app.py

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 store and product

Product_Weight = st.number_input("Product_Weight", min_value=0.1, max_value=100.0, value=90.0)
Product_Sugar_Content = st.selectbox("Product_Sugar_Content", ["Low Sugar", "No Sugar", "Regular"])
Product_Allocated_Area = st.number_input("Product_Allocated_Area", min_value=0.001, max_value=1.0, value=0.045, step=0.001)
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=10.0, max_value=500.0, value=150.75)
Store_Establishment_Year = st.number_input("Store_Establishment_Year", min_value=1980, max_value=2025, step=1, value=2009)
Store_Size = st.selectbox("Store_Size", ["High", "Medium", "Small"])
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://nishantpathak461-Backend_Stores.hf.space/v1/sales", json=input_data.to_dict(orient='records')[0])

        if response.status_code == 200:
            prediction = response.json()['predicted_sales']
            st.metric(f"Predicted Sales", f"₹{prediction:.2f}")
        else:
            st.error("Error in API request")



# 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://<jyotisharma/storesalesfrontend>.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.")