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| import os | |
| os.environ["OPENAI_API_KEY"] | |
| from llama_index import ( | |
| VectorStoreIndex, | |
| SummaryIndex, | |
| SimpleKeywordTableIndex, | |
| SimpleDirectoryReader, | |
| ServiceContext, | |
| StorageContext, | |
| load_index_from_storage | |
| ) | |
| from llama_index.schema import IndexNode | |
| from llama_index.tools import QueryEngineTool, ToolMetadata | |
| from llama_index.llms import OpenAI | |
| llm = OpenAI(temperature=0, model="gpt-3.5-turbo") | |
| service_context = ServiceContext.from_defaults(llm=llm) | |
| PERSIST_DIR = "arv_metadata" | |
| storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR) | |
| index = load_index_from_storage(storage_context) | |
| query_engine = index.as_query_engine(similarity_top_k=3, llm=OpenAI(model="gpt-3.5-turbo")) | |
| preamble = (" The person asking the following prompt is a person living with HIV in Kenya." | |
| " For every response, 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." | |
| " They are asking questions through a mobile application called Nishauri" | |
| " through which they can see their lab results, appointment histories, and upcoming appointments." | |
| " Here is some information that is authoritative and should guide responses, when relevant." | |
| " For questions about viral load, be sure to provide specific information" | |
| " about cutoffs for viral load categories. 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." | |
| " An established client is one who is on their current ART regimen for a period greater" | |
| " than 6 months, had no active OI or in the previous 6 months, has adhered to scheduled" | |
| " clinic visits for the previous 6 months and Viral load results has been less than 200 copies/ml" | |
| " within the last 6 months." | |
| " For questions about when patients should get their viral loads taken," | |
| " if they are newly initiated on ART, the first viral load sample should be taken after 3 months of" | |
| " taking ART. Otherwise, if they are not new on ART, then if their previous result was below 50 to 199 cp/ml," | |
| " their viral load should be taken after every 12 months. If their previous result was above 200cp/ml," | |
| " then viral load sample should be taken after three months." | |
| " Please answer the prompt using the information retrieved" | |
| " and do not rely at all on your prior knowledge." | |
| " Please keep your reply to no longer than three sentences, and please use simple language. ") | |
| prompt_intro = (" Here is the prompt: ") | |
| import gradio as gr | |
| def nishauri(question: str, conversation_history: list[str]): | |
| context = " ".join([item["user"] + " " + item["chatbot"] for item in conversation_history]) | |
| response = query_engine.query(preamble + | |
| "the user previously asked and received the following: " + | |
| context + | |
| prompt_intro + | |
| question) | |
| conversation_history.append({"user": question, "chatbot": response.response}) | |
| source1 = ("File Name: " + | |
| response.source_nodes[0].metadata["file_name"] + | |
| "\nPage Number: " + | |
| response.source_nodes[0].metadata["page_label"] + | |
| "\n Source Text: " + | |
| response.source_nodes[0].text) | |
| source2 = ("File Name: " + | |
| response.source_nodes[1].metadata["file_name"] + | |
| "\nPage Number: " + | |
| response.source_nodes[1].metadata["page_label"] + | |
| "\n Source Text: " + | |
| response.source_nodes[1].text) | |
| source3 = ("File Name: " + | |
| response.source_nodes[2].metadata["file_name"] + | |
| "\nPage Number: " + | |
| response.source_nodes[2].metadata["page_label"] + | |
| "\n Source Text: " + | |
| response.source_nodes[2].text) | |
| return response, source1, source2, source3, conversation_history | |
| inputs = [gr.Textbox(lines=10, label="Question"), | |
| gr.State(value=[])] | |
| outputs = [ | |
| gr.Textbox(label="Chatbot Response", type="text"), | |
| gr.Textbox(label="Source 1", max_lines = 10, autoscroll = False, type="text"), | |
| gr.Textbox(label="Source 2", max_lines = 10, autoscroll = False, type="text"), | |
| gr.Textbox(label="Source 3", max_lines = 10, autoscroll = False, type="text"), | |
| gr.State() | |
| ] | |
| gr.Interface(fn=nishauri, inputs=inputs, outputs=outputs, title="Nishauri Chatbot", | |
| description="Enter a question and see the processed outputs in collapsible boxes.").launch() |