import streamlit as st
import pandas as pd
import requests

# Set the title of the Streamlit app
st.title("SuperKart Sale Price Prediction")

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

# Collect user input for property features
Product_Id = "DR1690"
Product_Weight = st.number_input("Product_Weight", min_value=4.0, max_value=20.0, step=0.5)
Product_Sugar_Content = st.selectbox("Product_Sugar_Content", ["Low Sugar", "Regular", "No Sugar"])
Product_Allocated_Area = st.number_input("Product_Allocated_Area", min_value=0.004, step=0.001, max_value=0.295)
Product_Type = st.selectbox("Product_Type", ["Dairy", "Fruits and Vegetables", "Meat", "Bakery", "Baking Goods", "Frozen Foods", "Canned", "Breads", "Breakfast","Hard Drinks","Seafood","Snack Foods","Soft Drinks","Starchy Foods"])
Product_MRP = st.number_input("Product_MRP", min_value=41, step=5, max_value=250)
Store_Id = st.selectbox("Store_Id", ["OUT001", "OUT002", "OUT003", "OUT004"])
Store_Establishment_Year = st.selectbox("Store_Establishment_Year", ["1987", "1998", "1999", "2009"])
Store_Size = st.selectbox("Store_Size", ["Small", "Medium", "High", "OUT004"])
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"])

# Convert user input into a DataFrame
input_data = pd.DataFrame([{
    'Product_Id': Product_Id,
    '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,  # Convert to 't' or 'f'
    '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 the "Predict" button is clicked
if st.button("Predict"):
    response = requests.post("https://SarojRauth-SuperKart.hf.space/v1/sale", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
    if response.status_code == 200:
        prediction = response.json()['Predicted Sale Price (in dollars)']
        st.success(f"Predicted Sale Price (in dollars): {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("https://SarojRauth-SuperKart.hf.space/v1/salebatch", 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.")