| import os |
| os.environ["OPENAI_API_KEY"] |
|
|
| from llama_index.llms.openai import OpenAI |
| from llama_index.core.schema import MetadataMode |
| import openai |
| from openai import OpenAI as OpenAIOG |
| import logging |
| import sys |
| llm = OpenAI(temperature=0.0, model="gpt-3.5-turbo") |
| client = OpenAIOG() |
|
|
| from langdetect import detect |
| from langdetect import DetectorFactory |
| DetectorFactory.seed = 0 |
| from deep_translator import GoogleTranslator |
|
|
| |
| from llama_index.core import VectorStoreIndex |
| from llama_index.core import StorageContext |
| from llama_index.core import load_index_from_storage |
| storage_context = StorageContext.from_defaults(persist_dir="arv_metadata") |
| index = load_index_from_storage(storage_context) |
| query_engine = index.as_query_engine(similarity_top_k=3, llm=llm) |
| retriever = index.as_retriever(similarity_top_k = 3) |
|
|
| import gradio as gr |
|
|
| def nishauri(question: str, conversation_history: list[str]): |
| |
| context = " ".join([item["user"] + " " + item["chatbot"] for item in conversation_history]) |
|
|
| |
| words = question.split() |
|
|
| |
| num_words = len(words) |
|
|
| lang_question = "en" |
| |
| if num_words > 4: |
| lang_question = detect(question) |
| |
| if lang_question=="sw": |
| question = GoogleTranslator(source='sw', target='en').translate(question) |
| |
| sources = retriever.retrieve(question) |
| source0 = sources[0].text |
| source1 = sources[1].text |
| source2 = sources[2].text |
|
|
| background = ("The person who asked the question is a person living with HIV." |
| " If the person says sasa or niaje, that is swahili slang for hello." |
| " They are asking questions about HIV. Do not talk about anything that is not related to HIV. " |
| " Recognize that they already have HIV and do not suggest that they have to get tested" |
| " for HIV or take post-exposure prophylaxis, as that is not relevant, though their partners perhaps should." |
| " Do not suggest anything that is not relevant to someone who already has HIV." |
| " Do not mention in the response that the person is living with HIV." |
| " The following information about viral loads is authoritative for any question about viral loads:" |
| " Under 50 copies/ml is low detectable level," |
| " 50 - 199 copies/ml is low level viremia, 200 - 999 is high level viremia, and " |
| " 1000 and above is suspected treatment failure." |
| " A high viral load or non-suppressed viral load is any viral load above 200 copies/ml." |
| " A suppressed viral load is one below 200 copies / ml.") |
|
|
| question_final = ( |
| f" The user previously asked and answered the following: {context}. " |
| f" The user just asked the following question: {question}." |
| f" Please use the following content to generate a response: {source0} {source1} {source2}." |
| f" Please consider the following background information when generating a response: {background}." |
| " Keep answers brief and limited to the question that was asked." |
| " Do not provide information the user did not ask about. If they start with a greeting, just greet them in return and don't share anything else." |
| " Do not change the subject or address anything the user didn't directly ask about." |
| " If they respond with an acknowledgement such as 'ok' or 'thanks', simply thank them ask if there is anything else that you can help with.") |
|
|
| completion = client.chat.completions.create( |
| model="gpt-4o", |
| messages=[ |
| {"role": "user", "content": question_final} |
| ] |
| ) |
|
|
| reply_to_user = completion.choices[0].message.content |
| |
| if lang_question=="sw": |
| reply_to_user = GoogleTranslator(source='auto', target='sw').translate(reply_to_user) |
| |
| conversation_history.append({"user": question, "chatbot": reply_to_user}) |
|
|
| return reply_to_user, conversation_history |
|
|
| demo = gr.Interface( |
| title = "Nishauri Chatbot Demo", |
| fn=nishauri, |
| inputs=["text", gr.State(value=[])], |
| outputs=["text", gr.State()], |
| ) |
|
|
| demo.launch() |
|
|
