Duplicate from shibing624/ChatPDF

Co-authored-by: Ming Xu (徐明) <[email protected]>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

LICENSE

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README.md

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+---
+title: ChatPDF
+emoji: 😁
+colorFrom: blue
+colorTo: blue
+sdk: gradio
+sdk_version: 3.24.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+duplicated_from: shibing624/ChatPDF
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+# -*- coding: utf-8 -*-
+"""
+@author:XuMing([email protected])
+@description:
+modified from https://github.com/imClumsyPanda/langchain-ChatGLM/blob/master/webui.py
+"""
+import argparse
+import hashlib
+import os
+import shutil
+
+import gradio as gr
+from loguru import logger
+
+from chatpdf import ChatPDF
+
+pwd_path = os.path.abspath(os.path.dirname(__file__))
+
+CONTENT_DIR = os.path.join(pwd_path, "content")
+logger.info(f"CONTENT_DIR: {CONTENT_DIR}")
+VECTOR_SEARCH_TOP_K = 3
+MAX_INPUT_LEN = 2048
+
+embedding_model_dict = {
+    "text2vec-base": "shibing624/text2vec-base-chinese",
+    "text2vec-multilingual": "shibing624/text2vec-base-multilingual",
+    "text2vec-large": "GanymedeNil/text2vec-large-chinese",
+    "sentence-transformers": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+    "ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
+    "ernie-base": "nghuyong/ernie-3.0-base-zh",
+}
+
+# supported LLM models
+llm_model_dict = {
+    "llama-2-7b": "LinkSoul/Chinese-Llama-2-7b-4bit",
+    "baichuan-13b-chat": "baichuan-inc/Baichuan-13B-Chat",
+    "chatglm-6b-int4-qe": "THUDM/chatglm-6b-int4-qe",
+    "chatglm-2-6b": "THUDM/chatglm2-6b",
+    "chatglm-2-6b-int4": "THUDM/chatglm2-6b-int4",
+    "chatglm-6b-int4": "THUDM/chatglm-6b-int4",
+    "chatglm-6b": "THUDM/chatglm-6b",
+    "llama-7b": "shibing624/chinese-alpaca-plus-7b-hf",
+    "llama-13b": "shibing624/chinese-alpaca-plus-13b-hf",
+}
+
+llm_model_dict_list = list(llm_model_dict.keys())
+embedding_model_dict_list = list(embedding_model_dict.keys())
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--sim_model", type=str, default="shibing624/text2vec-base-chinese")
+parser.add_argument("--gen_model_type", type=str, default="llama")
+parser.add_argument("--gen_model", type=str, default="LinkSoul/Chinese-Llama-2-7b-4bit")
+parser.add_argument("--lora_model", type=str, default=None)
+parser.add_argument("--device", type=str, default="cpu")
+parser.add_argument("--int4", action='store_true', help="use int4 quantization")
+parser.add_argument("--int8", action='store_true', help="use int8 quantization")
+args = parser.parse_args()
+print(args)
+
+model = None
+
+
+def get_file_list():
+    if not os.path.exists("content"):
+        return []
+    return [f for f in os.listdir("content") if
+            f.endswith(".txt") or f.endswith(".pdf") or f.endswith(".docx") or f.endswith(".md")]
+
+
+file_list = get_file_list()
+
+
+def upload_file(file):
+    if not os.path.exists(CONTENT_DIR):
+        os.mkdir(CONTENT_DIR)
+    filename = os.path.basename(file.name)
+    shutil.move(file.name, os.path.join(CONTENT_DIR, filename))
+    # file_list首位插入新上传的文件
+    file_list.insert(0, filename)
+    return gr.Dropdown.update(choices=file_list, value=filename)
+
+
+def parse_text(text):
+    """copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""
+    lines = text.split("\n")
+    lines = [line for line in lines if line != ""]
+    count = 0
+    for i, line in enumerate(lines):
+        if "```" in line:
+            count += 1
+            items = line.split('`')
+            if count % 2 == 1:
+                lines[i] = f'<pre><code class="language-{items[-1]}">'
+            else:
+                lines[i] = f'<br></code></pre>'
+        else:
+            if i > 0:
+                if count % 2 == 1:
+                    line = line.replace("`", "\`")
+                    line = line.replace("<", "&lt;")
+                    line = line.replace(">", "&gt;")
+                    line = line.replace(" ", "&nbsp;")
+                    line = line.replace("*", "&ast;")
+                    line = line.replace("_", "&lowbar;")
+                    line = line.replace("-", "&#45;")
+                    line = line.replace(".", "&#46;")
+                    line = line.replace("!", "&#33;")
+                    line = line.replace("(", "&#40;")
+                    line = line.replace(")", "&#41;")
+                    line = line.replace("$", "&#36;")
+                lines[i] = "<br>" + line
+    text = "".join(lines)
+    return text
+
+
+def get_answer(query, index_path, history, topn=VECTOR_SEARCH_TOP_K, max_input_size=1024, only_chat=False):
+    if model is None:
+        return [None, "模型还未加载"], query
+    if index_path and not only_chat:
+        if not model.sim_model.corpus_embeddings:
+            model.load_index(index_path)
+        response, reference_results = model.predict(
+            query=query, topn=topn, context_len=max_input_size)
+
+        logger.debug(f"query: {query}, response with content: {response}")
+        for i in range(len(reference_results)):
+            r = reference_results[i]
+            response += f"\n{r.strip()}"
+        response = parse_text(response)
+        history = history + [[query, response]]
+    else:
+        # 未加载文件，仅返回生成模型结果
+        instruction = """[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
+
+                    If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n{} [/INST]"""
+        if args.gen_model_type == "llama":
+            query = instruction.format(query)
+        model.history.append([query, ''])
+        response = ""
+        for new_text in model.stream_generate_answer(query, context_len=max_input_size):
+            response += new_text
+        response = response.strip()
+        model.history[-1][1] = response
+        response = parse_text(response)
+        history = history + [[query, response]]
+        logger.debug(f"query: {query}, response: {response}")
+    return history, ""
+
+
+def update_status(history, status):
+    history = history + [[None, status]]
+    logger.info(status)
+    return history
+
+
+def reinit_model(llm_model, embedding_model, history):
+    try:
+        global model
+        if model is not None:
+            del model
+ 
+        model = ChatPDF(
+            sim_model_name_or_path=embedding_model_dict.get(
+                embedding_model,
+                "shibing624/text2vec-base-chinese"
+            ),
+            gen_model_type=llm_model.split('-')[0],
+            gen_model_name_or_path=llm_model_dict.get(llm_model, "LinkSoul/Chinese-Llama-2-7b-4bit"),
+            lora_model_name_or_path=None,
+        )
+
+        model_status = """模型已成功重新加载，请选择文件后点击"加载文件"按钮"""
+    except Exception as e:
+        model = None
+        logger.error(e)
+        model_status = """模型未成功重新加载，请重新选择后点击"加载模型"按钮"""
+    return history + [[None, model_status]]
+
+
+def get_file_hash(fpath):
+    return hashlib.md5(open(fpath, 'rb').read()).hexdigest()
+
+
+def get_vector_store(filepath, history, embedding_model):
+    logger.info(filepath, history)
+    index_path = None
+    file_status = ''
+    if model is not None:
+
+        local_file_path = os.path.join(CONTENT_DIR, filepath)
+
+        local_file_hash = get_file_hash(local_file_path)
+        index_file_name = f"{filepath}.{embedding_model}.{local_file_hash}.index.json"
+
+        local_index_path = os.path.join(CONTENT_DIR, index_file_name)
+
+        if os.path.exists(local_index_path):
+            model.load_index(local_index_path)
+            index_path = local_index_path
+            file_status = "文件已成功加载，请开始提问"
+
+        elif os.path.exists(local_file_path):
+            model.load_doc_files(local_file_path)
+            model.save_index(local_index_path)
+            index_path = local_index_path
+            if index_path:
+                file_status = "文件索引并成功加载，请开始提问"
+            else:
+                file_status = "文件未成功加载，请重新上传文件"
+    else:
+        file_status = "模型未完成加载，请先在加载模型后再导入文件"
+
+    return index_path, history + [[None, file_status]]
+
+
+def reset_chat(chatbot, state):
+    return None, None
+
+
+def change_max_input_size(input_size):
+    if model is not None:
+        model.max_input_size = input_size
+    return
+
+
+block_css = """.importantButton {
+    background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important;
+    border: none !important;
+}
+.importantButton:hover {
+    background: linear-gradient(45deg, #ff00e0,#8500ff, #6e00ff) !important;
+    border: none !important;
+}"""
+
+webui_title = """
+# 🎉ChatPDF WebUI🎉
+Link in: [https://github.com/shibing624/ChatPDF](https://github.com/shibing624/ChatPDF)  PS: 2核CPU 16G内存机器，约2min一条😭
+"""
+
+init_message = """欢迎使用 ChatPDF Web UI，可以直接提问或上传文件后提问 """
+
+with gr.Blocks(css=block_css) as demo:
+    index_path, file_status, model_status = gr.State(""), gr.State(""), gr.State("")
+    gr.Markdown(webui_title)
+    with gr.Row():
+        with gr.Column(scale=2):
+            chatbot = gr.Chatbot([[None, init_message], [None, None]],
+                                 elem_id="chat-box",
+                                 show_label=False).style(height=700)
+            query = gr.Textbox(show_label=False,
+                               placeholder="请输入提问内容，按回车进行提交",
+                               ).style(container=False)
+            clear_btn = gr.Button('🔄Clear!', elem_id='clear').style(full_width=True)
+        with gr.Column(scale=1):
+            llm_model = gr.Radio(llm_model_dict_list,
+                                 label="LLM 模型",
+                                 value=list(llm_model_dict.keys())[0],
+                                 interactive=True)
+            embedding_model = gr.Radio(embedding_model_dict_list,
+                                       label="Embedding 模型",
+                                       value=embedding_model_dict_list[0],
+                                       interactive=True)
+
+            load_model_button = gr.Button("重新加载模型")
+
+            with gr.Row():
+                only_chat = gr.Checkbox(False, label="不加载文件(纯聊天)")
+
+            with gr.Row():
+                topn = gr.Slider(1, 100, 20, step=1, label="最大搜索数量")
+                max_input_size = gr.Slider(512, 4096, MAX_INPUT_LEN, step=10, label="摘要最大长度")
+            with gr.Tab("select"):
+                selectFile = gr.Dropdown(
+                    file_list,
+                    label="content file",
+                    interactive=True,
+                    value=file_list[0] if len(file_list) > 0 else None
+                )
+            with gr.Tab("upload"):
+                file = gr.File(
+                    label="content file",
+                    file_types=['.txt', '.md', '.docx', '.pdf']
+                )
+            load_file_button = gr.Button("加载文件")
+    max_input_size.change(
+        change_max_input_size,
+        inputs=max_input_size
+    )
+    load_model_button.click(
+        reinit_model,
+        show_progress=True,
+        inputs=[llm_model, embedding_model, chatbot],
+        outputs=chatbot
+    )
+    # 将上传的文件保存到content文件夹下,并更新下拉框
+    file.upload(upload_file, inputs=file, outputs=selectFile)
+    load_file_button.click(
+        get_vector_store,
+        show_progress=True,
+        inputs=[selectFile, chatbot, embedding_model],
+        outputs=[index_path, chatbot],
+    )
+    query.submit(
+        get_answer,
+        [query, index_path, chatbot, topn, max_input_size, only_chat],
+        [chatbot, query],
+    )
+    clear_btn.click(reset_chat, [chatbot, query], [chatbot, query])
+
+demo.queue(concurrency_count=3).launch()

chatpdf.py

@@ -0,0 +1,296 @@
+# -*- coding: utf-8 -*-
+"""
+@author:XuMing([email protected])
+@description: 
+"""
+import argparse
+from threading import Thread
+from typing import Union, List
+
+import torch
+from loguru import logger
+from peft import PeftModel
+from similarities import Similarity
+from transformers import (
+    AutoModel,
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BloomForCausalLM,
+    BloomTokenizerFast,
+    LlamaTokenizer,
+    LlamaForCausalLM,
+    TextIteratorStreamer,
+    GenerationConfig,
+)
+
+MODEL_CLASSES = {
+    "bloom": (BloomForCausalLM, BloomTokenizerFast),
+    "chatglm": (AutoModel, AutoTokenizer),
+    "llama": (LlamaForCausalLM, LlamaTokenizer),
+    "baichuan": (AutoModelForCausalLM, AutoTokenizer),
+    "auto": (AutoModelForCausalLM, AutoTokenizer),
+}
+
+LLAMA_TEMPLATE = """[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
+
+If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n"""
+
+PROMPT_TEMPLATE = """基于以下已知信息，简洁和专业的来回答用户的问题。
+如果无法从中得到答案，请说 "根据已知信息无法回答该问题" 或 "没有提供足够的相关信息"，不允许在答案中添加编造成分，答案请使用中文。
+
+已知内容:
+{context_str}
+
+问题:
+{query_str}
+"""
+
+
+class ChatPDF:
+    def __init__(
+            self,
+            sim_model_name_or_path: str = "shibing624/text2vec-base-chinese",
+            gen_model_type: str = "baichuan",
+            gen_model_name_or_path: str = "baichuan-inc/Baichuan-13B-Chat",
+            lora_model_name_or_path: str = None,
+            device: str = None,
+            int8: bool = False,
+            int4: bool = False,
+    ):
+        default_device = torch.device('cpu')
+        if torch.cuda.is_available():
+            default_device = torch.device(0)
+        elif torch.backends.mps.is_available():
+            default_device = 'mps'
+        self.device = device or default_device
+        self.sim_model = Similarity(model_name_or_path=sim_model_name_or_path, device=self.device)
+        self.gen_model, self.tokenizer = self._init_gen_model(
+            gen_model_type,
+            gen_model_name_or_path,
+            peft_name=lora_model_name_or_path,
+            int8=int8,
+            int4=int4,
+        )
+        self.history = []
+        self.doc_files = None
+
+    def _init_gen_model(
+            self,
+            gen_model_type: str,
+            gen_model_name_or_path: str,
+            peft_name: str = None,
+            int8: bool = False,
+            int4: bool = False,
+    ):
+        """Init generate model."""
+        if int8 or int4:
+            device_map = None
+        else:
+            device_map = "auto"
+        model_class, tokenizer_class = MODEL_CLASSES[gen_model_type]
+        tokenizer = tokenizer_class.from_pretrained(gen_model_name_or_path, trust_remote_code=True)
+        model = model_class.from_pretrained(
+            gen_model_name_or_path,
+            load_in_8bit=int8 if gen_model_type not in ['baichuan', 'chatglm'] else False,
+            load_in_4bit=int4 if gen_model_type not in ['baichuan', 'chatglm'] else False,
+            torch_dtype=torch.float16,
+            low_cpu_mem_usage=True,
+            device_map=device_map,
+            trust_remote_code=True,
+        )
+        if self.device == torch.device('cpu'):
+            model.float()
+        if gen_model_type in ['baichuan', 'chatglm']:
+            if int4:
+                model = model.quantize(4).cuda()
+            elif int8:
+                model = model.quantize(8).cuda()
+        try:
+            model.generation_config = GenerationConfig.from_pretrained(gen_model_name_or_path, trust_remote_code=True)
+        except Exception as e:
+            logger.warning(f"Failed to load generation config from {gen_model_name_or_path}, {e}")
+        if peft_name:
+            model = PeftModel.from_pretrained(
+                model,
+                peft_name,
+                torch_dtype=torch.float16,
+            )
+            logger.info(f"Loaded peft model from {peft_name}")
+        model.eval()
+        return model, tokenizer
+
+    @torch.inference_mode()
+    def stream_generate_answer(
+            self,
+            prompt,
+            max_new_tokens=512,
+            temperature=0.7,
+            repetition_penalty=1.0,
+            context_len=2048
+    ):
+        streamer = TextIteratorStreamer(self.tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
+        input_ids = self.tokenizer(prompt).input_ids
+        max_src_len = context_len - max_new_tokens - 8
+        input_ids = input_ids[-max_src_len:]
+        generation_kwargs = dict(
+            input_ids=torch.as_tensor([input_ids]).to(self.device),
+            max_new_tokens=max_new_tokens,
+            temperature=temperature,
+            repetition_penalty=repetition_penalty,
+            streamer=streamer,
+        )
+        thread = Thread(target=self.gen_model.generate, kwargs=generation_kwargs)
+        thread.start()
+
+        yield from streamer
+
+    def load_doc_files(self, doc_files: Union[str, List[str]]):
+        """Load document files."""
+        if isinstance(doc_files, str):
+            doc_files = [doc_files]
+        for doc_file in doc_files:
+            if doc_file.endswith('.pdf'):
+                corpus = self.extract_text_from_pdf(doc_file)
+            elif doc_file.endswith('.docx'):
+                corpus = self.extract_text_from_docx(doc_file)
+            elif doc_file.endswith('.md'):
+                corpus = self.extract_text_from_markdown(doc_file)
+            else:
+                corpus = self.extract_text_from_txt(doc_file)
+            self.sim_model.add_corpus(corpus)
+        self.doc_files = doc_files
+
+    @staticmethod
+    def extract_text_from_pdf(file_path: str):
+        """Extract text content from a PDF file."""
+        import PyPDF2
+        contents = []
+        with open(file_path, 'rb') as f:
+            pdf_reader = PyPDF2.PdfReader(f)
+            for page in pdf_reader.pages:
+                page_text = page.extract_text().strip()
+                raw_text = [text.strip() for text in page_text.splitlines() if text.strip()]
+                new_text = ''
+                for text in raw_text:
+                    new_text += text
+                    if text[-1] in ['.', '!', '?', '。', '！', '？', '…', ';', '；', ':', '：', '”', '’', '）', '】', '》', '」',
+                                    '』', '〕', '〉', '》', '〗', '〞', '〟', '»', '"', "'", ')', ']', '}']:
+                        contents.append(new_text)
+                        new_text = ''
+                if new_text:
+                    contents.append(new_text)
+        return contents
+
+    @staticmethod
+    def extract_text_from_txt(file_path: str):
+        """Extract text content from a TXT file."""
+        contents = []
+        with open(file_path, 'r', encoding='utf-8') as f:
+            contents = [text.strip() for text in f.readlines() if text.strip()]
+        return contents
+
+    @staticmethod
+    def extract_text_from_docx(file_path: str):
+        """Extract text content from a DOCX file."""
+        import docx
+        document = docx.Document(file_path)
+        contents = [paragraph.text.strip() for paragraph in document.paragraphs if paragraph.text.strip()]
+        return contents
+
+    @staticmethod
+    def extract_text_from_markdown(file_path: str):
+        """Extract text content from a Markdown file."""
+        import markdown
+        from bs4 import BeautifulSoup
+        with open(file_path, 'r', encoding='utf-8') as f:
+            markdown_text = f.read()
+        html = markdown.markdown(markdown_text)
+        soup = BeautifulSoup(html, 'html.parser')
+        contents = [text.strip() for text in soup.get_text().splitlines() if text.strip()]
+        return contents
+
+    @staticmethod
+    def _add_source_numbers(lst):
+        """Add source numbers to a list of strings."""
+        return [f'[{idx + 1}]\t "{item}"' for idx, item in enumerate(lst)]
+
+    def predict(
+            self,
+            query: str,
+            topn: int = 5,
+            max_length: int = 512,
+            context_len: int = 2048,
+            temperature: float = 0.7,
+            do_print: bool = True,
+    ):
+        """Query from corpus."""
+
+        sim_contents = self.sim_model.most_similar(query, topn=topn)
+
+        reference_results = []
+        for query_id, id_score_dict in sim_contents.items():
+            for corpus_id, s in id_score_dict.items():
+                reference_results.append(self.sim_model.corpus[corpus_id])
+        if not reference_results:
+            return '没有提供足够的相关信息', reference_results
+        reference_results = self._add_source_numbers(reference_results)
+        context_str = '\n'.join(reference_results)[:(context_len - len(PROMPT_TEMPLATE))]
+
+        prompt = PROMPT_TEMPLATE.format(context_str=context_str, query_str=query)
+        self.history.append([prompt, ''])
+        response = ""
+        for new_text in self.stream_generate_answer(
+                prompt,
+                max_new_tokens=max_length,
+                temperature=temperature,
+                context_len=context_len,
+        ):
+            response += new_text
+            if do_print:
+                print(new_text, end="", flush=True)
+        if do_print:
+            print("", flush=True)
+        response = response.strip()
+        self.history[-1][1] = response
+        return response, reference_results
+
+    def save_index(self, index_path=None):
+        """Save model."""
+        if index_path is None:
+            index_path = '.'.join(self.doc_files.split('.')[:-1]) + '_index.json'
+        self.sim_model.save_index(index_path)
+
+    def load_index(self, index_path=None):
+        """Load model."""
+        if index_path is None:
+            index_path = '.'.join(self.doc_files.split('.')[:-1]) + '_index.json'
+        self.sim_model.load_index(index_path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--sim_model", type=str, default="shibing624/text2vec-base-chinese")
+    parser.add_argument("--gen_model_type", type=str, default="baichuan")
+    parser.add_argument("--gen_model", type=str, default="baichuan-inc/Baichuan-13B-Chat")
+    parser.add_argument("--lora_model", type=str, default=None)
+    parser.add_argument("--device", type=str, default=None)
+    parser.add_argument("--int4", action='store_true', help="use int4 quantization")
+    parser.add_argument("--int8", action='store_true', help="use int8 quantization")
+    args = parser.parse_args()
+    print(args)
+    m = ChatPDF(
+        sim_model_name_or_path=args.sim_model,
+        gen_model_type=args.gen_model_type,
+        gen_model_name_or_path=args.gen_model,
+        lora_model_name_or_path=args.lora_model,
+        device=args.device,
+        int4=args.int4,
+        int8=args.int8
+    )
+    m.load_doc_files(doc_files='sample.pdf')
+    m.predict('自然语言中的非平行迁移是指什么？', do_print=True)
+    while True:
+        query = input("> ")
+        if query == 'exit':
+            break
+        m.predict(query, do_print=True)

requirements.txt

@@ -0,0 +1,16 @@
+gradio==3.26.0
+protobuf
+mdtex2html
+bitsandbytes
+similarities
+sentencepiece
+textgen>=1.0.1
+markdown
+PyPDF2
+python-docx
+pandas
+cpm-kernels
+torch>=2.0
+loguru
+accelerate
+transformers>=4.30.2