掌握AI摘要技术解锁个人第二大脑

python3 -m venv venv
source venv/bin/activate # ubuntu/Mac
venv\Scripts\activate # windows

pip install mkl mkl-include # MAC 使用CPU必须安装 
pip install torch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 # 核心
# 安装 Hugging Face Transformer库，需要与LLM进行交互
pip install git+https://github.com/huggingface/transformers 
# 这些将在下面用于与文档交互
pip install langchain==0.0.173 
pip install faiss-cpu==1.7.4
pip install unstructured==0.6.8
pip install pytesseract==0.3.10
pip install pypdf==3.9.0
pip install pdf2image==1.16.3
pip install sentence_transformers==2.2.2
# 只需要在CPU上运行
pip install accelerate==0.19.0
# 对于GUI和web应用程序
pip install streamlit

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers import pipeline
import torch
import streamlit

ImportError: accelerate>=0.20.3 is required for a normal functioning of this module, but found accelerate==0.19.0.

pip install accelerate==0.20.3

########### GUI IMPORTS ################
import streamlit as st
#### IMPORTS FOR AI PIPELINES ###############
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers import pipeline
from transformers import AutoModel, T5Tokenizer, T5Model
from transformers import T5ForConditionalGeneration
from langchain.llms import HuggingFacePipeline
import torch

# 设置 model 路径
checkpoint = "./model/"  # 实际上是LaMini-Flan-T5-248M
# 初始化标记器和模型
tokenizer = T5Tokenizer.from_pretrained(checkpoint)
base_model = T5ForConditionalGeneration.from_pretrained(
                                            checkpoint,
                                            device_map='auto',
                                            torch_dtype=torch.float32)

# 初始化管道
pipe_sum = pipeline('summarization', 
                    model = base_model,
                    tokenizer = tokenizer,
                    max_length = 350, 
                    min_length = 25)

text = "It was way back in 2011 when the game L.A. Noire came out with absolutely amazing life-like facial animations that seemed so ahead of every other game. Now, almost a decade later, we still haven’t seen many other games come anywhere close to matching its level in terms of delivering realistic facial expressions.This is because the facial scanning technology used in the development of this game, called MotionScan, was extremely expensive and the file sizes of the captured animations was too big, which is why it made it impractical for most publishers to adopt this technology for their games.Let’s take a look at the architecture of this Deep Learning Framework in the figure below. It consists of a Motion Module and an Appearance Module. The driving video is the input to the Motion Module and the Source Image is our target object which is the input to the Appearance Module.The Motion Module consists of an encoder that learns a latent representation containing sparse keypoints of high importance in relation to the motion of the object, which is a face in this scenario. The movement of these keypoints across the different frames of the driving video generate a motion field, which is driven by a function that we want our model to learn. The authors use Taylor Expansion to approximate this function to the first order that creates this motion field. According to the authors, this is the first time first order approximation has been used to model motion. Moreover, learned affine transformations of these keypoints are combined to produce Dense Motion Field. The dense motion field predicts the motion of every individual pixel of the frame, as opposed to focusing on just the keypoints in the sparse motion field. Next, the motion module also produces an Occlusion Map, which highlights the pixels of the frame that need to be in-painted, arising from the movements of the head w.r.t. the background.The Appearance Module uses an encoder to encode the source image, which is then combined with the Motion Field and the Occlusion Map to animate the source image. A Generator model is used for this purpose. During the self-supervised training process, a still frame from the driving video is used as the source image and the learned motion field is used to animate this source image. The actual frames of the video act as the ground truth for the generated motion, hence it is self-supervised training. During the testing/inference phase, this source image can be replaced with any other image from the same object category, and doesn’t have to arrive from the driving video.I wanted to explore how well this model works on some virtually designed faces of game characters. The authors have shared its code and an easy-to-use Google Colab notebook to test this out. Here’s how their trained model looks when tested on different characters from the game Grand Theft Auto.As you can see, it is extremely easy to create life-like animations with this AI, and I think it will be used by almost every game artist for creating facial animations in games. Moreover, in order to perform Mo-Cap with this technique, all we need now is one camera and any average computer with a GPU and this AI will take care of the rest, making it extremely cheap and feasible for game animators to use this tech on a large scale. This is why I’m excited about the massive improvements that can be brought by this AI in the development of future games."
# 在文本上运行管道并打印结果
result = pipe_sum(text)
print(result)

Token indices sequence length is longer than the specified maximum sequence length for this model (758 > 512). Running this sequence through the model will result in indexing errors

print(result[0]['summary_text'])

import streamlit as st
############# Displaying images on the front end #################
st.set_page_config(page_title="Mockup for single page webapp",
                   page_icon='💻',
                   layout="centered",  #or wide
                   initial_sidebar_state="expanded",
                   menu_items={
                        'Get Help': 'https://docs.streamlit.io/library/api-reference',
                        'Report a bug': "https://www.extremelycoolapp.com/bug",
                        'About': "# This is a header. This is an *extremely* cool app!"}
                        )
# Load image placeholder from the web
st.image('./images/header.png', width=750)
# Set a Descriptive Title
st.title("My AI Summarizer")
st.divider()
your_future_text = "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Cras rhoncus massa sit amet est congue dapibus. Duis dictum ac nulla sit amet sollicitudin. In non metus ac neque vehicula egestas. Vestibulum quis justo id enim vestibulum venenatis. Cras gravida ex vitae dignissim suscipit. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Duis efficitur, lorem ut fringilla commodo, lacus orci lobortis turpis, sit amet consequat ante diam ut libero."
st.text_area('Summarized text', your_future_text, 
              height = 150, key = 'result')

# Set 2 colums to make the Buttons wider
col1, col2 = st.columns(2)
btn1 = col1.button(" :star: Click ME ", use_container_width=True, type="secondary")
btn2 = col2.button(" :smile: Click ME ", use_container_width=True, type="primary")
if btn1:
    st.warning('You pressed the wrong one!', icon="⚠️")
if btn2:
    st.success('Good Choice!', icon="⚠️")  
st.divider()

########### GUI IMPORTS ################
import streamlit as st
import ssl
############# Displaying images on the front end #################
st.set_page_config(page_title="Summarize and Talk ot your Text",
                   page_icon='📖',
                   layout="centered",  #or wide
                   initial_sidebar_state="expanded",
                   menu_items={
                        'Get Help': 'https://docs.streamlit.io/library/api-reference',
                        'Report a bug': "https://www.extremelycoolapp.com/bug",
                        'About': "# This is a header. This is an AI text summarizer!"
                                },
                   )
########### SSL FOR PROXY ##############
ssl._create_default_https_context = ssl._create_unverified_context
#### IMPORTS FOR AI PIPELINES ###############
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers import pipeline
from transformers import AutoModel, T5Tokenizer, T5Model
from transformers import T5ForConditionalGeneration
from langchain.llms import HuggingFacePipeline
import torch
import datetime
#############################################################################
#               SIMPLE TEXT2TEXT GENERATION INFERENCE
#           checkpoint = "./models/LaMini-Flan-T5-783M.bin" 
# ###########################################################################
checkpoint = "./model/"  # 实际上是 LaMini-Flan-T5-248M

######################################################################
#     SUMMARIZATION FROM TEXT STRING WITH HUGGINGFACE PIPELINE       #
######################################################################
def AI_SummaryPL(checkpoint, text, chunks, overlap):
    """
    checkpoint is in the format of relative path
    example:  checkpoint = "/content/model/"  #it is actually LaMini-Flan-T5-248M   #tested fine
    text it is either a long string or a input long string or a loaded document into string
    chunks: integer, lenght of the chunks splitting
    ovelap: integer, overlap for cor attention and focus retreival
    RETURNS full_summary (str), delta(str) and reduction(str)
    post_summary14 = AI_SummaryPL(LaMini,doc2,3700,500)
    USAGE EXAMPLE:
    post_summary, post_time, post_percentage = AI_SummaryPL(LaMini,originalText,3700,500)
    """
    from langchain.text_splitter import RecursiveCharacterTextSplitter
    text_splitter = RecursiveCharacterTextSplitter(
        # 设置一个非常小的块大小，只是为了显示
        chunk_size = chunks,
        chunk_overlap  = overlap,
        length_function = len,
    )
    texts = text_splitter.split_text(text)
    checkpoint = checkpoint
    tokenizer = T5Tokenizer.from_pretrained(checkpoint)
    base_model = T5ForConditionalGeneration.from_pretrained(checkpoint,
                                                        device_map='auto',
                                                        torch_dtype=torch.float32)
    ### INITIALIZING PIPELINE
    pipe_sum = pipeline('summarization', 
                        model = base_model,
                        tokenizer = tokenizer,
                        max_length = 350, 
                        min_length = 25
                        )
    ## START TIMER
    start = datetime.datetime.now()  
    ## START CHUNKING
    full_summary = ''
    for cnk in range(len(texts)):
      result = pipe_sum(texts[cnk])
      full_summary = full_summary + ' '+ result[0]['summary_text']
    stop = datetime.datetime.now()  
    ## TIMER STOPPED AND RETURN DURATION
    delta = stop-start  
    ### Calculating Summarization PERCENTAGE
    reduction = '{:.1%}'.format(len(full_summary)/len(text))
    print(f"Completed in {delta}")
    print(f"Reduction percentage: ", reduction)
    return full_summary, delta, reduction

from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(
    # 设置一个非常小的块大小，只是为了显示
    chunk_size = chunks,
    chunk_overlap  = overlap,
    length_function = len,
)
texts = text_splitter.split_text(text)

## 开始组块
full_summary = ''
for cnk in range(len(texts)):
   result = pipe_sum(texts[cnk])
   full_summary = full_summary + ' '+ result[0]['summary_text']

### HEADER section
st.image('./images/header.png', width=750)
st.title("My AI Summarizer")
st.divider()
title = st.text_area('Insert here your Copy/Paste text', "", height = 350, key = 'copypaste')
btt = st.button("1. Start Summarization")
txt = st.empty()
timedelta = st.empty()
text_lenght = st.empty()
redux_bar = st.empty()
st.divider()
down_title = st.empty()
down_btn = st.button('2. Download Summarization') 
text_summary = ''

def start_sum(text):
    if st.session_state.copypaste == "":
        st.warning('You need to paste some text...', icon="⚠️")
    else:
        with st.spinner('Initializing pipelines...'):
            st.success(' AI process started', icon="🤖")
            print("Starting AI pipelines")
            text_summary, duration, reduction = AI_SummaryPL(LaMini,text,3700,500)
        txt.text_area('Summarized text', text_summary, height = 350, key='final')
        timedelta.write(f'Completed in {duration}')
        text_lenght.markdown(f"Initial length = {len(text.split(' '))} words / summarization = **{len(text_summary.split(' '))} words**")
        redux_bar.progress(len(text_summary)/len(text), f'Reduction: **{reduction}**')
        down_title.markdown(f"## Download your text Summarization")

if btt:
    start_sum(st.session_state.copypaste)
if down_btn:
    def savefile(generated_summary, filename):
        st.write("Download in progress...")
        with open(filename, 'w') as t:
            t.write(generated_summary)
        t.close()
        st.success(f'AI Summarization saved in {filename}', icon="✅")
    savefile(st.session_state.final, 'text_summarization.txt')
    txt.text_area('Summarized text', st.session_state.final, height = 350)

streamlit run AI-TextSummarizer.py

custom_style = """
    <style>
        .stButton>button {
            border-radius:0px;
            padding:5px 20px;
        }
        .stTextArea textarea {
            border-radius:0px;
            padding:5px;
            line-height:1.6
        }
        .stTextArea .st-br{
            border-radius:0px;
        }
        .stTextArea>label p{
            line-height:2
        }
        .block-container{
            max-width:780px
        }
    </style>
"""
st.markdown(custom_style, unsafe_allow_html=True)