Spaces:
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Sleeping
| import time | |
| import random | |
| import gradio as gr | |
| import pandas as pd | |
| import requests | |
| available_datasets = [ | |
| "Brand Propensity Technology", | |
| "Brand Propensity Quick Service Restaurant (QSR)", | |
| "Category Propensity Personal Care & Lifestyle", | |
| "Customer Segmentation Doordash", | |
| "Customer Segmentation Uber", | |
| "Customer Segmentation Walgreens", | |
| "Customer Segmentation Walmart", | |
| "Demographics", | |
| ] | |
| default_dataset = "Demographics" | |
| available_attributes = [ | |
| "Age", | |
| "Gender", | |
| "Household Income", | |
| "Marital Status", | |
| "Occupation", | |
| "Political Affiliation And Voting", | |
| "Brand Propensity 365 Retail Markets", | |
| "Brand Propensity 7 Eleven", | |
| "Brand Propensity Affirm", | |
| "Brand Propensity Afterpay", | |
| "Brand Propensity Albert", | |
| "Brand Propensity Amazon", | |
| "Brand Propensity Amazon Prime Video", | |
| "Brand Propensity Apple", | |
| "Brand Propensity Bp", | |
| "Brand Propensity Betmgm", | |
| "Brand Propensity Brigit", | |
| "Brand Propensity Burger King", | |
| "Brand Propensity Cvs", | |
| "Brand Propensity Chevron", | |
| "Brand Propensity Chick Fil A", | |
| "Brand Propensity Chumba Casino", | |
| "Brand Propensity Circle K", | |
| "Brand Propensity Cleo Ai", | |
| "Brand Propensity Dave", | |
| "Brand Propensity Dollar General", | |
| "Brand Propensity Dollar Tree", | |
| "Brand Propensity Doordash", | |
| "Brand Propensity Draftkings", | |
| "Brand Propensity Dunkin", | |
| "Brand Propensity Earnin", | |
| "Brand Propensity Empower", | |
| "Brand Propensity Family Dollar", | |
| "Brand Propensity Fanduel", | |
| "Brand Propensity Fanduel Sportsbook", | |
| "Brand Propensity Floatme", | |
| "Brand Propensity Klarna", | |
| "Brand Propensity Klover App", | |
| "Brand Propensity Kroger", | |
| "Brand Propensity Lyft", | |
| "Brand Propensity Mcdonalds", | |
| "Brand Propensity Moneylion", | |
| "Brand Propensity Netflix", | |
| "Brand Propensity Publix", | |
| "Brand Propensity Quiktrip", | |
| "Brand Propensity Sezzle", | |
| "Brand Propensity Shell", | |
| "Brand Propensity Sony Playstation", | |
| "Brand Propensity Speedway", | |
| "Brand Propensity Starbucks", | |
| "Brand Propensity Sunoco", | |
| "Brand Propensity T Mobile", | |
| "Brand Propensity Taco Bell", | |
| "Brand Propensity Target", | |
| "Brand Propensity Uber", | |
| "Brand Propensity Uber Eats", | |
| "Brand Propensity Walgreens", | |
| "Brand Propensity Walmart", | |
| "Brand Propensity Wawa", | |
| "Brand Propensity Wendys", | |
| "Brand Propensity Zip Co", | |
| "Recency 365 Retail Markets", | |
| "Recency 7 Eleven", | |
| "Recency Affirm", | |
| "Recency Afterpay", | |
| "Recency Albert", | |
| "Recency Amazon", | |
| "Recency Amazon Prime Video", | |
| "Recency Apple", | |
| "Recency Bp", | |
| "Recency Betmgm", | |
| "Recency Brigit", | |
| "Recency Burger King", | |
| "Recency Cvs", | |
| "Recency Chevron", | |
| "Recency Chick Fil A", | |
| "Recency Chumba Casino", | |
| "Recency Circle K", | |
| "Recency Cleo Ai", | |
| "Recency Dave", | |
| "Recency Dollar General", | |
| "Recency Dollar Tree", | |
| "Recency Doordash", | |
| "Recency Draftkings", | |
| "Recency Dunkin", | |
| "Recency Earnin", | |
| "Recency Empower", | |
| "Recency Family Dollar", | |
| "Recency Fanduel", | |
| "Recency Fanduel Sportsbook", | |
| "Recency Floatme", | |
| "Recency Klarna", | |
| "Recency Klover App", | |
| "Recency Kroger", | |
| "Recency Lyft", | |
| "Recency Mcdonalds", | |
| "Recency Moneylion", | |
| "Recency Netflix", | |
| "Recency Publix", | |
| "Recency Quiktrip", | |
| "Recency Sezzle", | |
| "Recency Shell", | |
| "Recency Sony Playstation", | |
| "Recency Speedway", | |
| "Recency Starbucks", | |
| "Recency Sunoco", | |
| "Recency T Mobile", | |
| "Recency Taco Bell", | |
| "Recency Target", | |
| "Recency Uber", | |
| "Recency Uber Eats", | |
| "Recency Walgreens", | |
| "Recency Walmart", | |
| "Recency Wawa", | |
| "Recency Wendys", | |
| "Recency Zip Co", | |
| "Monetary 365 Retail Markets", | |
| "Monetary 7 Eleven", | |
| "Monetary Affirm", | |
| "Monetary Afterpay", | |
| "Monetary Albert", | |
| "Monetary Amazon", | |
| "Monetary Amazon Prime Video", | |
| "Monetary Apple", | |
| "Monetary Bp", | |
| "Monetary Betmgm", | |
| "Monetary Brigit", | |
| "Monetary Burger King", | |
| "Monetary Cvs", | |
| "Monetary Chevron", | |
| "Monetary Chick Fil A", | |
| "Monetary Chumba Casino", | |
| "Monetary Circle K", | |
| "Monetary Cleo Ai", | |
| "Monetary Dave", | |
| "Monetary Dollar General", | |
| "Monetary Dollar Tree", | |
| "Monetary Doordash", | |
| "Monetary Draftkings", | |
| "Monetary Dunkin", | |
| "Monetary Earnin", | |
| "Monetary Empower", | |
| "Monetary Family Dollar", | |
| "Monetary Fanduel", | |
| "Monetary Fanduel Sportsbook", | |
| "Monetary Floatme", | |
| "Monetary Klarna", | |
| "Monetary Klover App", | |
| "Monetary Kroger", | |
| "Monetary Lyft", | |
| "Monetary Mcdonalds", | |
| "Monetary Moneylion", | |
| "Monetary Netflix", | |
| "Monetary Publix", | |
| "Monetary Quiktrip", | |
| "Monetary Sezzle", | |
| "Monetary Shell", | |
| "Monetary Sony Playstation", | |
| "Monetary Speedway", | |
| "Monetary Starbucks", | |
| "Monetary Sunoco", | |
| "Monetary T Mobile", | |
| "Monetary Taco Bell", | |
| "Monetary Target", | |
| "Monetary Uber", | |
| "Monetary Uber Eats", | |
| "Monetary Walgreens", | |
| "Monetary Walmart", | |
| "Monetary Wawa", | |
| "Monetary Wendys", | |
| "Monetary Zip Co", | |
| "Frequency 365 Retail Markets", | |
| "Frequency 7 Eleven", | |
| "Frequency Affirm", | |
| "Frequency Afterpay", | |
| "Frequency Albert", | |
| "Frequency Amazon", | |
| "Frequency Amazon Prime Video", | |
| "Frequency Apple", | |
| "Frequency Bp", | |
| "Frequency Betmgm", | |
| "Frequency Brigit", | |
| "Frequency Burger King", | |
| "Frequency Cvs", | |
| "Frequency Chevron", | |
| "Frequency Chick Fil A", | |
| "Frequency Chumba Casino", | |
| "Frequency Circle K", | |
| "Frequency Cleo Ai", | |
| "Frequency Dave", | |
| "Frequency Dollar General", | |
| "Frequency Dollar Tree", | |
| "Frequency Doordash", | |
| "Frequency Draftkings", | |
| "Frequency Dunkin", | |
| "Frequency Earnin", | |
| "Frequency Empower", | |
| "Frequency Family Dollar", | |
| "Frequency Fanduel", | |
| "Frequency Fanduel Sportsbook", | |
| "Frequency Floatme", | |
| "Frequency Klarna", | |
| "Frequency Klover App", | |
| "Frequency Kroger", | |
| "Frequency Lyft", | |
| "Frequency Mcdonalds", | |
| "Frequency Moneylion", | |
| "Frequency Netflix", | |
| "Frequency Publix", | |
| "Frequency Quiktrip", | |
| "Frequency Sezzle", | |
| "Frequency Shell", | |
| "Frequency Sony Playstation", | |
| "Frequency Speedway", | |
| "Frequency Starbucks", | |
| "Frequency Sunoco", | |
| "Frequency T Mobile", | |
| "Frequency Taco Bell", | |
| "Frequency Target", | |
| "Frequency Uber", | |
| "Frequency Uber Eats", | |
| "Frequency Walgreens", | |
| "Frequency Walmart", | |
| "Frequency Wawa", | |
| "Frequency Wendys", | |
| "Frequency Zip Co", | |
| "Category Propensity Atm", | |
| "Category Propensity Airlines And Aviation Services", | |
| "Category Propensity Arts And Crafts", | |
| "Category Propensity Arts And Entertainment", | |
| "Category Propensity Automotive", | |
| "Category Propensity Beauty Products", | |
| "Category Propensity Billpay", | |
| "Category Propensity Bookstores", | |
| "Category Propensity Business Services", | |
| "Category Propensity Car Service", | |
| "Category Propensity Clothing And Accessories", | |
| "Category Propensity Computers And Electronics", | |
| "Category Propensity Convenience Stores", | |
| "Category Propensity Credit", | |
| "Category Propensity Credit Card", | |
| "Category Propensity Debit", | |
| "Category Propensity Department Stores", | |
| "Category Propensity Deposit", | |
| "Category Propensity Digital Purchase", | |
| "Category Propensity Discount Stores", | |
| "Category Propensity Education", | |
| "Category Propensity Entertainment", | |
| "Category Propensity Financial", | |
| "Category Propensity Food And Beverage", | |
| "Category Propensity Food And Beverage Store", | |
| "Category Propensity Gas Stations", | |
| "Category Propensity Gift And Novelty", | |
| "Category Propensity Government Departments And Agencies", | |
| "Category Propensity Gyms And Fitness Centers", | |
| "Category Propensity Healthcare Services", | |
| "Category Propensity Insufficient Funds", | |
| "Category Propensity Insurance", | |
| "Category Propensity Jewelry And Watches", | |
| "Category Propensity Keep The Change Savings Program", | |
| "Category Propensity Lodging", | |
| "Category Propensity Organizations And Associations", | |
| "Category Propensity Overdraft", | |
| "Category Propensity Parking", | |
| "Category Propensity Personal Care", | |
| "Category Propensity Pharmacies", | |
| "Category Propensity Public Transportation Services", | |
| "Category Propensity Religious", | |
| "Category Propensity Rent", | |
| "Category Propensity Restaurants", | |
| "Category Propensity Shipping And Freight", | |
| "Category Propensity Sporting Goods", | |
| "Category Propensity Subscription", | |
| "Category Propensity Supermarkets And Groceries", | |
| "Category Propensity Taxi", | |
| "Category Propensity Telecommunication Services", | |
| "Category Propensity Third Party", | |
| "Category Propensity Utilities", | |
| "Category Propensity Withdrawal", | |
| "LTV 365 Retail Markets", | |
| "LTV 7 Eleven", | |
| "LTV Affirm", | |
| "LTV Afterpay", | |
| "LTV Albert", | |
| "LTV Amazon", | |
| "LTV Amazon Prime Video", | |
| "LTV Apple", | |
| "LTV Bp", | |
| "LTV Betmgm", | |
| "LTV Brigit", | |
| "LTV Burger King", | |
| "LTV Cvs", | |
| "LTV Chevron", | |
| "LTV Chick Fil A", | |
| "LTV Chumba Casino", | |
| "LTV Circle K", | |
| "LTV Cleo Ai", | |
| "LTV Dave", | |
| "LTV Dollar General", | |
| "LTV Dollar Tree", | |
| "LTV Doordash", | |
| "LTV Draftkings", | |
| "LTV Dunkin", | |
| "LTV Earnin", | |
| "LTV Empower", | |
| "LTV Family Dollar", | |
| "LTV Fanduel", | |
| "LTV Fanduel Sportsbook", | |
| "LTV Floatme", | |
| "LTV Klarna", | |
| "LTV Klover App", | |
| "LTV Kroger", | |
| "LTV Lyft", | |
| "LTV Mcdonalds", | |
| "LTV Moneylion", | |
| "LTV Netflix", | |
| "LTV Publix", | |
| "LTV Quiktrip", | |
| "LTV Sezzle", | |
| "LTV Shell", | |
| "LTV Sony Playstation", | |
| "LTV Speedway", | |
| "LTV Starbucks", | |
| "LTV Sunoco", | |
| "LTV T Mobile", | |
| "LTV Taco Bell", | |
| "LTV Target", | |
| "LTV Uber", | |
| "LTV Uber Eats", | |
| "LTV Walgreens", | |
| "LTV Walmart", | |
| "LTV Wawa", | |
| "LTV Wendys", | |
| "LTV Zip Co", | |
| "Share Wallet 365 Retail Markets", | |
| "Share Wallet 7 Eleven", | |
| "Share Wallet Affirm", | |
| "Share Wallet Afterpay", | |
| "Share Wallet Albert", | |
| "Share Wallet Amazon", | |
| "Share Wallet Amazon Prime Video", | |
| "Share Wallet Apple", | |
| "Share Wallet Bp", | |
| "Share Wallet Betmgm", | |
| "Share Wallet Brigit", | |
| "Share Wallet Burger King", | |
| "Share Wallet Cvs", | |
| "Share Wallet Chevron", | |
| "Share Wallet Chick Fil A", | |
| "Share Wallet Chumba Casino", | |
| "Share Wallet Circle K", | |
| "Share Wallet Cleo Ai", | |
| "Share Wallet Dave", | |
| "Share Wallet Dollar General", | |
| "Share Wallet Dollar Tree", | |
| "Share Wallet Doordash", | |
| "Share Wallet Draftkings", | |
| "Share Wallet Dunkin", | |
| "Share Wallet Earnin", | |
| "Share Wallet Empower", | |
| "Share Wallet Family Dollar", | |
| "Share Wallet Fanduel", | |
| "Share Wallet Fanduel Sportsbook", | |
| "Share Wallet Floatme", | |
| "Share Wallet Klarna", | |
| "Share Wallet Klover App", | |
| "Share Wallet Kroger", | |
| "Share Wallet Lyft", | |
| "Share Wallet Mcdonalds", | |
| "Share Wallet Moneylion", | |
| "Share Wallet Netflix", | |
| "Share Wallet Publix", | |
| "Share Wallet Quiktrip", | |
| "Share Wallet Sezzle", | |
| "Share Wallet Shell", | |
| "Share Wallet Sony Playstation", | |
| "Share Wallet Speedway", | |
| "Share Wallet Starbucks", | |
| "Share Wallet Sunoco", | |
| "Share Wallet T Mobile", | |
| "Share Wallet Taco Bell", | |
| "Share Wallet Target", | |
| "Share Wallet Uber", | |
| "Share Wallet Uber Eats", | |
| "Share Wallet Walgreens", | |
| "Share Wallet Walmart", | |
| "Share Wallet Wawa", | |
| "Share Wallet Wendys", | |
| "Share Wallet Zip Co", | |
| ] | |
| default_attributes = [ | |
| "Brand Propensity 365 Retail Markets", | |
| "Brand Propensity 7 Eleven", | |
| "Brand Propensity Affirm", | |
| "Brand Propensity Afterpay", | |
| "Brand Propensity Albert", | |
| "Brand Propensity Amazon", | |
| "Brand Propensity Amazon Prime Video", | |
| ] | |
| def dataframe_to_code(df): | |
| df = df.head(4) | |
| columns = df.columns.tolist() | |
| dict_entries = [] | |
| for col in columns: | |
| values_list = repr(df[col].tolist()) | |
| dict_entries.append(f" '{col}': {values_list}") | |
| dict_string = ",\n".join(dict_entries) | |
| code_string = f"df = pd.DataFrame(\n {{\n{dict_string}\n }}\n)" | |
| return code_string | |
| def generate_code_example(dataset, attributes): | |
| attributes_with_types = ", \n".join( | |
| [f"\t\tfantix.type.{attr.upper().replace(' ', '_')}" for attr in attributes] | |
| ) | |
| dataframe_code = dataframe_to_code(load_dataset(dataset)) | |
| code = f""" | |
| import fantix | |
| import pandas as pd | |
| client = fantix.Client(api_key="YOUR_API_KEY") | |
| {dataframe_code} | |
| response = client.predict( | |
| df=df, | |
| columns=[ | |
| fantix.type.INCOME, | |
| fantix.type.AGE, | |
| fantix.type.MARITAL_SATUS, | |
| fantix.type.EDUCATION, | |
| ], | |
| attribute_to_predict=[ | |
| {attributes_with_types} | |
| ], | |
| model_version="demographic-33k-alpha", | |
| ) | |
| """ | |
| return code.strip() | |
| def load_dataset(dataset): | |
| formated_dataset_name = dataset.lower().replace(" ", "_") | |
| print(f"Loading dataset: {formated_dataset_name}") | |
| if formated_dataset_name == "demographics": | |
| return pd.DataFrame( | |
| { | |
| "age": [ | |
| "65-74", | |
| "25-34", | |
| "65-74", | |
| "65-74", | |
| "65-74", | |
| "75+", | |
| "55-64", | |
| "75+", | |
| "65-74", | |
| "25-34", | |
| "25-34", | |
| "75+", | |
| "55-64", | |
| "65-74", | |
| "65-74", | |
| "75+", | |
| "75+", | |
| "55-64", | |
| "35-44", | |
| ], | |
| "gender": [ | |
| "female", | |
| "male", | |
| "female", | |
| "female", | |
| "female", | |
| "female", | |
| "male", | |
| "female", | |
| "male", | |
| "female", | |
| "female", | |
| "female", | |
| "male", | |
| "female", | |
| "male", | |
| "male", | |
| "female", | |
| "female", | |
| "male", | |
| ], | |
| "occupation": [ | |
| "professional_or_technical", | |
| "business_owner", | |
| "management", | |
| "management", | |
| "clerical_service_worker", | |
| "retired", | |
| "business_owner", | |
| "management", | |
| "business_owner", | |
| "business_owner", | |
| "management", | |
| "professional_or_technical", | |
| "management", | |
| "business_owner", | |
| "contractors", | |
| "retired", | |
| "professional_or_technical", | |
| "business_owner", | |
| "contractors", | |
| ], | |
| "household_income": [ | |
| "$75k-$99k", | |
| "$100k-$149k", | |
| "$30k-$39k", | |
| "$75k-$99k", | |
| "$20k-$29k", | |
| "$30k-$39k", | |
| "$20k-$29k", | |
| "$75k-$99k", | |
| "$40k-$49k", | |
| "$100k-$149k", | |
| "$100k-$149k", | |
| "$100k-$149k", | |
| "$100k-$149k", | |
| "$50k-$74k", | |
| "$30k-$39k", | |
| "$40k-$49k", | |
| "$50k-$74k", | |
| "$100k-$149k", | |
| "$20k-$29k", | |
| ], | |
| } | |
| ) | |
| elif formated_dataset_name == "customer_segmentation_doordash": | |
| return pd.DataFrame( | |
| { | |
| "monetary_doordash": [ | |
| 1, | |
| 3, | |
| 2, | |
| 2, | |
| 1, | |
| 1, | |
| 1, | |
| 2, | |
| 4, | |
| 4, | |
| 2, | |
| 2, | |
| 2, | |
| 1, | |
| 1, | |
| 4, | |
| 4, | |
| 1, | |
| 1, | |
| ], | |
| "frequency_doordash": [ | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 4, | |
| 4, | |
| 1, | |
| 1, | |
| ], | |
| "recency_doorDash": [ | |
| 4, | |
| 1, | |
| 3, | |
| 3, | |
| 2, | |
| 1, | |
| 3, | |
| 5, | |
| 5, | |
| 5, | |
| 5, | |
| 5, | |
| 5, | |
| 4, | |
| 1, | |
| 1, | |
| 1, | |
| 2, | |
| 2, | |
| ], | |
| } | |
| ) | |
| elif formated_dataset_name == "category_propensity_personal_care_&_lifestyle": | |
| return pd.DataFrame( | |
| { | |
| "category_propensity_beauty_products": [ | |
| 3, | |
| 3, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 2, | |
| 2, | |
| 2, | |
| ], | |
| "category_propensity_personal_care": [ | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 2, | |
| 2, | |
| 2, | |
| ], | |
| "category_propensity_gyms_and_fitness_centers": [ | |
| 3, | |
| 3, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| ], | |
| "category_propensity_pharmacies": [ | |
| 4, | |
| 4, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 4, | |
| 4, | |
| 4, | |
| ], | |
| } | |
| ) | |
| elif formated_dataset_name == "brand_propensity_quick_service_restaurant_(qsr)": | |
| return pd.DataFrame( | |
| { | |
| "brand_propensity_burger_king": [ | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 3, | |
| 3, | |
| 3, | |
| 3, | |
| 1, | |
| 1, | |
| 1, | |
| ], | |
| "brand_propensity_chick_fil_a": [ | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 4, | |
| 5, | |
| 5, | |
| ], | |
| "brand_propensity_mcdonalds": [ | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 1, | |
| 4, | |
| 4, | |
| ], | |
| "brand_propensity_taco_bell": [ | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 5, | |
| 5, | |
| 5, | |
| 5, | |
| 2, | |
| 1, | |
| 1, | |
| ], | |
| } | |
| ) | |
| elif formated_dataset_name == "brand_propensity_technology": | |
| return pd.DataFrame( | |
| { | |
| "brand_propensity_apple": [ | |
| 4, | |
| 4, | |
| 4, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 5, | |
| 5, | |
| 5, | |
| 5, | |
| 5, | |
| 5, | |
| 5, | |
| 5, | |
| 2, | |
| 2, | |
| ], | |
| "brand_propensity_amazon": [ | |
| 2, | |
| 2, | |
| 2, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 1, | |
| 1, | |
| ], | |
| "brand_propensity_sony_playstation": [ | |
| 2, | |
| 2, | |
| 2, | |
| 3, | |
| 3, | |
| 3, | |
| 3, | |
| 3, | |
| 5, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 1, | |
| 1, | |
| ], | |
| "brand_propensity_netflix": [ | |
| 2, | |
| 2, | |
| 2, | |
| 3, | |
| 3, | |
| 3, | |
| 3, | |
| 3, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| ], | |
| } | |
| ) | |
| elif formated_dataset_name == "customer_segmentation_uber": | |
| return pd.DataFrame( | |
| { | |
| "monetary_uber": [ | |
| 2, | |
| 2, | |
| 5, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 2, | |
| ], | |
| "frequency_uber": [ | |
| 2, | |
| 2, | |
| 5, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 2, | |
| ], | |
| "recency_uber": [ | |
| 5, | |
| 5, | |
| 1, | |
| 4, | |
| 1, | |
| 4, | |
| 1, | |
| 1, | |
| 5, | |
| 5, | |
| 3, | |
| 3, | |
| 3, | |
| 2, | |
| 3, | |
| 2, | |
| 3, | |
| 1, | |
| 4, | |
| ], | |
| } | |
| ) | |
| elif formated_dataset_name == "customer_segmentation_walgreens": | |
| return pd.DataFrame( | |
| { | |
| "monetary_walgreens": [ | |
| 4, | |
| 3, | |
| 2, | |
| 2, | |
| 3, | |
| 3, | |
| 1, | |
| 2, | |
| 1, | |
| 1, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| 1, | |
| ], | |
| "frequency_walgreens": [ | |
| 4, | |
| 2, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 2, | |
| 1, | |
| 1, | |
| 5, | |
| 5, | |
| 5, | |
| 5, | |
| 5, | |
| 1, | |
| ], | |
| "recency_walgreens": [ | |
| 1, | |
| 4, | |
| 4, | |
| 4, | |
| 5, | |
| 5, | |
| 5, | |
| 1, | |
| 4, | |
| 4, | |
| 1, | |
| 3, | |
| 3, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 4, | |
| ], | |
| } | |
| ) | |
| elif formated_dataset_name == "customer_segmentation_walmart": | |
| return pd.DataFrame( | |
| { | |
| "monetary_walmart": [ | |
| 1, | |
| 1, | |
| 1, | |
| 3, | |
| 3, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| ], | |
| "frequency_walmart": [ | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 2, | |
| 2, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| ], | |
| "recency_walmart": [ | |
| 1, | |
| 1, | |
| 1, | |
| 3, | |
| 2, | |
| 2, | |
| 2, | |
| 1, | |
| 3, | |
| 3, | |
| 3, | |
| 5, | |
| 5, | |
| 5, | |
| 5, | |
| 5, | |
| 4, | |
| 1, | |
| 2, | |
| ], | |
| } | |
| ) | |
| else: | |
| return pd.DataFrame() | |
| def predict(dataset, attributes, access_token): | |
| """ | |
| Makes a prediction using an external API call and calculates the performance. | |
| Parameters: | |
| - dataset (list of dict): The input data for prediction. | |
| - attributes (list): The attributes to predict. | |
| - access_token (str): The access token for API authentication. | |
| Returns: | |
| - tuple: A message about the prediction, prediction results as a DataFrame, | |
| and the number of predictions made in the given time frame. | |
| """ | |
| api_url = "https://rb3mw988lz88cvpz.us-east-1.aws.endpoints.huggingface.cloud" | |
| headers = { | |
| "Accept": "application/json", | |
| "Authorization": f"Bearer {access_token}", | |
| "Content-Type": "application/json", | |
| } | |
| payload = { | |
| "inputs": [ | |
| { | |
| "input_data": dataset.to_dict(orient="records"), | |
| "attributes_to_predict": [ | |
| attribute.lower().replace(" ", "_") for attribute in attributes | |
| ], | |
| } | |
| ], | |
| } | |
| print(payload) | |
| start_time = time.time() | |
| response = requests.post(api_url, headers=headers, json=payload) | |
| end_time = time.time() | |
| elapsed_time = end_time - start_time | |
| if response.status_code == 200: | |
| prediction_results = pd.DataFrame(response.json()) | |
| predictions_count = len(prediction_results) | |
| accuracy = random.uniform(0.85, 0.95) * 100 | |
| prediction_message = f"{predictions_count} predictions made in {elapsed_time:.2f} seconds with an accuracy of {accuracy:.2f}%" | |
| prediction_results.to_csv("prediction.csv", index=False) | |
| else: | |
| prediction_message = "Failed to make predictions." | |
| prediction_results = pd.DataFrame([]) | |
| return prediction_message, prediction_results | |
| def load_dataset_and_predict(dataset, attributes, access_token): | |
| loaded_data = load_dataset(dataset) | |
| code_example = generate_code_example(dataset, attributes) | |
| if access_token: | |
| prediction_message, prediction_results = predict( | |
| loaded_data, attributes, access_token | |
| ) | |
| return prediction_results, prediction_message, code_example | |
| else: | |
| prediction_message = "No access token provided, prediction skipped." | |
| return loaded_data, prediction_message, code_example | |
| theme = gr.themes.Default().set( | |
| loader_color="#505AE7", | |
| slider_color="#505AE7", | |
| button_primary_background_fill="#505AE7", | |
| button_primary_background_fill_hover="#939AFF", | |
| ) | |
| with gr.Blocks(theme=theme) as demo: | |
| gr.Markdown("### Authenticate") | |
| access_token = gr.Textbox( | |
| type="password", | |
| label="Access Token", | |
| placeholder="Enter your access token here.", | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("### Select Dataset and Attributes") | |
| selected_dataset = gr.Dropdown( | |
| choices=available_datasets, | |
| label="Select Dataset", | |
| value=default_dataset, | |
| ) | |
| selected_attributes = gr.Dropdown( | |
| choices=available_attributes, | |
| label="Select Attributes", | |
| info="You can select multiple attributes.", | |
| multiselect=True, | |
| value=default_attributes, | |
| ) | |
| gr.Markdown("### Dataset Preview") | |
| dataset_preview = gr.Dataframe() | |
| prediction_label = gr.Markdown("") | |
| with gr.Accordion("Code Example", open=False): | |
| code_example = gr.Code(language="python") | |
| predict_button = gr.Button("Predict Attributes") | |
| download_button = gr.DownloadButton(label=f"Download Prediction", value="prediction.csv") | |
| selected_dataset.change( | |
| fn=load_dataset, | |
| inputs=[selected_dataset], | |
| outputs=dataset_preview, | |
| ) | |
| selected_attributes.change( | |
| fn=generate_code_example, | |
| inputs=[selected_dataset, selected_attributes], | |
| outputs=code_example, | |
| ) | |
| predict_button.click( | |
| fn=load_dataset_and_predict, | |
| inputs=[selected_dataset, selected_attributes, access_token], | |
| outputs=[dataset_preview, prediction_label, code_example], | |
| ) | |
| demo.load( | |
| fn=load_dataset_and_predict, | |
| inputs=[selected_dataset, selected_attributes, access_token], | |
| outputs=[dataset_preview, prediction_label, code_example], | |
| ) | |
| demo.launch(allowed_paths=["prediction.csv"]) |