st.set_page_config(page_title="SuperKart Sales Forecasting App", layout="wide")
st.title("SuperKart Sales Forecasting App")
st.write("Enter the product and store details to get a sales forecast.")

# Collect user input
product_weight = st.number_input("Product Weight", min_value=1.0, max_value=25.0, value=10.0, step=0.1)
product_sugar_content = st.selectbox("Product Sugar Content", ['Low Sugar', 'Regular', 'No Sugar'])
product_allocated_area = st.number_input("Product Allocated Area", min_value=0.0, max_value=0.5, value=0.1, step=0.01)
product_mrp = st.number_input("Product MRP", min_value=10.0, max_value=300.0, value=150.0, step=1.0)
product_type = st.selectbox("Product Type", ['Meat', 'Snack Foods', 'Hard Drinks', 'Dairy', 'Canned', 'Soft Drinks', 'Health and Hygiene', 'Baking Goods', 'Bread', 'Breakfast', 'Frozen Foods', 'Fruits and Vegetables', 'Household', 'Seafood', 'Starchy Foods', 'Others'])
store_establishment_year = st.selectbox("Store Establishment Year", list(range(1985, 2023)))
store_size = st.selectbox("Store Size", ['High', 'Medium', 'Low'])
store_location_city_type = st.selectbox("Store Location City Type", ['Tier 1', 'Tier 2', 'Tier 3'])
store_type = st.selectbox("Store Type", ['Departmental Store', 'Supermarket Type 1', 'Supermarket Type 2', 'Food Mart'])

# Construct the input data dictionary
# The keys here must exactly match the column names expected by your backend model
input_data = {
    'Product_Id': ["dummy"], # Placeholder, if not used by model during training
    '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': ["dummy"], # Placeholder, if not used by model during training
    'Store_Establishment_Year': [store_establishment_year],
    'Store_Size': [store_size],
    'Store_Location_City_Type': [store_location_city_type],
    'Store_Type': [store_type],
}

# --- Predict Button ---
if st.button("Predict Sales"):
    # Define the URL of your deployed Flask API
    # You'll get this URL from your Hugging Face Backend Space settings after deployment.
    # It will be in the format: https://<your-username>-<space-name>.hf.space
    # You then append '/predict' to call your endpoint.
    api_url = "https://huggingface.co/spaces/pawanmall/superkart-frontend/predict" # <--- **IMPORTANT: Replace this**

    if "YOUR_HUGGING_FACE_BACKEND_SPACE_URL" in api_url:
        st.error("Please replace 'https://huggingface.co/spaces/pawanmall/superkart-frontend' with your actual backend Space URL.")
    else:
        try:
            # Send a POST request to the Flask API
            # Sending input_data as a list for robustness in the backend
            response = requests.post(api_url, json=input_data)
            response.raise_for_status() # Raise an exception for bad status codes (4xx or 5xx)

            # Get predictions from the response
            predictions = response.json().get("predictions")

            if predictions is not None and len(predictions) > 0:
                st.success(f"Predicted Sales Total: {predictions[0]:,.2f}")
            else:
                 st.error("API did not return valid predictions.")

        except requests.exceptions.RequestException as e:
            st.error(f"Error calling prediction API: {e}")
        except json.JSONDecodeError:
            st.error("Error decoding JSON response from API. Check backend logs for details.")
        except Exception as e:
            st.error(f"An unexpected error occurred: {e}")