app.py

import gradio as gr
import requests
import os
import json
from datasets import load_dataset
from sentence_transformers import SentenceTransformer, util

# 문장 임베딩 모델 로드
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')

# 데이터셋 로드
datasets = [
    ("all-processed", "all-processed"),
    ("chatdoctor-icliniq", "chatdoctor-icliniq"),
    ("chatdoctor_healthcaremagic", "chatdoctor_healthcaremagic"),
]

all_datasets = {}
for dataset_name, config in datasets:
    all_datasets[dataset_name] = load_dataset("lavita/medical-qa-datasets", config)

def find_most_similar_data(query):
    query_embedding = model.encode(query, convert_to_tensor=True)
    most_similar = None
    highest_similarity = -1
    
    for dataset_name, dataset in all_datasets.items():
        for split in dataset.keys():
            for item in dataset[split]:
                if 'question' in item and 'answer' in item:
                    item_text = f"질문: {item['question']} 답변: {item['answer']}"
                    item_embedding = model.encode(item_text, convert_to_tensor=True)
                    similarity = util.pytorch_cos_sim(query_embedding, item_embedding).item()
                    
                    if similarity > highest_similarity:
                        highest_similarity = similarity
                        most_similar = item_text
    
    return most_similar

def respond_with_prefix(message, history, max_tokens=10000, temperature=0.7, top_p=0.95):
    # 여기에 한글 답변 관련 프리픽스 로직 삽입
    system_prefix = """
    여기에 원래 코드의 시스템 프리픽스를 삽입하세요.
    """
    modified_message = system_prefix + message  # 사용자 메시지에 프리픽스 적용
    
    # 가장 유사한 데이터를 데이터셋에서 찾기
    similar_data = find_most_similar_data(message)
    if similar_data:
        modified_message += "\n\n" + similar_data  # 유사한 데이터를 메시지에 추가
    
    data = {
        "model": "jinjavis:latest",
        "prompt": modified_message,
        "max_tokens": max_tokens,
        "temperature": temperature,
        "top_p": top_p
    }
    
    # API 요청
    response = requests.post("http://hugpu.ai:7877/api/generate", json=data, stream=True)
    
    partial_message = ""
    for line in response.iter_lines():
        if line:
            try:
                result = json.loads(line)
                if result.get("done", False):
                    break
                new_text = result.get('response', '')
                partial_message += new_text
                yield partial_message
            except json.JSONDecodeError as e:
                print(f"Failed to decode JSON: {e}")
                yield "An error occurred while processing your request."

demo = gr.ChatInterface(
    fn=respond_with_prefix,
    additional_inputs=[
        gr.Slider(minimum=1, maximum=120000, value=4000, label="Max Tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, label="Top-P")  # Corrected comma placement
    ],
    theme="Nymbo/Nymbo_Theme"
)

if __name__ == "__main__":
    demo.queue(max_size=4).launch()