import streamlit as st
import pandas as pd
import requests

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

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

st.header("Enter Product and Store Details")

# Collect user input for product store features

product_weight = st.number_input(
    "Product Weight (in kg)", min_value=0.0, step=0.1, value=10.0
)

product_sugar_content = st.selectbox(
    "Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"]
)

product_allocated_area = st.number_input(
    "Product Allocated Area (store fraction)", min_value=0.0, max_value=1.0, step=0.01, value=0.05
)

product_type = st.selectbox(
    "Product Type",
    [
        "Frozen Foods", "Dairy", "Canned", "Baking Goods", "Health and Hygiene",
        "Snack Foods", "Soft Drinks", "Meat", "Fruits and Vegetables", "Breads",
        "Breakfast Foods", "Starchy Foods", "Seafood", "Household", "Others"
    ]
)

product_mrp = st.number_input(
    "Product MRP (Maximum Retail Price)", min_value=0.0, step=1.0, value=150.0
)

store_establishment_year = st.number_input(
    "Store Establishment Year", min_value=1900, max_value=2025, step=1, value=2005
)

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_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://<username>-<repo_id>.hf.space/v1/rental", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
    response = requests.post("https://Santhu976-ProdStoreSalesTotalPredictionBackend.hf.space/v1/sales", json=input_data.to_dict(orient='records')[0])
    if response.status_code == 200:
        prediction = response.json()['Predicted Price']
        st.success(f"Predicted Product_Store_Sales_Total: {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://Santhu976-ProdStoreSalesTotalPredictionBackend.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.")