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()