app_streamlit.py

import streamlit as st
import requests
import json

# Define the URL of your Flask API (replace with your Hugging Face Space URL)
API_URL = "https://pkulkar-salesforcastbackend.hf.space/v1/sales" # Replace with your Hugging Face Space URL

st.title("SuperKart Sales Forecaster")
st.write("Enter the details of the product and store to get a sales forecast.")

# Create input fields for the user
product_weight = st.number_input("Product Weight", min_value=0.0, format="%f")
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, format="%f")
product_type = st.selectbox("Product Type", ['Dairy', 'Soft Drinks', 'Meat', 'Fruits and Vegetables', 'Household', 'Baking Goods', 'Snack Foods', 'Frozen Foods', 'Breakfast', 'Health and Hygiene', 'Hard Drinks', 'Canned', 'Bread', 'Starchy Foods', 'Others', 'Seafood'])
product_mrp = st.number_input("Product MRP", min_value=0.0, format="%f")
store_id = st.selectbox("Store ID", [f"Store_{i}" for i in range(1, 11)])
store_establishment_year = st.number_input("Store Establishment Year", min_value=1900, max_value=2024, step=1)
store_size = st.selectbox("Store Size", ['Medium', 'High', 'Low'])
store_location_city_type = st.selectbox("Store Location City Type", ['Tier 1', 'Tier 3', 'Tier 2'])
store_type = st.selectbox("Store Type", ['Supermarket Type 1', 'Supermarket Type 2', 'Departmental Store', 'Food Mart'])


if st.button("Predict Sales"):
    # Prepare the data to be sent to the API
    input_data = {
        '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,
    }

    # Send the data to the Flask API
    try:
        response = requests.post(API_URL, json=input_data)

        if response.status_code == 200:
            prediction = response.json()
            st.success(f"Predicted Sales: {prediction['Predicted Price (in dollars)']:.2f}")
        else:
            st.error(f"Error predicting sales: {response.status_code} - {response.text}")
    except requests.exceptions.RequestException as e:
        st.error(f"Error connecting to the API: {e}")

# Create a requirements.txt file for the Streamlit app
%%writefile /content/drive/MyDrive/deployment_files/requirements_streamlit.txt
streamlit==1.43.2
requests==2.32.3

# Upload the Streamlit app file and requirements file to Hugging Face Space
from huggingface_hub import upload_file

repo_id_frontend = "pkulkar/SalesForcasterFrontend" # Replace with your Hugging Face Space ID for the frontend

upload_file(
    path_or_fileobj="/content/drive/MyDrive/deployment_files/app_streamlit.py",
    path_in_repo="app.py",
    repo_id=repo_id_frontend,
    repo_type="space",
)

upload_file(
    path_or_fileobj="/content/drive/MyDrive/deployment_files/requirements_streamlit.txt",
    path_in_repo="requirements.txt",
    repo_id=repo_id_frontend,
    repo_type="space",
)

```