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from langchain_community.embeddings.sentence_transformer import ( |
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SentenceTransformerEmbeddings, |
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) |
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from langchain.retrievers import ParentDocumentRetriever |
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from langchain_community.vectorstores import Chroma |
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from langchain_text_splitters import CharacterTextSplitter, RecursiveCharacterTextSplitter |
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import pickle |
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from langchain_community.llms import HuggingFaceEndpoint |
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from dotenv import load_dotenv, find_dotenv |
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import os |
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from langchain.chains import ConversationalRetrievalChain |
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from langchain.chains import LLMChain |
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from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT |
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from langchain.chains.question_answering import load_qa_chain |
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from langchain import PromptTemplate |
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from transformers import pipeline |
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load_dotenv(find_dotenv()) |
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HUGGINGFACEHUB_API_TOKEN = os.environ["HUGGINGFACEHUB_API_TOKEN"] |
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repo_id ="mistralai/Mistral-7B-Instruct-v0.2" |
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llm = None |
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def choose_model2(model): |
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global repo_id |
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global llm |
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if model == "mistral-7b-base-model": |
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repo_id="mistralai/Mistral-7B-Instruct-v0.2" |
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print("model chooosed from recomm",repo_id) |
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else: |
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repo_id="GRMenon/mental-health-mistral-7b-instructv0.2-finetuned-V2" |
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print("model chooosed from recomm",repo_id) |
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llm = HuggingFaceEndpoint( |
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repo_id=repo_id, max_length=512, temperature=0.5, token=HUGGINGFACEHUB_API_TOKEN |
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) |
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def start_recommend(): |
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persist_directory="Data/chroma" |
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embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2") |
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vectors = Chroma(persist_directory = persist_directory, embedding_function = embedding_function, collection_name="split_parents") |
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retriever = vectors.as_retriever() |
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prompt = """You're a Mental Health Specialist. Support those with Depressive Disorder. Your task is to provide brief mental health advice. PLEASE KEEP IT BRIEF. |
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Listen compassionately, respond helpfully. For casual talk, be friendly. For facts, use context. |
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Following is the user feeling {question}. |
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If unsure, say, 'Out of my knowledge.' Always stay direct. |
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If you cannot find the answer from the pieces of context, just say that you don't know, don't try to make up an answer. |
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PLEASE GIVE THE BRIEF RESPONSE IN THE FORM OF BULLET POINTS. |
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---------------- |
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{context}""" |
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global chain |
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prompt = PromptTemplate(input_variables=['question'],template=prompt) |
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chain1 = LLMChain(llm=llm, prompt=prompt, verbose=True) |
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doc_chain = load_qa_chain(llm, chain_type="stuff") |
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chain = ConversationalRetrievalChain( |
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retriever=retriever, |
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question_generator=chain1, |
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combine_docs_chain=doc_chain, |
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verbose=True, |
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) |
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def recommend2(query): |
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chat_history = [] |
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result = chain.invoke({"question": query, "chat_history": chat_history}) |
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print("---------------\nSummary from the chat agent:",query) |
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return result["answer"] |
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def is_depressed(human_inputs): |
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'''' |
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returns wether according to human inputs the person is depressed or not |
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''' |
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pipe = pipeline('sentiment-analysis') |
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status = pipe(human_inputs) |
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return 'Is depressed' if status[0]["label"] == "NEGATIVE" else 'Not Depressed' |
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def is_depressed_from_nlp_model(human_inputs): |
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'''' |
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returns wether according to human inputs the person is depressed or not |
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''' |
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from nlp_models import sentiment_class |
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predictions = sentiment_class(human_inputs) |
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probability_depressed, probability_not_depressed = predictions[0] |
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if probability_depressed > probability_not_depressed: |
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return "Depressed", probability_depressed |
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else: |
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return "Not depressed", probability_not_depressed |
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