app.py

import gradio as gr
from datasets import load_dataset

import os
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig
import torch
from threading import Thread
from sentence_transformers import SentenceTransformer
from datasets import load_dataset
import time

token = os.environ["HF_TOKEN"]
ST = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")

dataset = load_dataset("AI-4-Health/embedded-dataset")

data = dataset["train"]
data = data.add_faiss_index("embeddings") # column name that has the embeddings of the dataset


model_id = "meta-llama/Meta-Llama-3-8B-Instruct"

# use quantization to lower GPU usage
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16
)

tokenizer = AutoTokenizer.from_pretrained(model_id,token=token)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config,
    token=token
)
terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

SYS_PROMPT = """You are an expert biomedical researcher. For answering the Question at the end with brevity, you need to first read the Context provided. Then give your final answer briefly, by citing the Provenance information from the context. You can find Provenance from the Context statement 'Provenance of this association is <Provenance>'. Do not forget to cite the Provenance information. Note that, if Provenance is 'GWAS' report it as 'GWAS Catalog'. If Provenance is 'DISEASES' report it
as 'DISEASES database - https://diseases.jensenlab.org'. Additionally, when providing drug or medication suggestions, give maximum information available and then advise the user to seek guidance from a healthcare professional as a precautionary measure."""


def search(query: str, k: int = 3 ):
    """a function that embeds a new query and returns the most probable results"""
    embedded_query = ST.encode(query) # embed new query
    scores, retrieved_examples = data.get_nearest_examples( # retrieve results
        "embeddings", embedded_query, # compare our new embedded query with the dataset embeddings
        k=k # get only top k results
    )
    return scores, retrieved_examples

def format_prompt(prompt,retrieved_documents,k):
    """using the retrieved documents we will prompt the model to generate our responses"""
    PROMPT = f"Question:{prompt}\nContext:"
    for idx in range(k) :
        PROMPT+= f"{retrieved_documents['text'][idx]}\n"
    return PROMPT



TITLE = "# RAG"

DESCRIPTION = """
HPP Chatbot
"""
@spaces.GPU(duration=150)
def talk(prompt):
    k = 1 # number of retrieved documents
    scores, retrieved_documents = search(prompt, k)
    filename = retrieved_documents['filename'][0]  # Assuming filename is in the returned dictionary
    print("filename is ", filename)
    formatted_prompt = format_prompt(prompt, retrieved_documents, k)
    formatted_prompt = formatted_prompt[:2000]  # to avoid GPU OOM
    messages = [{"role":"system", "content":SYS_PROMPT}, {"role":"user", "content":formatted_prompt}]
    # Tell the model to generate
    input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to(model.device)
    outputs = model.generate(
        input_ids,
        max_new_tokens=1024,
        eos_token_id=terminators,
        do_sample=True,
        temperature=0.6,
        top_p=0.9,
    )
    streamer = TextIteratorStreamer(
        tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True
    )
    generate_kwargs = dict(
        input_ids=input_ids,
        streamer=streamer,
        max_new_tokens=1024,
        do_sample=True,
        top_p=0.95,
        temperature=0.75,
        eos_token_id=terminators,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    outputs = []
    for text in streamer:
        outputs.append(text)
        #print(outputs)
    return "".join(outputs), filename, filename

def update_document(filename):
    # Reads the content of the specified file for display
    with open('datasets/'+filename, "r", encoding='iso-8859-15') as file:
        content = file.read()
    return content

TITLE = "# RAG"
DESCRIPTION = """
HPP Chatbot
"""
with gr.Blocks() as demo:
    with gr.Row():
        prompt_input = gr.Textbox(
            label="Enter your prompt",
            value="What are the current challenges in developing effective gene therapy for hypophosphatasia?",
            lines=3,
        )
        submit_button = gr.Button("Submit")
        chat_output = gr.Textbox(label="Chat Response", lines=10)
        filename = gr.Textbox(label="File Name", lines=1)
        file_display = gr.Textbox(label="File Content", lines=10)

    submit_button.click(
        fn=talk,
        inputs=prompt_input,
        outputs=[chat_output, filename, file_display]
    )

    file_display.change(
        fn=update_document,
        inputs=filename,
        outputs=file_display
    )
if __name__ == "__main__":
    demo.launch(debug=True, share=True)