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

# Set the title of the Streamlit app
st.title("SuperKart Sales Forecast System")

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

# Collect user input for Product and Store features
Product_Id = st.text_input("Product_Id")
Product_Weight = st.number_input("Product_Weight", min_value=1.0,max_value=100.0, value=1.0)
Product_Sugar_Content = st.selectbox("Product_Sugar_Content", ["No Sugar", "Low Sugar", "Regular"])
Product_Allocated_Area = st.number_input("Product_Allocated_Area", min_value=0.0,max_value=1.0,value=.5)
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"])
Product_MRP = st.number_input("Product_MRP", min_value=0.0)
#Store_Id = st.selectbox("Store_Id", ["OUT001", "OUT002", "OUT003","OUT004"])
Store_Establishment_Year = st.number_input("Store_Establishment_Year", min_value=1987,max_value=2025,value=2009)
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_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,
    'Store_Establishment_Year': Store_Establishment_Year,
    'Store_Size': Store_Size,
    'Store_Location_City_Type': Store_Location_City_Type,
    'Store_Type': Store_Type
}])

# Extract the Product_Code and Store_Age before feeding to the model
input_data["Product_Code"] = input_data["Product_Id"].str[:2]
input_data.drop("Product_Id", axis=1, inplace=True)
    
current_year = datetime.now().year
input_data["Store_Age"] = current_year - input_data["Store_Establishment_Year"]
input_data.drop("Store_Establishment_Year", axis=1, inplace=True)

# Make prediction when the "Predict" button is clicked
if st.button("Forecast"):
    try:
        response = requests.post("https://UncloudMe-SK_Sales_Backend.hf.space/salespredict",
            json=input_data.to_dict(orient='records')[0],
            timeout=30  # add timeout for safety
        )

        if response.status_code == 200:
            prediction = response.json().get('Predicted Sale', None)
            if prediction is not None:
                st.success(f"Predicted Sale: {prediction}")
            else:
                st.error("No prediction found in response.")
        else:
            # show backend error text if available
            st.error(f"Error {response.status_code}: {response.text}")

    except requests.exceptions.RequestException as e:
        # catch all connection, timeout, DNS, etc. errors
        st.error(f"Connection error: {str(e)}")


#if st.button("Forecast"):
#    response = requests.post("https://UncloudMe-SK_Sales_Backend.hf.space/salespredict", 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("https://UncloudMe-SK_Sales_Backend.hf.space/salespredictbatch", 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.")