app.py

from OpenAITools.ExpertTools import GetPubmedSummaryDf, generate, search
from llama_index.core import  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
#from llama_index.settings import Settings
from llama_index.core import Settings
import gradio as gr

#models
LLAMA3_8B = "llama-3.1-8b-instant"
LLAMA3_70B = "llama-3.1-8b-instant"
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,
    )
    
    # グローバルサービスコンテキストの設定
    Settings.llm = llm
    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 = f"Please prepare a single summary of the abstracts of the following papers. Pay particular attention to the {gene_name} gene"
    response = query_engine.query(prompt)
    
    # テキストをファイルに保存
    summary_text = str(response)
    outputname = cancer_name + "_" + gene_name + "_" + "mutation_effect_summary.txt"
    with open(outputname, "w") as file:
        file.write(summary_text)
    
    return summary_text, outputname  # テキストとダウンロード用ファイルを返す

# Gradioインターフェース設定
demo = gr.Interface(
    fn=getMutationEffect,
    inputs=[gr.Textbox(label="CancerName"), gr.Textbox(label="GeneName")],
    outputs=[gr.Textbox(label="Summary"), gr.File(label="Download Summary as .txt")]  # テキスト表示とダウンロードボタンを両方表示
)

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