import streamlit as st
import pandas as pd
import requests
from datetime import datetime

# Set the title of the Streamlit app
st.title("Super Kart Sales Prediction")

# Section for online prediction
st.subheader("Online Prediction")

# Product Weight
Product_Weight = st.number_input("Product Weight", min_value=1.0, max_value=100.0, value=10.0, step=0.01)
# Product Sugar Content
Product_Sugar_Content = st.selectbox("Product Sugar Content", options=["Low Sugar", "Regular", "No Sugar"])
# Product Allocated Area
Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.000, max_value=1.00, value=0.05, step=0.001)
# Product Type
Product_Type = st.selectbox(  "Product Type",  options=["Frozen Foods", "Dairy", "Canned", "Baking Goods", "Health and Hygiene", "Breads", "Fruits and Vegetables", "Meat", "Seafood", "Soft Drinks", "Hard Drinks", "Breakfast", "Starchyfoods"])
# Product MRP
Product_MRP = st.number_input("Product MRP", min_value=0.0, max_value=1000.0, value=100.0, step=1.0)
# Store Establishment Year
Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, value=2010, step=1)
# Store Size
Store_Size = st.selectbox("Store Size", options=["Small", "Medium", "High"])
# Store Location City Type
Store_Location_City_Type = st.selectbox("Store Location City Type", options=["Tier 1", "Tier 2", "Tier 3"])
# Store Type
Store_Type = st.selectbox("Store Type",options=["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"])
#Store Id
Store_id = st.selectbox("Store Id",options=["OUT001", "OUT002", "OUT003", "OUT004"])
#calculation of store age
Current_Year = datetime.now().year
Store_Age = Current_Year - Store_Establishment_Year

# Convert user input into a DataFrame
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_Age': Store_Age,
    'Store_Size': Store_Size,
    'Store_Location_City_Type': Store_Location_City_Type,
    'Store_Type': Store_Type,
    "Store_id": Store_id
}])

# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
    response = requests.post("https://cheeka84-SalesPredictionBackend.hf.space/v1/sales", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
    if response.status_code == 200:
        prediction = response.json()['Predicted Sales (in dollars)']
        st.success(f"Predicted Sales (in dollars): {prediction}")
    else:
        st.error(response.status_code)

# 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://cheeka84-SalesPredictionBackend.hf.space/v1/salesbatch", files={"file": uploaded_file})  # Send file to Flask API
        if response.status_code == 200:
            predictions = response.json()
            st.success("Batch predictions completed!")
            st.write(predictions)  # Display the predictions
        else:
            st.error(response.status_code)