import os #import cv2 #import pdb import sys import time import numpy as np #from PIL import Image #from transformers import logging #logging.set_verbosity_error() #from models.kts_model import VideoSegmentor #from models.clip_model import FeatureExtractor #from models.blip2_model import ImageCaptioner #from models.grit_model import DenseCaptioner #from models.whisper_model import AudioTranslator #from models.gpt_model import LlmReasoner from utils import logger_creator, format_time #from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM, LlamaTokenizer, AutoModelForSeq2SeqLM import together import warnings from together import Together from langchain.prompts import PromptTemplate from langchain.llms.base import LLM from langchain.chains import ConversationalRetrievalChain from langchain_core.output_parsers import StrOutputParser from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.llms import HuggingFacePipeline #from langchain.embeddings import HuggingFaceEmbeddings from langchain_community.embeddings import HuggingFaceEmbeddings from langchain_community.document_loaders import TextLoader from langchain.document_loaders import UnstructuredFileLoader from langchain_community.vectorstores import FAISS from langchain.utils import get_from_dict_or_env from langchain_core.runnables import RunnablePassthrough, RunnableParallel from pydantic.v1 import Extra, Field, root_validator from typing import Any, Dict, List, Mapping, Optional from langchain.memory import ConversationBufferMemory from langchain import LLMChain, PromptTemplate #from paddleocr import PaddleOCR, draw_ocr #sys.path.append('/root/autodl-tmp/recognize-anything') #from ram.models import ram #from ram.models import tag2text #from ram import inference_ram as inference #from ram import inference_tag2text as inference #from ram import get_transform warnings.filterwarnings("ignore", category=UserWarning) B_INST, E_INST = "[INST]", "[/INST]" B_SYS, E_SYS = "<>\n", "\n<>\n\n" DEFAULT_SYSTEM_PROMPT = "" instruction = """You are an AI assistant designed for answering questions about a video. You are given a document and a question, the document records what people see and hear from this video. Try to connet these information and provide a conversational answer. Question: {question} ========= {context} ========= """ system_prompt = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. You can assume the discussion is about the video content. Chat History: {chat_history} Follow Up Input: {question} Standalone question:""" #os.environ['HF_HOME'] = '/root/autodl-tmp/cache/' os.environ["TOGETHER_API_KEY"] = "48bf2536f85b599c7d5d7f9921cc9ee7056f40ed535fd2174d061e1b9abcf8af" def get_prompt(instruction, new_system_prompt=DEFAULT_SYSTEM_PROMPT ): SYSTEM_PROMPT = B_SYS + new_system_prompt + E_SYS prompt_template = B_INST + SYSTEM_PROMPT + instruction + E_INST return prompt_template template = get_prompt(instruction, system_prompt) prompt = PromptTemplate( input_variables=["chat_history", "user_input"], template=template ) class TogetherLLM(LLM): """Together large language models.""" model: str = "togethercomputer/llama-2-70b-chat" """model endpoint to use""" together_api_key: str = os.environ["TOGETHER_API_KEY"] """Together API key""" temperature: float = 0.7 """What sampling temperature to use.""" max_tokens: int = 512 """The maximum number of tokens to generate in the completion.""" class Config: extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that the API key is set.""" api_key = get_from_dict_or_env( values, "together_api_key", "TOGETHER_API_KEY" ) values["together_api_key"] = api_key return values @property def _llm_type(self) -> str: """Return type of LLM.""" return "together" def _call( self, prompt: str, **kwargs: Any, ) -> str: """Call to Together endpoint.""" together.api_key = self.together_api_key output = together.Complete.create(prompt, model=self.model, max_tokens=self.max_tokens, temperature=self.temperature, top_p=0.7, top_k=50, repetition_penalty=1, stop=[""], ) text = output['choices'][0]['text'] return text class Vlogger4chat : def __init__(self, args): self.args = args self.alpha = args.alpha self.beta = args.beta self.data_dir = args.data_dir, self.tmp_dir = args.tmp_dir self.models_flag = False self.init_llm() self.history = [] if not os.path.exists(self.tmp_dir): os.makedirs(self.tmp_dir) def init_llm(self): print('\033[1;33m' + "Initializing LLM Reasoner...".center(50, '-') + '\033[0m') self.llm = TogetherLLM( model= "microsoft/WizardLM-2-8x22B", temperature=0.1, max_tokens=768 ) print('\033[1;32m' + "LLM initialization finished!".center(50, '-') + '\033[0m') def exist_videolog(self, video_id): if isinstance(self.data_dir, tuple): self.data_dir = self.data_dir[0] # 或者根据实际情况选择合适的索引 if isinstance(video_id, tuple): video_id = video_id[0] # 或者根据实际情况选择合适的索引 log_path = os.path.join(self.data_dir, f"{video_id}.log") #print(f"log_path: {log_path}\n") if os.path.exists(log_path): #print("existing log path!!!\n") loader = UnstructuredFileLoader(log_path) raw_documents = loader.load() if not raw_documents: print("The log file is empty or could not be loaded.") return False # 如果 raw_documents 为空或所有内容都为空白，直接返回 # Split text text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50) chunks = text_splitter.split_documents(raw_documents) self.vector_storage = FAISS.from_documents(chunks, self.my_embedding) self.chain = ConversationalRetrievalChain.from_llm(self.llm, self.vector_storage.as_retriever(), return_source_documents=True) return True return False def create_videolog(self, video_id): video_id = os.path.basename(self.video_path).split('.')[0] log_path = os.path.join(self.data_dir, video_id + '.log') loader = UnstructuredFileLoader(log_path) raw_documents = loader.load() # Split text text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50) chunks = text_splitter.split_documents(raw_documents) self.vector_storage = FAISS.from_documents(chunks, self.my_embedding) self.chain = ConversationalRetrievalChain.from_llm(self.llm, self.vector_storage.as_retriever(), return_source_documents=True) def video2log(self, video_path): self.video_path = video_path video_id = os.path.basename(video_path).split('.')[0] if self.exist_videolog(video_id): return self.printlog(video_id) return self.printlog(video_id) def printlog(self, video_id): log_list = [] log_path = os.path.join(self.data_dir, video_id + '.log') with open(log_path, 'r', encoding='utf-8') as f: for line in f: log_list.append(line.strip()) return log_list def chat2video(self, question): print(f"Question: {question}") response = self.chain({"question": "请用中文回答："+question, "chat_history": self.history})['answer'] self.history.append((question, response)) return response def clean_history(self): self.history = [] return