import streamlit as st
import pandas as pd
import requests

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

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

# Collect user input for property features
Product_Weight = st.number_input("Product_Weight", min_value=0.00, max_value=100.00, step=0.01, value=0.0)
Product_Sugar_Content = st.selectbox("Product_Sugar_Content" , ["Low Sugar", "Regular", "No Sugar"])
Product_Allocated_Area = st.number_input("Product_Allocated_Area", min_value=0.000, max_value=1.000, step=0.001, value=0.000)
Product_Type = st.selectbox("Product_Type" , ["Frozen Foods","Dairy","Canned","Baking Goods","Health and Hygiene","Snack Foods","Meat","Household","Hard Drinks","Fruits and Vegetables","Breads","Soft Drinks","Breakfast","Others","Starchy Foods","Seafood"
])
Product_MRP = st.number_input("Product_MRP", min_value=0.0, step=0.01, value=0.0)
#Store_Id = st.selectbox("Store_Id" , ["OUT001","OUT002","OUT003","OUT004"])
Store_Establishment_Year = st.number_input("Store_Establishment_Year", min_value=1800, max_value=3000, step=1, value=1900)
Store_Size = st.selectbox("Store_Size" , ["Small","Medium","High"])
Store_Location_City_Type = st.selectbox("Store_Location_City_Type" , ["Tier 1","Tier 2","Tier 3"])
Store_Type = st.selectbox("Store_Type" , ["Supermarket Type1","Supermarket Type2","Departmental Store","Food Mart"])


# 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_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 the "Predict" button is clicked
if st.button("Predict"):
    response = requests.post("https://adityasharma0511-StoreSalesPredictionBackend.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 Store Sales (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://adityasharma0511-StoreSalesPredictionBackend.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("Error making batch prediction.")