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| # from streamlit.logger import get_logger | |
| from models.custom_parsers import CustomStringOutputParser | |
| from langchain.chains import LLMChain | |
| from langchain.llms import OpenAI | |
| from langchain.prompts import PromptTemplate | |
| # logger = get_logger(__name__) | |
| # logger.debug("START APP") | |
| finetuned_models = { | |
| # "olivia_babbage_engine": "babbage:ft-crisis-text-line:exp-olivia-babbage-2023-02-23-19-57-19", | |
| "Anxiety-English": "curie:ft-crisis-text-line:exp-olivia-curie-2-2023-02-24-00-25-13", | |
| # "olivia_davinci_engine": "davinci:ft-crisis-text-line:exp-olivia-davinci-2023-02-24-00-02-41", | |
| # "olivia_augmented_babbage_engine": "babbage:ft-crisis-text-line:exp-olivia-augmented-babbage-2023-02-24-18-35-42", | |
| # "Olivia-Augmented": "curie:ft-crisis-text-line:exp-olivia-augmented-curie-2023-02-24-20-13-33", | |
| # "olivia_augmented_davinci_engine": "davinci:ft-crisis-text-line:exp-olivia-augmented-davinci-2023-02-24-23-57-08", | |
| # "kit_babbage_engine": "babbage:ft-crisis-text-line:exp-kit-babbage-2023-03-06-21-34-10", | |
| # "kit_curie_engine": "curie:ft-crisis-text-line:exp-kit-curie-2023-03-06-22-01-29", | |
| "Suicide-English": "curie:ft-crisis-text-line:exp-kit-curie-2-2023-03-08-16-26-48", | |
| # "kit_davinci_engine": "davinci:ft-crisis-text-line:exp-kit-davinci-2023-03-06-23-09-15", | |
| # "olivia_es_davinci_engine": "davinci:ft-crisis-text-line:es-olivia-davinci-2023-04-25-17-07-44", | |
| "Anxiety-Spanish": "curie:ft-crisis-text-line:es-olivia-curie-2023-04-27-15-02-42", | |
| # "olivia_curie_engine": "curie:ft-crisis-text-line:exp-olivia-curie-2-2023-02-24-00-25-13", | |
| # "Oscar-Spanish": "curie:ft-crisis-text-line:es-oscar-curie-2023-05-03-21-55-06", | |
| # "oscar_es_davinci_engine": "davinci:ft-crisis-text-line:es-oscar-davinci-2023-05-03-21-39-29", | |
| } | |
| # def generate_next_response(completion_engine, context, temperature=0.8): | |
| # completion = openai.Completion.create( | |
| # engine=completion_engine, | |
| # prompt=context, | |
| # temperature=temperature, | |
| # max_tokens=150, | |
| # stop="helper:" | |
| # ) | |
| # completion_text = completion['choices'][0]['text'] | |
| # return completion_text | |
| # def update_memory_completion(helper_input, memory, OA_engine, temperature=0.8): | |
| # memory.chat_memory.add_user_message(helper_input) | |
| # context = "## BEGIN ## \n" + memory.load_memory_variables({})['history'] + "\ntexter:" | |
| # print(context) | |
| # response = generate_next_response(OA_engine, context, temperature).strip().replace("\n","") | |
| # response = response.split("texter:")[0] | |
| # memory.chat_memory.add_ai_message(response) | |
| # return response | |
| def get_finetuned_chain(model_name, memory, temperature=0.8): | |
| _TEXTER_TEMPLATE_ = """The following is a friendly conversation between a volunter and a person in crisis; | |
| Current conversation: | |
| {history} | |
| helper: {input} | |
| texter:""" | |
| PROMPT = PromptTemplate( | |
| input_variables=['history', 'input'], | |
| template=_TEXTER_TEMPLATE_ | |
| ) | |
| llm = OpenAI( | |
| temperature=temperature, | |
| model=model_name, | |
| max_tokens=150, | |
| ) | |
| llm_chain = LLMChain( | |
| llm=llm, | |
| prompt=PROMPT, | |
| memory=memory, | |
| output_parser = CustomStringOutputParser() | |
| ) | |
| # logger.debug(f"{__name__}: loaded fine tuned model {model_name}") | |
| return llm_chain, "helper:" |