Spaces:
Sleeping
Sleeping
File size: 4,363 Bytes
745aa9d 5d2a50d 745aa9d 07ef2c8 745aa9d 07ef2c8 745aa9d 07ef2c8 745aa9d 5d2a50d 745aa9d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
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.")
|