])

# Make prediction when "Forecast Sales" is clicked
if st.button("Forecast Sales"):
    try:
        response = requests.post(
            "https://nsa9-ProductStoreSalesBackend.hf.space/v1/forecast",  # Flask backend endpoint
            json=input_data.to_dict(orient="records")[0]
        )
        if response.status_code == 200:
            # First, get the JSON data
            response_json = response.json()
            # Then, check if the key exists before trying to access it
            if "Predicted Sales (in dollars)" in response_json:
                prediction = response_json.get("Predicted Sales (in dollars)")
                st.success(f"Predicted Sales Revenue: ${prediction:,.2f}")
            else:
                st.error("Invalid response format from backend. The expected key 'Predicted Sales (in dollars)' was not found.")
                st.write("Full response from backend:", response_json)
        else:
            st.error(f"Error from backend: {response.status_code}")
            st.write("Full response text:", response.text)
    except requests.exceptions.RequestException as e:
        st.error(f"Request failed: {e}")

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

uploaded_file = st.file_uploader("Upload a CSV file with multiple product-store entries", type=["csv"])

if uploaded_file is not None and st.button("Predict Batch"):
    try:
        response = requests.post(
            "https://nsa9-ProductStoreSalesBackend.hf.space/v1/forecastbatch",  # Flask backend batch endpoint
            files={"file": uploaded_file}
        )
        if response.status_code == 200:
            predictions = response.json()
            st.success("Batch predictions completed!")
            st.write(pd.DataFrame(predictions, index=["Predicted Sales"]))  # Display nicely as a table
        else:
            st.error(f"Error from backend: {response.status_code}")
    except Exception as e:
        st.error(f"Batch request failed: {e}")