Here are some free inference AI models from huggingface and free APIs you can use for your game.
I’ve been doing research and chose some of the best and free models to create this open-source documentation for the community.
Please note You must get your bearer key from huggingface.
The bearer key is free and does not require payment information. Visit hugging face and click deploy->interference->get access token
This API gets summary of top result from wikipedia.
function SearchWikipedia(searchq)
local url = "https://en.wikipedia.org/w/rest.php/v1/search/page?q="
-- Define the query parameters
-- Make the request and get the response
local success, response = pcall(function()
return HttpService:RequestAsync({
Url = url..searchq
-- Method = "GET",
--Query = params -- Pass the query parameters as a table
})
end)
-- Check if the request was successful
if success then
-- Check if the response status code was 200 (OK)
if response.StatusCode == 200 then
-- Parse the response body as JSON
local data = HttpService:JSONDecode(response.Body)
local Filter=nil
-- Get the first item from the result list
local index=0
local item
repeat
index=index+1
item = data.pages[index]
Filter= chatmodule.SearchQuery(item.excerpt,badwords,true,true,false)
until Filter==nil or data.pages[index]==nil
-- Extract the title and text from the item
local title = item.title
local excerpt = item.excerpt
local pattern = "<span class=\"searchmatch\">(.-)</span>"
-- Replace the HTML tags with empty strings
local text = excerpt:gsub(pattern, "%1")
-- Print the title and text to the output
print(title)
print(text)
else
-- Print the response status code and status message to the output
print("Error: " .. response.StatusCode .. " " .. response.StatusMessage)
end
else
-- Print the error message to the output
print("Error: " .. response)
end
end
Search Wikipedia Article. I included my filtering module function to keep out dangerous results but it does not work in this context. Gets entire wikipedia article
function SearchWikipedia2(searchq)
local url = "https://en.wikipedia.org/w/rest.php/v1/search/page?q="
-- Define the query parameters
-- Make the request and get the response
local success, response = pcall(function()
return HttpService:RequestAsync({
Url = url..searchq
-- Method = "GET",
--Query = params -- Pass the query parameters as a table
})
end)
-- Check if the request was successful
if success then
-- Check if the response status code was 200 (OK)
if response.StatusCode == 200 then
-- Parse the response body as JSON
local data = HttpService:JSONDecode(response.Body)
-- Get the first item from the result list
local Filter=nil
-- Get the first item from the result list
local index=0
local item
repeat
index=index+1
item = data.pages[index]
Filter= chatmodule.SearchQuery(item.excerpt,badwords,true,true,false)
until Filter==nil or data.pages[index]==nil
-- Extract the title and text from the item
local title = item.title
local excerpt = item.excerpt
local pattern = "<span class=\"searchmatch\">(.-)</span>"
-- Replace the HTML tags with empty strings
local text = excerpt:gsub(pattern, "%1")
-- Print the title and text to the output
print(title)
print(text)
-- Extract the key from the item
local key = item.key
-- Construct the article URL from the key and the base URL
local base_url = "https://en.wikipedia.org/w/api.php?action=query&prop=extracts&exintro&explaintext&titles="
local article_url = base_url .. key.."&format=json"
-- Print the article URL to the output
--print(article_url)
-- Make another request to get the article content
local success, response = pcall(function()
return HttpService:RequestAsync({
Url = article_url,
Method = "GET"
})
end)
-- Check if the request was successful
if success then
-- Check if the response status code was 200 (OK)
if response.StatusCode == 200 then
-- Parse the response body as JSON
-- Access the extract property of the JSON object
local data = HttpService:JSONDecode(response.Body)
-- Access the pages table of the JSON object
local pages = data.query.pages
-- Use the pairs function to iterate over the pages table
for key, value in pairs(pages) do
-- Get the page key and the extract from each subtable
local page_key = key
local extract = value.extract
-- Print the page key and the extract to the output
--print(page_key)
return extract
-- print(extract)
end
print(data)
-- Print the extract to the output
--print(extract)
else
-- Print the response status code and status message to the output
print("Error: " .. response.StatusCode .. " " .. response.StatusMessage)
end
else
-- Print the error message to the output
print("Error: " .. response)
end
end
end
end
Summarize paragraph AI API.
function summarrization(inputq)
-- Get the HttpService
--https://huggingface.co/facebook/bart-large-cnn?
-- Get the HttpService
-- Define the URL and the headers for the request
local API_URL = "https://api-inference.huggingface.co/models/facebook/bart-large-cnn"
local headers = {
["Authorization"] = Bearerkey,
--["Content-Type"] = "application/json"
}
-- Define the payload for the request
local payload = {
inputs = inputq
}
-- Encode the payload as a JSON string
local payloadJSON = HttpService:JSONEncode(payload)
-- Send the request and get the response
local success, response = pcall(function()
return HttpService:RequestAsync({
Url = API_URL,
Method = "POST",
Headers = headers,
Body = payloadJSON
})
end)
-- Check if the request was successful
if success then
-- Decode the response as a JSON table
local responseJSON = HttpService:JSONDecode(response.Body)
print(response)
-- Check if the response has a summary
if responseJSON[1].summary_text then -- Use [1] to access the first element of the array
-- Print the summary
print(responseJSON[1].summary_text) -- Use [1] to access the first element of the array
return responseJSON[1].summary_text
else
-- Print an error message
print(response)
return inputq
end
else
-- Print an error message
print("Request failed: " .. response)
return inputq
end
end
Google Flan T5 Chatbot AI
function Instruct(inputText)
-- Import the HttpService
-- Define the API URL
local API_URL = "https://api-inference.huggingface.co/models/google/flan-t5-base"
-- Define the headers with your authorization key
local headers = {
["Authorization"] = Bearerkey
}
-- Define a function to query the API with a payload
local function query(payload)
-- Encode the payload as a JSON string
local jsonPayload = HttpService:JSONEncode(payload)
-- Send a POST request to the API URL with the headers and the payload
local response = HttpService:PostAsync(API_URL, jsonPayload, Enum.HttpContentType.ApplicationJson, false, headers)
-- Decode the response as a JSON table
local jsonResponse = HttpService:JSONDecode(response)
-- Return the JSON table
return jsonResponse
end
-- Define your input text
-- Query the API with your input text as the inputs field
local output = query({
["inputs"] = inputText
})
local generatedText = output[1].generated_text
-- or
local generatedText = output[1]["generated_text"]
return generatedText
-- Print the output
end
Basic GPT2 sentence completion API.
function GPT2(inputText)
-- Import the HttpService
-- Define the API URL
--local API_URL = "https://api-inference.huggingface.co/models/gpt2"
local API_URL ="https://api-inference.huggingface.co/models/gpt2-large"
-- Define the headers with your authorization key
local headers = {
["Authorization"] = Bearerkey
}
-- Define a function to query the API with a payload
local function query(payload)
-- Encode the payload as a JSON string
local jsonPayload = HttpService:JSONEncode(payload)
-- Send a POST request to the API URL with the headers and the payload
local response = HttpService:PostAsync(API_URL, jsonPayload, Enum.HttpContentType.ApplicationJson, false, headers)
-- Decode the response as a JSON table
local jsonResponse = HttpService:JSONDecode(response)
-- Return the JSON table
return jsonResponse
end
-- Define your input text
-- Query the API with your input text as the inputs field
local output = query({
["inputs"] = inputText,
["max_length"] = 200, -- This will limit the output to 150 tokens
["num_return_sequences"] =1, -- This will generate only one output
["temperature"] = 2-- This will increase the randomness of the output
})
-- or
local generatedText = output[1]["generated_text"]
print(output)
-- Print the string
return generatedText
-- Print the output
end
Conversational models input str,previous inputs, responses, and model 1 or 2
function conversationaldialogue(str,context,responses,model)
local model=model
local API_URL
if model==nil then
model=math.random(1,2)
end
if model==1 then
API_URL = "https://api-inference.huggingface.co/models/microsoft/GODEL-v1_1-base-seq2seq"
else
-- Define the URL and the headers for the request
API_URL = "https://api-inference.huggingface.co/models/facebook/blenderbot-400M-distill"
end
local headers = {
["Authorization"] = Bearerkey,
--["Content-Type"] = "application/json"
}
table.insert(context,str)
-- Define the payload for the request
local payload = {
inputs = {
past_user_inputs = context,
generated_responses = responses
}
}
-- Encode the payload as a JSON string
local payloadJSON = HttpService:JSONEncode(payload)
-- Send the request and get the response
local success, response = pcall(function()
return HttpService:RequestAsync({
Url = API_URL,
Method = "POST",
Headers = headers,
Body = payloadJSON
})
end)
-- Check if the request was successful
if success then
-- Decode the response as a JSON table
local responseJSON = HttpService:JSONDecode(response.Body)
-- Check if the response has a generated_text
if responseJSON.generated_text then
-- Print the generated_text
return (responseJSON.generated_text)
else
-- Print an error message
print(response)
return nil
end
else
-- Print an error message
print("Request failed: " .. response)
return nil
end
end
Philosophical quotes API. Maintains theme if the same input keyword.
function buildquotes(keyword)
-- Make the request and get the response
local success, response = pcall(function()
return HttpService:RequestAsync({
Url = url2..string.lower(keyword), -- Append the keyword to the URL
Method = "GET"
})
end)
-- Check if the request was successful
if success then
-- Check if the response status code was 200 (OK)
if response.StatusCode == 200 then
-- Parse the response body as JSON
local data = HttpService:JSONDecode(response.Body)
-- Get a random item from the result list
local item = data[math.random(#data)]
-- Extract the quote and author from the item
local quote = item.q
local author = item.a
if quote~="Too many requests. Obtain an auth key for unlimited access." then
-- Print the quote and author to the output
print(quote)
print("- " .. author)
return quote
end
end
else
-- Print the response status code and status message to the output
print("Error: " .. response.StatusCode .. " " .. response.StatusMessage)
return ""
end
else
-- Print the error message to the output
print("Error: " .. response)
return ""
end
end
Zero shot classification. input string and table of labels maximum of 10 labels={“greetings”,“question”}
function zero_shot_classification (input, labels)
-- Get the HttpService
-- Define the URL and the headers for the request
local API_URL = "https://api-inference.huggingface.co/models/sileod/deberta-v3-base-tasksource-nli"
local headers = {
["Authorization"] = Bearerkey, -- Your Hugging Face token
--["Content-Type"] = "application/json" -- The content type of the request
}
-- Define the payload for the request
local payload = {
inputs = input, -- The input text to classify
parameters = {
candidate_labels = labels -- The candidate labels to choose from
}
}
-- Encode the payload as a JSON string
local payloadJSON = HttpService:JSONEncode(payload)
-- Send the request and get the response
local success, response = pcall(function()
return HttpService:PostAsync(API_URL, payloadJSON, Enum.HttpContentType.ApplicationJson, false, headers) -- Post the JSON payload to the API URL with the headers
end)
-- Check if the request was successful
if success then
-- Decode the response as a JSON table
local responseJSON = HttpService:JSONDecode(response)
-- Print the scores and labels of the classification
print("Scores: " .. table.concat(responseJSON.scores, ", "))
print("Labels: " .. table.concat(responseJSON.labels, ", "))
return responseJSON.scores,responseJSON.labels
else
-- Print an error message
print("Request failed: " .. response)
end
end
function get_highest_classifier_value (input, labels)
-- Call the zero_shot_classification function to get the scores and labels
local scores, labels = zero_shot_classification (input, labels)
-- Initialize the variables to store the highest score and label
local highest_score = -math.huge -- A very small number
local highest_label = ""
-- Loop through the scores and labels arrays
for i = 1, #scores do
-- Check if the current score is higher than the highest score
if scores[i] > highest_score then
-- Update the highest score and label
highest_score = scores[i]
highest_label = labels[i]
end
end
-- Return the highest score and label
return highest_score, highest_label
end
Cheesy chuck norris jokes API.
function ExtractjokeTable()
local jokeURL = "https://geek-jokes.sameerkumar.website/api?format=json"
local response = HttpService:GetAsync(jokeURL)
local data =HttpService:JSONDecode(response)
if data.joke then
-- response successful
table.insert(jokes,data.joke)
return data.joke
end
end
Search Open library for a book
function search_book (query)
-- Get the HttpService
-- Define the URL and the parameter for the request
local API_URL = "https://openlibrary.org/search.json"
local param = "q=" .. HttpService:UrlEncode(query) -- Encode the query parameter
-- Send the request and get the response
local success, response = pcall(function()
return HttpService:GetAsync(API_URL .. "?" .. param) -- Append the parameter to the URL
end)
-- Check if the request was successful
if success then
-- Decode the response as a JSON table
local responseJSON = HttpService:JSONDecode(response)
-- Check if the response has any docs
if responseJSON.num_found > 0 then
-- Get the first doc from the docs array
local doc = responseJSON.docs[1]
-- Print some information about the doc
print("Title: " .. doc.title)
print("Author: " .. (doc.author_name and doc.author_name[1] or "Unknown"))
print("First published year: " .. (doc.first_publish_year or "Unknown"))
print("Open Library ID: " .. doc.key)
--print(response)
else
-- Print an error message
print("No results found for query: "..response)
end
else
-- Print an error message
print("Request failed: " .. response)
end
end
