app.py

#import gradio as gr
#import cv2
#def to_black(image):
#    output = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
#    return output
#interface = gr.Interface(fn=to_black, inputs="image", outputs="image")
#print('here')
#interface.launch()

#print(share_url)
#print(local_url)
#print(app)
#interface.launch(inbrowser =True, share=True, port=8888)
#url = interface.share()
#print(url)
from langchain.chains import RetrievalQA
from langchain.document_loaders import UnstructuredFileLoader, TextLoader, CSVLoader
from langchain.document_loaders import CSVLoader
from langchain.document_loaders import TextLoader
from langchain.vectorstores import DocArrayInMemorySearch
from langchain.indexes import VectorstoreIndexCreator
from langchain.prompts import PromptTemplate
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain import HuggingFacePipeline
import torch
from langchain.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.base import Chain
from langchain.chains import ConversationalRetrievalChain
from langchain.chains.summarize import load_summarize_chain
import gradio as gr
from typing import List
from tqdm import tqdm
import logging
import argparse
import os
import string

CHUNK_SIZE=600
CHUNK_OVERLAP = 100
SEARCH_TOP_K = 5
logger = logging.getLogger("bio_LLM_logger")

def tree(filepath, ignore_dir_names=None, ignore_file_names=None):
    """返回两个列表，第一个列表为 filepath 下全部文件的完整路径, 第二个为对应的文件名"""
    if ignore_dir_names is None:
        ignore_dir_names = []
    if ignore_file_names is None:
        ignore_file_names = []
    ret_list = []
    if isinstance(filepath, str):
        if not os.path.exists(filepath):
            print("路径不存在")
            return None, None
        elif os.path.isfile(filepath) and os.path.basename(filepath) not in ignore_file_names:
            return [filepath], [os.path.basename(filepath)]
        elif os.path.isdir(filepath) and os.path.basename(filepath) not in ignore_dir_names:
            for file in os.listdir(filepath):
                fullfilepath = os.path.join(filepath, file)
                if os.path.isfile(fullfilepath) and os.path.basename(fullfilepath) not in ignore_file_names:
                    ret_list.append(fullfilepath)
                if os.path.isdir(fullfilepath) and os.path.basename(fullfilepath) not in ignore_dir_names:
                    ret_list.extend(tree(fullfilepath, ignore_dir_names, ignore_file_names)[0])
    return ret_list, [os.path.basename(p) for p in ret_list]


def load_file(file_path, chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP):
    if file_path.lower().endswith(".pdf"):
        loader = UnstructuredFileLoader(file_path, mode="elements")
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap= chunk_overlap)
        docs = loader.load_and_split(text_splitter=text_splitter)
    elif file_path.lower().endswith(".txt"):
        loader = TextLoader(file_path, autodetect_encoding=True)
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap= chunk_overlap)
        docs = loader.load_and_split(text_splitter=text_splitter)
    elif file_path.lower().endswith(".csv"):
        loader = CSVLoader(file_path)
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap= chunk_overlap)
        docs = loader.load_and_split(text_splitter=text_splitter)
    else:
        print("unsupported the file format")
    
    return docs 

#class summary_chain:
#        def init_cfg(self,
#                     llm_model: Chain,
            
def summary(model, chain_type, PROMPT, REFINE_PROMPT,docs):
    if chain_type == "stuff":
        chain = load_summarize_chain(model, chain_type="stuff", prompt=PROMPT)
    elif chain_type == "refine":
        chain = load_summarize_chain(model, chain_type="refine", question_prompt=PROMPT, refine_prompt=REFINE_PROMPT)
    print(chain.run(docs))       


class QA_Localdb:
    llm_model_chain: Chain = None
    embeddings: object = None
    top_k: int = SEARCH_TOP_K
    chunk_size: int = CHUNK_SIZE
    
    def init_cfg(self,
                 llm_model: Chain,
                 embedding_model: str,
                 #embedding_device: str,
                 top_k = SEARCH_TOP_K,
                ):
        self.llm_model_chain = llm_model
        self.embeddings = HuggingFaceEmbeddings(model_name = embedding_model)
        self.top_k = top_k
    
    def init_knowledge_vector_store(self,
                                    file_path: str or List[str],
                                    vectorstore_path: str or os.PathLike = None,
                                ):
        loaded_files = []
        failed_files = []
        if isinstance(file_path, str):
            if not os.path.exists(file_path):
                print("unknown path")
                return None
            elif os.path.isfile(file_path):
                file = os.path.split(file_path)[-1]
                try:
                    docs = load_file(file_path)
                    logger.info(f"{file} sucessful loaded")
                    loaded_files.append(file_path)
                except Exception as e:
                    logger.error(e)
                    logger.info(f"{file} unsucessful loaded")
                    return None
                
            elif os.path.isdir(file_path):    
                docs=[]
                for fullfilepath, file in tqdm(zip(*tree(file_path, ignore_dir_names=['tmp_files'])), desc="load file"):
                    try:
                        docs += load_file(fullfilepath)
                        loaded_files.append(fullfilepath)
                    except Exception as e:
                        logger.error(e)
                        failed_files.append(file)
                
                if len(failed_files) > 0:
                    logger.info('unloaded files are as follows')
                    for file in failed_files:
                        logger.info(f"{file}\n")
        else:
            docs = []
            for file in file_path:
                try: 
                    docs += load_file(file)
                    logger.info(f"{file} sucessful loaded")
                    loaded_files.append(file)
                except Exception as e:
                    logger.error(e)
                    logger.info(f"{file} unsucessful loaded")
        if len(docs) > 0:
            logger.info("sucessful loaded, generating vector store")
            if vectorstore_path and os.path.isdir(vectorstore_path) and "index.faiss" in os.listdir(vectorstore_path):
                print("temp")
              
              #  vector_store = load_vector_store(vectorstore_path, self.embeddings)
              #  vector_store.add_documents(docs)
               # torch_gc()
            else:
                if not vectorstore_path:
                    vectorstore_path = ""
                vector_store =  FAISS.from_documents(docs, self.embeddings)
                #vector_store.save_local(vectorstore_path)
                return vector_store, loaded_files
        else:
            logger.info("file load failed")  
    
        
            
    '''     
    def delete_file_from_vector_store(self,
                                      filepath: str or List[str],
                                      vs_path):
        vector_store = load_vector_store(vs_path, self.embeddings)
        status = vector_store.delete_doc(filepath)
        return status

    def update_file_from_vector_store(self,
                                      filepath: str or List[str],
                                      vs_path,
                                      docs: List[Document], ):
        vector_store = load_vector_store(vs_path, self.embeddings)
        status = vector_store.update_doc(filepath, docs)
        return status

    def list_file_from_vector_store(self,
                                    vs_path,
                                    fullpath=False):
        vector_store = load_vector_store(vs_path, self.embeddings)
        docs = vector_store.list_docs()
        if fullpath:
            return docs
        else:
            return [os.path.split(doc)[-1] for doc in docs]
    '''     
def QA_model():
   # file_path = "/mnt/petrelfs/lvying/LLM/BoMA/data/test/OPUS-DSD.pdf"
    file_path = "doc1.txt"
   # file_path = "/mnt/petrelfs/lvying/LLM/BoMA/data/test/Interageting-Prior-into-DA.pdf"
   # file_path = "/mnt/petrelfs/lvying/LLM/BoMA/data/test/"

    model_path = "/mnt/petrelfs/lvying/LLM/BoMA/models/LLM/Llama-2-13b-chat-hf"
    embedding_path = "/mnt/petrelfs/lvying/LLM/BoMA/text2vec/instructor-xl/"
    
    model = HuggingFacePipeline.from_model_id(model_id="daryl149/llama-2-7b-chat-hf",
        task="text-generation",
        model_kwargs={
                    "torch_dtype" : torch.float32,
                    "low_cpu_mem_usage" :True,
                    "temperature": 0.2,
                    "max_length": 2048,
                #    "device_map": "auto",
                    "repetition_penalty":1.1}
            )
    print(model.model_id)
    QA = QA_Localdb()
    QA.init_cfg(llm_model=model, embedding_model = "sentence-transformers/paraphrase-MiniLM-L6-v2")
    
    vector_store, _ =QA.init_knowledge_vector_store(file_path)     
    retriever = vector_store.as_retriever(search_kwargs={"k": 3})   
     
    print("loading LLM...")
    prompt_template = ("Below is an instruction that describes a task. "
                   "Write a response that appropriately completes the request.\n\n"
                   "### Instruction:\n{context}\n{question}\n\n### Response: ") 
    
    PROMPT = PromptTemplate(
            template=prompt_template, input_variables=["context", "question"]
        )
    
    chain_type_kwargs = {"prompt": PROMPT}
    
    #print(chain_type_kwargs)
    '''
    qa_stuff = RetrievalQA.from_chain_type(
        llm = model,
        chain_type="stuff",
        retriever = retriever,
        chain_type_kwargs = chain_type_kwargs,
    #    verbose = True
    )
    while True:
        print("Input Qusetion:")
        query = input()
        if len(query.strip())==0:
            break
        print(qa_stuff.run(query))
    
    '''
    '''
    qa = ConversationalRetrievalChain.from_llm(
        llm = QA.llm_model_chain,
        chain_type="stuff",
        retriever = retriever,
        combine_docs_chain_kwargs = chain_type_kwargs,
    #    verbose = True
    )
    '''
    qa = RetrievalQA.from_chain_type(
        llm = QA.llm_model_chain,
        chain_type="stuff",
        retriever = retriever,
        chain_type_kwargs = chain_type_kwargs,
    #    verbose = True
    )
    return qa
qa_temp = QA_model() 

def temp(query):
    return qa_temp.run(query)
  


def answer_question(query): 
    print(query)
    chat_history = []
    threshold_history = 10 # Remembered historical conversations
    i = 0
    if i>threshold_history:
        chat_history = []
    print("Send a Message:")
    #query = context
    #if len(query.strip())==0:
    #    break
    result = qa_temp({"question":query, "chat_history": chat_history})
    print(type(result["answer"]))
    chat_history.append((query, result["answer"]))
    i = i + 1
    resp = result["answer"]
    return str(resp) 
   



iface = gr.Interface(
    fn = temp,
    inputs="text",
    outputs="text",)
    #title="问答界面",
    #description="输入问题和相关文本，得到问题的答案。",
    #article="这里是相关的文本。可以输入一些段落或者问题的背景。",
    #examples=[
    #    ["Gradio是什么？", "Gradio是一个用于构建和部署机器学习模型的开源库。"],
    #    ["Python的创始人是谁？", "Python的创始人是Guido van Rossum。"]
    #])
#print(iface.launch(share=True))

#print("======Finish======")
#share_url = iface.share()
#print(share_url)
iface.launch()