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import os | |
secret = os.getenv("VerySecret") | |
print(secret) | |
# from pathlib import Path | |
# import gradio as gr | |
# #import openai | |
# import os | |
# import tiktoken | |
# from openai import OpenAI | |
# # Set secret key | |
# #HF_TOKEN = os.getenv("NextStar") | |
# # Set client and secret key | |
# client = OpenAI(api_key=os.getenv("NextStar")) | |
# #Set prompt engineering paths (so globally available) | |
# inStructionPath = "intro_instructions_combine.txt" | |
# inRulesPath = "formatting_rules_expanded.txt" | |
# inExamplesPath = "examples_longer1.txt" | |
# inDialoguesPath = "examples_dialogues.txt" | |
# #Set to read in prompting files | |
# def openReadFiles(inpath): | |
# infile = Path (inpath) | |
# with open(infile) as f: | |
# data = f.read() | |
# return data | |
# # Set up prompting data (so globally available) | |
# instruct = openReadFiles(inStructionPath) | |
# rules = openReadFiles(inRulesPath) | |
# examples = openReadFiles(inExamplesPath) | |
# exampleDialogues = openReadFiles(inDialoguesPath) | |
# def formatQuery(engText): | |
# """Add prompt instructions to English text for GPT4""" | |
# instruct = "Now, translate the following sentences to perfect ASL gloss using the grammatical, syntactic, and notation rules you just learned. \n\n" | |
# query = instruct+engText | |
# return query | |
# def num_tokens_from_string(string: str, encoding_name: str) -> int: | |
# """Returns the number of tokens in a text string.""" | |
# encoding = tiktoken.get_encoding(encoding_name) | |
# num_tokens = len(encoding.encode(string)) | |
# return num_tokens | |
# def checkTokens(tokens): | |
# """Checks tokens to ensrue we can translate to ASL gloss""" | |
# goAhead = None | |
# if tokens >= 553: | |
# print(f"Cannot translate to ASL gloss at this time: too many tokens ({tokens})") | |
# goAhead = False | |
# else: | |
# goAhead = True | |
# print(f"Number of tokens is acceptable: can continue translating") | |
# return goAhead | |
# def getGlossFromText(query): | |
# """Sets all for getting ASL gloss""" | |
# text = formatQuery(query) | |
# tokens = num_tokens_from_string(text, "cl100k_base") | |
# goAhead = checkTokens(tokens) | |
# if goAhead == True: | |
# results = getASLGloss(text) | |
# else: | |
# results = "Too many tokens: cannot translate" | |
# return results | |
# def getASLGloss(testQs): | |
# """Get ASL gloss from OpenAI using our prompt engineering""" | |
# #openai.api_key = HF_TOKENS | |
# completion = client.chat.completions.create( | |
# model = 'gpt-4-0125-preview', | |
# messages = [ | |
# {"role": "system", "content": instruct}, | |
# {"role": "system", "content": rules}, | |
# {"role": "system", "content": examples}, | |
# {"role": "system", "content": exampleDialogues}, | |
# {"role": "user", "content": testQs}, | |
# ], | |
# temperature = 0 | |
# ) | |
# #results = completion['choices'][0]['message']['content'] | |
# results = completion.choices[0].message.content | |
# return results | |
# def main(): | |
# title = "English to ASL Gloss" | |
# #description = """Translate English text to ASL Gloss""" | |
# description = "This program uses GPT4 alongside prompt engineering to \ | |
# translate English text to ASL gloss.\n \ | |
# <b>Type in the English sentence you would like to translate into ASL Gloss.</b> \ | |
# \n \n This program was last updated on February 27, 2024, and uses GPT4-Turbo (0125 preview version) \ | |
# \n\n \ | |
# \n \n This version of EngToASLGloss contains superscript notation which adds \ | |
# grammatical context to assist in ASL generation. \ | |
# \n Below are the guidelines we are using to express grammatical concepts \ | |
# in ASL gloss.\ | |
# Anything within the angle brackets < > indicates this additional grammatical notation.\ | |
# If the angle brackets are directly next to a word, the notation inside \ | |
# the angle brackets is associate with just that word, e.g. WILL < A >. \ | |
# If the angle brackets are next to a whitespace after a word,\ | |
# the notation inside the angle bracket is associated with all of the words\ | |
# before it, up until a comma, another angle bracket, or a double space.\ | |
# \n \n This sentence is an example of this rule:\ | |
# \n NEXT-YEAR < Ti >, MY FIANCE < T >, TWO-OF-US MARRY \< A \>.\ | |
# \n\r \ | |
# \n The superscript notation options that will appear in results are as follows:\ | |
# \n Ti marks time\ | |
# \n T marks topic\ | |
# \n A marks comment\ | |
# \n Y/N marks yes-no question\ | |
# \n WHQ marks wh-question\ | |
# \n RHQ marks rhetorical question\ | |
# \n < Cond > marks conditional sentences\ | |
# \n lower case marks directional verbs\ | |
# \n ++ marks emphesis ('very' or 'a lot of')\ | |
# \n \# marks lexical fingerspelling \ | |
# \n \- marks space between individual letters of fingerspelling\ | |
# \n \n <b>Note: This is a prototype and is still in development. \ | |
# Do not use it in a production deployment.</b> \ | |
# \n For additional details on how the program works, please see \ | |
# [the README](https://huggingface.co/spaces/rrakov/EngTexToASLGloss/blob/main/README.md)" | |
# interface = gr.Interface( | |
# fn=getGlossFromText, | |
# inputs="textbox", | |
# outputs="text", | |
# title = title, | |
# description = description) | |
# #examples = [[("Prompt: Every year I buy my dad a gift \n", "Result: EVERY-YEAR<Ti>, MY DAD GIFT<T>, ME BUY<A>")]]) | |
# # examples=[["Every year I buy my dad a gift"], ["I always look forward to the family vacation"], | |
# # ["If I don't travel often, I am sad."]]) | |
# interface.launch() | |
# if __name__ == "__main__": | |
# main() | |
# # def getAnswer(query, texts = texts, embeddings = embeddings): | |
# # docsearch = FAISS.from_texts(texts, embeddings) | |
# # docs = docsearch.similarity_search(query) | |
# # chain = load_qa_chain(OpenAI(openai_api_key = HF_TOKEN, temperature=0), chain_type="map_reduce", return_map_steps=False) | |
# # response = chain({"input_documents": docs, "question": query}, return_only_outputs=True) | |
# # #interum_q = list(response.keys()) | |
# # interum_a = list(response.values()) | |
# # q = query | |
# # a = interum_a[0] | |
# # return a | |
# # # query = "describe the fisher database" | |
# # # docs = docsearch.similarity_search(query) | |
# # # chain = load_qa_chain(OpenAI(openai_api_key = "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", temperature=0), chain_type="map_reduce", return_map_steps=False) | |
# # # chain({"input_documents": docs, "question": query}, return_only_outputs=True) | |
# # title = "Query the S Drive!" | |
# # description = """This QA system will answer questions based on information in [data descriptions](https://indeocorp-my.sharepoint.com/:x:/g/personal/rrakov_sorenson_com/EWhs_Gpp9nNEukR7iJLd4mQBPREngKdRGYpT545jX8mY4Q?e=9EeEWF)""" | |
# # interface = gr.Interface( | |
# # fn=getAnswer, | |
# # inputs="textbox", | |
# # outputs="text", | |
# # title = title, | |
# # description = description, | |
# # examples=[["Where is the Fisher database?"], ["Where is the Defined Crowd audio?"], ["Do we have any Spanish audio data?"], | |
# # ["How many audio files do we have in the CallHome database?"]]) | |
# # interface.launch() | |
# # if __name__ == "__main__": | |
# # main() | |
# # def main(): | |
# # results = setMode() | |
# # print (results) | |
# # main() | |