import streamlit as st
import requests
import pandas as pd

# App Title
st.title("Superkart Product Store Sales Prediction")

# --- Online (Single) Prediction ---
st.subheader("Online Prediction")

# Input Fields
store_id = st.number_input("Store ID", min_value=1, step=1, value=1)
item_id = st.number_input("Item ID", min_value=1, step=1, value=1)
item_price = st.number_input("Item Price (₹)", min_value=0.0, step=1.0, value=100.0)
promotion = st.selectbox("Promotion Applied?", ["Yes", "No"])
holiday = st.selectbox("Is it a Holiday or Weekend?", ["Yes", "No"])
day_of_week = st.selectbox("Day of the Week", ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"])

# Format data as expected by backend
input_data = {
    "store_id": store_id,
    "item_id": item_id,
    "item_price": item_price,
    "promotion": 1 if promotion == "Yes" else 0,
    "holiday": 1 if holiday == "Yes" else 0,
    "day_of_week": day_of_week
}

# Predict Button
if st.button("Predict"):
    try:
        response = requests.post(
            "https://namita2025-superkart-backend.hf.space/predict",  # Replace with your backend URL if needed
            json=input_data
        )
        if response.status_code == 200:
            result = response.json()
            st.success(f"Predicted Sales: {result['predicted_sales']} units")
        else:
            st.error(f"Error from API: Status Code {response.status_code}")
    except Exception as e:
        st.error(f"Failed to connect to the API. Error: {e}")

# --- Batch Prediction ---
st.subheader("Batch Prediction (CSV Upload)")

file = st.file_uploader("Upload a CSV file with multiple records", type=["csv"])
if file is not None:
    if st.button("Predict Batch"):
        try:
            response = requests.post(
                "https://namita2025-superkart-backend.hf.space/predict_batch",  # Replace if needed
                files={"file": file}
            )
            if response.status_code == 200:
                result = response.json()
                df = pd.DataFrame.from_dict(result, orient='index', columns=["Predicted Sales"])
                st.success("Batch Prediction Done!")
                st.dataframe(df)
            else:
                st.error(f"API returned status code {response.status_code}")
        except Exception as e:
            st.error(f"Failed to perform batch prediction. Error: {e}")