import streamlit as st
import pandas as pd
import requests

model_root_url = "https://bala-ai-KartSalesPredictionBackend.hf.space"
model_predict_url = model_root_url+"/v1/kart"  # Base URL of the deployed Flask API on Hugging Face Spaces
model_batch_url = model_root_url+"/v1/kartBatch"


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

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

# Collect user input for property features

Product_Weight = st.number_input("Weight of the Product", min_value=0.1, max_value=100.0, step=1.0, value=20.0)

Product_MRP = st.number_input("MRP of the Product", min_value=0.1, max_value=10000.0, step=1.0, value=100.0)

Store_Establishment_Year = st.number_input("Store established Year", min_value=1800, step=1, value=2005, max_value=2025)

Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "No Sugar", "Regular","reg"])
Product_Type = st.selectbox("Product Type", ["Fruits and Vegetables", "Snack Foods", "Frozen Foods", "Dairy", "Household", "Baking Goods", "Canned", "Health and Hygiene", "Meat", "Soft Drinks", "Breads", "Hard Drinks", "Others", "Starchy Foods", "Breakfast", "Seafood"])
Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
Store_Id = st.selectbox("Store Id", ["OUT001", "OUT002","OUT003","OUT004"])
Store_Type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2","Departmental Store","Food Mart"])
Store_Size = st.selectbox("Store Size", ["High", "Medium","Small"])


Product_Allocated_Area = st.number_input("Allocated display area", min_value=0.001, step=0.01, value=0.2,max_value=1.0)

# Convert user input into a DataFrame
input_data = pd.DataFrame([{

  "Product_Sugar_Content": Product_Sugar_Content,
  "Product_Type": Product_Type,
  "Store_Establishment_Year": Store_Establishment_Year,
  "Store_Location_City_Type": Store_Location_City_Type,
  "Store_Id": Store_Id,
  "Product_MRP": Product_MRP,
  "Product_Weight": Product_Weight,
  "Store_Size": Store_Size,
  "Store_Type" : Store_Type,
  "Product_Allocated_Area" : Product_Allocated_Area


}])

# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
    response = requests.post(model_predict_url, json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
    if response.status_code == 200:
        prediction = response.json()['Predicted Sales']
        st.success(f"Predicted Sales: {prediction}")
    else:
        st.error("Error making prediction.")

# 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(model_batch_url, 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("Error making batch prediction.")