app.py

from OpenAITools.ExpertTools import GetPubmedSummaryDf, generate, search
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, SummaryIndex
from llama_index.core import Document
from llama_index.llms.groq import Groq
from llama_index.core import ServiceContext, set_global_service_context
from llama_index.llms.llama_cpp.llama_utils import messages_to_prompt, completion_to_prompt
import gradio as gr

#models
LLAMA3_8B = "Llama3-8b-8192"
LLAMA3_70B = "Llama3-70b-8192"
Mixtral  = "mixtral-8x7b-32768" 

def custom_completion_to_prompt(completion:str) ->str:
    return completion_to_prompt(
        completion, system_prompt=(
            "You are a Q&A assistant. Your goal is to answer questions as "
            "accurately as possible is the instructions and context provided."
        ),
    )

def getMutationEffect(cancer_name, gene_name):
    searchWords=  "(" +str(cancer_name)+ ") AND " + "(" + str(gene_name) + ") AND(treatment)"
    studies = search(searchWords)
    df, abstracts= GetPubmedSummaryDf(studies)
    #Define LLM
    llm = Groq(
    model=LLAMA3_8B,
    temperature=0.01,
    context_window=4096,
    completion_to_prompt=custom_completion_to_prompt,
    messages_to_prompt=messages_to_prompt,)
    #set global service context
    ctx = ServiceContext.from_defaults(llm=llm)
    set_global_service_context(ctx)
    documents = [Document(text=t) for t in abstracts[:10]]
    index = SummaryIndex.from_documents(documents)
    query_engine = index.as_query_engine(response_mode="tree_summarize")
    prompt = "Please prepare a single summary of the abstracts of the following papers. Pay particular attention to the {} gene".format(gene_name)
    response = query_engine.query(prompt)
    return response

demo = gr.Interface(fn=getMutationEffect,
                    inputs=[gr.Textbox(label="CancerName"),
                            gr.Textbox(label="GeneName"),
                    ],
                    outputs="text")


if __name__ == "__main__":
    demo.launch()