File size: 3,607 Bytes
374f2ba 8c05f1f 374f2ba 8c05f1f 374f2ba 8c05f1f 374f2ba 8c05f1f 374f2ba 8c05f1f 374f2ba 8c05f1f |
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 |
import gradio as gr
import pandas as pd
import numpy as np
import requests
def predict_sales(product_weight, product_sugar_content, product_allocated_area, product_type, product_mrp,
store_establishment_year, store_size, store_location_city_type, store_type):
# Create input dictionary
sample = {
'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
}
# Convert to DataFrame
features_df = pd.DataFrame([sample])
# Apply one-hot encoding for nominal columns (matching backend)
features_df = pd.get_dummies(features_df, columns=['Product_Type', 'Store_Type'], drop_first=True)
# Apply ordinal encoding (based on backend mappings)
sugar_mapping = {'No Sugar': 0, 'Low Sugar': 1, 'Regular': 2}
size_mapping = {'Small': 0, 'Medium': 1, 'High': 2}
city_mapping = {'Tier 3': 0, 'Tier 2': 1, 'Tier 1': 2}
features_df['Product_Sugar_Content'] = features_df['Product_Sugar_Content'].map(sugar_mapping)
features_df['Store_Size'] = features_df['Store_Size'].map(size_mapping)
features_df['Store_Location_City_Type'] = features_df['Store_Location_City_Type'].map(city_mapping)
# Call the backend API
backend_url = "https://Hugo014-TotalSalesPredictionBackend.hf.space/v1/sales"
try:
response = requests.post(backend_url, json=sample)
if response.status_code == 200:
result = response.json()
predicted_sales = result['Predicted Sales Total (in dollars)']
return f"The predicted sales total for the product is ${predicted_sales:.2f}."
else:
return f"Backend error: {response.status_code} - {response.text}"
except Exception as e:
return f"Error calling backend: {str(e)}"
# Gradio interface
demo = gr.Interface(
fn=predict_sales,
inputs=[
gr.Number(label="Product Weight", value=10.0, minimum=0.0, step=0.1),
gr.Dropdown(label="Product Sugar Content", choices=["No Sugar", "Low Sugar", "Regular"], value="Low Sugar"),
gr.Number(label="Product Allocated Area (sq ft)", value=500.0, minimum=0.0, step=1.0),
gr.Dropdown(label="Product Type", choices=[
"Dairy", "Soft Drinks", "Meat", "Fruits and Vegetables", "Snack Foods", "Household",
"Frozen Foods", "Baking Goods", "Canned", "Health and Hygiene", "Hard Drinks",
"Breads", "Starchy Foods", "Breakfast", "Seafood", "Others"
], value="Dairy"),
gr.Number(label="Product MRP (price)", value=100.0, minimum=0.0, step=1.0),
gr.Number(label="Store Establishment Year", value=2000, minimum=1900, maximum=2025, step=1),
gr.Dropdown(label="Store Size", choices=["Small", "Medium", "High"], value="Medium"),
gr.Dropdown(label="Store Location City Type", choices=["Tier 3", "Tier 2", "Tier 1"], value="Tier 1"),
gr.Dropdown(label="Store Type", choices=[
"Grocery Store", "Supermarket Type1", "Supermarket Type2", "Supermarket Type3"
], value="Supermarket Type1")
],
outputs=gr.Textbox(label="Prediction Result"),
title="Super Kart Product Sales Prediction App",
description="This tool predicts the total sales for a product based on store and product details."
)
demo.launch()
|