From 99d962c2f8f8cf8ae32ef856475ee1f4ed1c04aa Mon Sep 17 00:00:00 2001 From: EduCoderStudent0014 Date: Mon, 14 Aug 2023 15:53:33 +0800 Subject: [PATCH] =?UTF-8?q?'=E6=8F=8F=E8=BF=B0'?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 18 + LICENSE | 29 + README.md | 75 +- UI工具.py | 459 ++ UI界面.py | 464 ++ args.py | 48 + demo/果蔬识别.png | Bin 0 -> 332334 bytes demo/自由对话.png | Bin 0 -> 220403 bytes demo/菜单推荐--知识库.png | Bin 0 -> 269793 bytes demo/菜单推荐--自定义随机.png | Bin 0 -> 157680 bytes demo/菜单规划-启动-菜品.png | Bin 0 -> 234220 bytes demo/菜单规划-启动-食材.png | Bin 0 -> 352501 bytes demo/菜品识别.png | Bin 0 -> 708214 bytes requirements.txt | 18 + vectorstores/caidanguihua/index.pkl | Bin 0 -> 8461671 bytes .../caidanguihua/知识库太大上传不了 | 0 vectorstores/caidantuijian/index.pkl | Bin 0 -> 2359748 bytes .../caidantuijian/知识库太大上传不了 | 0 .../cchardet-2.1.7-cp310-cp310-win_amd64.whl | Bin 0 -> 118798 bytes 其他/conda环境.txt | Bin 0 -> 16820 bytes 其他/huggingface数据下载.py | 9 + 其他/huggingface模型下载.py | 59 + 其他/虫子1.py | 85 + 其他/虫子2.py | 62 + 数据处理.py | 123 + 模型.py | 58 + 模型数据/商户菜单/111.jpg | Bin 0 -> 196859 bytes 模型数据/商户菜单/小饭店菜单.pdf | Bin 0 -> 116373 bytes 模型数据/菜单推荐/菜单.csv | 6065 +++++++++++++++++ 模型数据/菜单翻译.xlsx | Bin 0 -> 125784 bytes 模型数据/菜单规划/中国菜系1.csv | 1614 +++++ 模型数据/菜单规划/外国菜谱1.csv | 962 +++ 模型数据/菜单规划/用餐场景1.csv | 3354 +++++++++ 模型数据/菜单规划/菜谱1.csv | 3034 +++++++++ 模型数据/菜品清单.txt | 209 + 35 files changed, 16744 insertions(+), 1 deletion(-) create mode 100644 .gitignore create mode 100644 LICENSE create mode 100644 UI工具.py create mode 100644 UI界面.py create mode 100644 args.py create mode 100644 demo/果蔬识别.png create mode 100644 demo/自由对话.png create mode 100644 demo/菜单推荐--知识库.png create mode 100644 demo/菜单推荐--自定义随机.png create mode 100644 demo/菜单规划-启动-菜品.png create mode 100644 demo/菜单规划-启动-食材.png create mode 100644 demo/菜品识别.png create mode 100644 requirements.txt create mode 100644 vectorstores/caidanguihua/index.pkl create mode 100644 vectorstores/caidanguihua/知识库太大上传不了 create mode 100644 vectorstores/caidantuijian/index.pkl create mode 100644 vectorstores/caidantuijian/知识库太大上传不了 create mode 100644 其他/cchardet-2.1.7-cp310-cp310-win_amd64.whl create mode 100644 其他/conda环境.txt create mode 100644 其他/huggingface数据下载.py create mode 100644 其他/huggingface模型下载.py create mode 100644 其他/虫子1.py create mode 100644 其他/虫子2.py create mode 100644 数据处理.py create mode 100644 模型.py create mode 100644 模型数据/商户菜单/111.jpg create mode 100644 模型数据/商户菜单/小饭店菜单.pdf create mode 100644 模型数据/菜单推荐/菜单.csv create mode 100644 模型数据/菜单翻译.xlsx create mode 100644 模型数据/菜单规划/中国菜系1.csv create mode 100644 模型数据/菜单规划/外国菜谱1.csv create mode 100644 模型数据/菜单规划/用餐场景1.csv create mode 100644 模型数据/菜单规划/菜谱1.csv create mode 100644 模型数据/菜品清单.txt diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..5d947ca --- /dev/null +++ b/.gitignore @@ -0,0 +1,18 @@ +# Build and Release Folders +bin-debug/ +bin-release/ +[Oo]bj/ +[Bb]in/ + +# Other files and folders +.settings/ + +# Executables +*.swf +*.air +*.ipa +*.apk + +# Project files, i.e. `.project`, `.actionScriptProperties` and `.flexProperties` +# should NOT be excluded as they contain compiler settings and other important +# information for Eclipse / Flash Builder. diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..8844aef --- /dev/null +++ b/LICENSE @@ -0,0 +1,29 @@ +BSD 3-Clause License + +Copyright (c) 2023, Gs-song +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/README.md b/README.md index 3c8f2a3..4ea0b74 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,75 @@ -# SmartMenu +# 菜单推荐系统 +### 介绍 + +本项目在Huggingface的基础上实现Chatglm2的本地部署,再结合Langchain和本地知识库实现了菜单的智能推荐,拥有菜单推荐、菜品识别、菜单规划、自由对话几种功能。NLP模型和Emmbeding模型可以在设置里自行更换,但需满足Huggingface的要求。最后,通过Gradio实现UI界面的部署。 +### 使用说明 + +使用前建议使用conda创建新的虚拟环境,乙方后面安装依赖包时对原有环境破坏,另外建议python>=3.10,torch>=2.0 。可以依次运行下面的命令。 + +``` +conda create -n caidan python=3.10 + +pip install -r requirements.txt +``` + +安装好运行环境后在虚拟环境下运行下面代码启动UI界面。 + +``` +python .\UI界面.py +``` + +### 功能简介 + +##### 菜单推荐 + +基于知识库的菜单推荐:根据输入的饮食喜好,结合本地向量化的知识库进行推荐。 + +基于菜单的推荐:根据输入的饮食喜好,结合上传的文本或图片菜单进行推荐。 + +大模型推荐:根据输入的饮食喜好,模型自由发挥进行推荐。 + +自定义推荐:自定义随机范围,让机器随机选择。 + +##### 智能识别 + +菜品识别: 用户上传照片,模型返回图片中可能的菜品名称。 + +果蔬识别:用户上传照片,模型返回图片中的果蔬名称。 + +补充:菜品识别和果蔬识别都是使用百度的API,需要该功能的可以去申请。没使用本地模型的是为了节省显存,追求性能且现存足够的需自行修改和微调。 + +##### 菜单规划 + +菜单规划有两种模式,可以结合使用。 + +1. 根据输入的食材,返回可以制作的菜品名称。 +2. 根据输入的菜品名称,返回制作的过程和细节。 + +##### 自由对话 + +直接对接NLP模型,可以自由对话。 + +### demo展示 + +#### 菜单推荐 + +![输入图片说明](demo/%E8%8F%9C%E5%8D%95%E6%8E%A8%E8%8D%90--%E7%9F%A5%E8%AF%86%E5%BA%93.png) +![输入图片说明](demo/%E8%8F%9C%E5%8D%95%E6%8E%A8%E8%8D%90--%E8%87%AA%E5%AE%9A%E4%B9%89%E9%9A%8F%E6%9C%BA.png) + +#### 菜单规划 + +![输入图片说明](demo/%E8%8F%9C%E5%8D%95%E8%A7%84%E5%88%92-%E5%90%AF%E5%8A%A8-%E8%8F%9C%E5%93%81.png) +![输入图片说明](demo/%E8%8F%9C%E5%8D%95%E8%A7%84%E5%88%92-%E5%90%AF%E5%8A%A8-%E9%A3%9F%E6%9D%90.png) + +#### 智能识别 + +![输入图片说明](demo/%E8%8F%9C%E5%93%81%E8%AF%86%E5%88%AB.png) +![输入图片说明](demo/%E6%9E%9C%E8%94%AC%E8%AF%86%E5%88%AB.png) + +#### 自由对话 + +![输入图片说明](demo/%E8%87%AA%E7%94%B1%E5%AF%B9%E8%AF%9D.png) + +### 最后 +如有任何问题或者想参与到项目中可以联系我,QQ邮箱 1906217418@qq.com diff --git a/UI工具.py b/UI工具.py new file mode 100644 index 0000000..46cc115 --- /dev/null +++ b/UI工具.py @@ -0,0 +1,459 @@ +# coding=utf-8 +from 数据处理 import embed_data,dBgx +import random as rand +from langchain import PromptTemplate, LLMChain +from 模型 import CustomLLM +from args import args +import torch +from transformers import AutoTokenizer, AutoModel,AutoConfig +from langchain.text_splitter import CharacterTextSplitter +import re +from typing import List +from langchain.document_loaders.unstructured import UnstructuredFileLoader +from paddleocr import PaddleOCR +import os +import fitz + + +zhishiku_init1='''欢迎来到基于知识库的菜单推荐,模型的回答效果除了与模型能力有关外,还需注意创建知识库时根据数据选择合适的切分长度和重叠长度,喜好提交时尽量详细数据库里存在的内容''' +caidan_init0="""欢迎来到基于菜单的菜单推荐,询问前请先上传菜单""" +damodel_init0="""欢迎来到大模型随机推荐,输入你的喜好,模型将根据你的喜好随机推荐""" +zhinengshibie = '''欢迎使用智能识别,使用前请先去配置文件填写相关的api。 + api申请方法见:https://blog.csdn.net/2303_79001442/article/details/132093208 + 别问为什么不用本地模型,问就是显存不足。显存足够的可以考虑更换自己的模型。''' +def get_answer(query,chatbot): + + llm = CustomLLM() + db = embed_data(args.embeddings_model_name, + args.original_data_path, + args.preprocessed_data_path, + ) + retriever = db.as_retriever(search_kwargs={"k": args.topk_1}) + docs = retriever.get_relevant_documents(query) + prompt_template = """根据已知内容推荐几盘菜肴。已知内容: {adjective}""" + + prompt = PromptTemplate(template=prompt_template, input_variables=["adjective"]) + + llm_chain = LLMChain(prompt=prompt, llm=llm) + a = llm_chain.run(adjective=docs) + + b=[query,a] + chatbot.append(b) + + return chatbot, '' +from langchain.vectorstores import FAISS +from langchain.embeddings import HuggingFaceEmbeddings +def get_answer_caidan(query, chatbot): + embeddings = HuggingFaceEmbeddings(model_name=args.embeddings_model_name) + llm = CustomLLM() + caidan = load_caidan(args.path_caidan,sentence_size=args.chunk_size) # 加载菜单 + db = FAISS.from_documents(caidan, embeddings) + retriever = db.as_retriever(search_kwargs={"k": args.topk_1}) + docs = retriever.get_relevant_documents(query) + + prompt = """基于我提供的菜单信息和饮食喜好信息。简洁的回答我的问题。 + 菜单信息:{adjective} + 饮食喜好信息:{xihao} + 问题:{question}""" + question = '''请根据我的喜好从菜单中选出我可能喜欢的菜肴,答案中不允许出现菜单中没有的信息。''' + + prompt = PromptTemplate(template=prompt, input_variables=["adjective", "xihao", "question"]) + llm_chain = LLMChain(prompt=prompt, llm=llm) + a = llm_chain.run(adjective=docs, xihao=query, question=question) + b = [query, a] + chatbot.append(b) + + return chatbot, '' +def get_answer_model(query, chatbot): + + llm = CustomLLM() + prompt = """你现在是一位优秀的厨师,你要根据我的饮食喜好推荐几种美食。我的饮食喜好如下:{xihao}。回答只能展示美食的名字不需要过多的解释。""" + prompt = PromptTemplate(template=prompt, input_variables=["xihao"]) + llm_chain = LLMChain(prompt=prompt, llm=llm) + a = llm_chain.run(xihao=query) + b = [query, a] + chatbot.append(b) + + return chatbot, '' +def get_answer_guihua(query, chatbot,mode2): + llm = CustomLLM() + if mode2 == '启用': + db = embed_data(args.embeddings_model_name, + args.original_data_path_g, + args.preprocessed_data_path_g, + ) + retriever = db.as_retriever(search_kwargs={"k": args.topk_1}) + docs = retriever.get_relevant_documents(query) + prompt_template = """请参考以下的素材信息和已有食材为我推荐几种菜肴,并指出缺少的食材和具体的操作过程。 + 素材信息:{adjective}, + 已有食材:{shicai}""" + + prompt = PromptTemplate(template=prompt_template, input_variables=["adjective",'shicai']) + + llm_chain = LLMChain(prompt=prompt, llm=llm) + a = llm_chain.run(adjective=docs,shicai=query) + + b = [query, a] + chatbot.append(b) + + elif mode2 == '不启用': + prompt = """你是一位优秀的厨师,我将提供一些食材,你要根据食材制作几种不同的美味菜肴。提供的食材如下:{食材}。""" + prompt = PromptTemplate(template=prompt, input_variables=["食材"]) + llm_chain = LLMChain(prompt=prompt, llm=llm) + a = llm_chain.run(食材=query) + b = [query, a] + chatbot.append(b) + + return chatbot, '' +def get_answer_guihua2(query, chatbot,mode2): + llm = CustomLLM() + if mode2 == '启用': + db = embed_data(args.embeddings_model_name, + args.original_data_path_g, + args.preprocessed_data_path_g) + retriever = db.as_retriever(search_kwargs={"k": args.topk_1}) + docs = retriever.get_relevant_documents(query) + prompt_template = """请参考以下的素材信息回答{shicai}制作过程。 + 素材信息:{adjective}""" + + prompt = PromptTemplate(template=prompt_template, input_variables=["adjective",'shicai']) + + llm_chain = LLMChain(prompt=prompt, llm=llm) + a = llm_chain.run(adjective=docs,shicai=query) + + b = [query, a] + chatbot.append(b) + + elif mode2 == '不启用': + prompt = """你是一位优秀的厨师,我想制作一份{shicai},请问具体应该怎么做。""" + prompt = PromptTemplate(template=prompt, input_variables=["shicai"]) + llm_chain = LLMChain(prompt=prompt, llm=llm) + a = llm_chain.run(shicai=query) + b = [query, a] + chatbot.append(b) + + return chatbot, '' + +def ziyouduihau(query, chatbot): + payload = { + "temperature": args.temperature, + "top_k": args.topk, + } + model_config = AutoConfig.from_pretrained(args.pretrained_model_name, trust_remote_code=True) + model_config.update(payload) + + model = AutoModel.from_pretrained(args.pretrained_model_name, config=model_config,trust_remote_code=True).half().to(args.device) + tokenizer = AutoTokenizer.from_pretrained(args.pretrained_model_name, trust_remote_code=True) + model = model.eval() + prompt = query + response, history = model.chat(tokenizer, prompt, history=[]) + b = [query, response] + chatbot.append(b) + + return chatbot, '' +def change_data_path(data_path): + args.original_data_path = data_path +def change_data_path_g(data_path): + args.original_data_path = data_path +def change_P_data_path(data_path): + args.preprocessed_data_path = data_path +def change_P_data_path_g(data_path): + args.preprocessed_data_path_g = data_path +def change_emb_name(emb_name): + args.embeddings_model_name = emb_name +def change_search_top_k(search_top_k): + if search_top_k != '': + args.topk_1 = int(search_top_k) +def change_model_top_k(model_top_k): + if model_top_k != '': + args.topk = int(model_top_k) +def change_model_temperature(model_temperature): + args.temperature = float(model_temperature) +def change_model_name(model_name): + args.pretrained_model_name = model_name +def db_update_click1(chatbot): + + embeddings_model_name =args.embeddings_model_name + + original_data_path = args.original_data_path + preprocessed_data_path = args.preprocessed_data_path + + dBgx(original_data_path,preprocessed_data_path,embeddings_model_name) + + dbok = [None, '知识库已更新'] + chatbot.append(dbok) + return chatbot +def db_update_click(chatbot): + + embeddings_model_name = args.embeddings_model_name + + original_data_path = args.original_data_path_g + preprocessed_data_path = args.preprocessed_data_path_g + + dBgx(original_data_path,preprocessed_data_path,embeddings_model_name) + dbok = [None, '知识库已更新'] + chatbot.append(dbok) + return chatbot +def change_chunk_size(chunk_size): + args.chunk_size = chunk_size +def change_chunk_overlap(chunk_overlap): + args.chunk_overlap = chunk_overlap +def clean_memory(chatbot): + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.ipc_collect() + + elif torch.backends.mps.is_available(): + try: + from torch.mps import empty_cache + empty_cache() + except Exception as e: + print(e) + print("如果您使用的是 macOS 建议将 pytorch 版本升级至 2.0.0 或更高版本,以支持及时清理 torch 产生的内存占用。") + clmok = [None, '显存已清理'] + chatbot.append(clmok) + + return chatbot +def change_caidan_path(caidan): + args.path_caidan = caidan +def change_path_zidingyi(suijidz): + args.path_zidingyi = suijidz +def change_suiji_num(suiji_num): + args.num_select = suiji_num +def change_shitu_top_k(shitu_top_k): + args.top_num = shitu_top_k +def change_shitu_filter_threshold(shitu_filter_threshold): + args.filter_threshold = shitu_filter_threshold +def change_shitu_baike_num(shitu_baike_num): + args.baike_num = shitu_baike_num +def Random_selection1(chatbot): + content = [] + with open(args.path_zidingyi, 'r',encoding='utf-8') as file: + for line in file: + content.append(line.strip()) + data_len = len(content) + num_select = args.num_select + random_rows = rand.sample(range(data_len), num_select) + elements = [content[i] for i in random_rows] + shuchu='随机内容为:\n' + '\n '.join(elements) + b = [None, shuchu] + chatbot.append(b) + return chatbot +def Random_selection2(chatbot,content_list): + + content_list = content_list.split("\n") + data_len = len(content_list) + num_select = args.num_select + random_rows = rand.sample(range(data_len), num_select) + elements = [content_list[i] for i in random_rows] + shuchu='随机内容为:\n' + '\n '.join(elements) + b = [None, shuchu] + chatbot.append(b) + + return chatbot +class UnstructuredPaddleImageLoader(UnstructuredFileLoader): + """Loader that uses unstructured to load image files, such as PNGs and JPGs.""" + + def _get_elements(self) -> List: + def image_ocr_txt(filepath, dir_path="tmp_files"): + full_dir_path = os.path.join(os.path.dirname(filepath), dir_path) + if not os.path.exists(full_dir_path): + os.makedirs(full_dir_path) + filename = os.path.split(filepath)[-1] + ocr = PaddleOCR(use_angle_cls=True, lang="ch", use_gpu=False, show_log=False) + result = ocr.ocr(img=filepath) + + ocr_result = [i[1][0] for line in result for i in line] + txt_file_path = os.path.join(full_dir_path, "%s.txt" % (filename)) + with open(txt_file_path, 'w', encoding='utf-8') as fout: + fout.write("\n".join(ocr_result)) + return txt_file_path + + txt_file_path = image_ocr_txt(self.file_path) + from unstructured.partition.text import partition_text + return partition_text(filename=txt_file_path, **self.unstructured_kwargs) + +class ChineseTextSplitter(CharacterTextSplitter): + def __init__(self, pdf: bool = False, sentence_size: int = 100, **kwargs): + super().__init__(**kwargs) + self.pdf = pdf + self.sentence_size = sentence_size + + def split_text1(self, text: str) -> List[str]: + if self.pdf: + text = re.sub(r"\n{3,}", "\n", text) + text = re.sub('\s', ' ', text) + text = text.replace("\n\n", "") + sent_sep_pattern = re.compile('([﹒﹔﹖﹗.。!?]["’”」』]{0,2}|(?=["‘“「『]{1,2}|$))') # del :; + sent_list = [] + for ele in sent_sep_pattern.split(text): + if sent_sep_pattern.match(ele) and sent_list: + sent_list[-1] += ele + elif ele: + sent_list.append(ele) + return sent_list + + def split_text(self, text: str) -> List[str]: ##此处需要进一步优化逻辑 + if self.pdf: + text = re.sub(r"\n{3,}", r"\n", text) + text = re.sub('\s', " ", text) + text = re.sub("\n\n", "", text) + + text = re.sub(r'([;;.!?。!?\?])([^”’])', r"\1\n\2", text) # 单字符断句符 + text = re.sub(r'(\.{6})([^"’”」』])', r"\1\n\2", text) # 英文省略号 + text = re.sub(r'(\…{2})([^"’”」』])', r"\1\n\2", text) # 中文省略号 + text = re.sub(r'([;;!?。!?\?]["’”」』]{0,2})([^;;!?,。!?\?])', r'\1\n\2', text) + # 如果双引号前有终止符,那么双引号才是句子的终点,把分句符\n放到双引号后,注意前面的几句都小心保留了双引号 + text = text.rstrip() # 段尾如果有多余的\n就去掉它 + # 很多规则中会考虑分号;,但是这里我把它忽略不计,破折号、英文双引号等同样忽略,需要的再做些简单调整即可。 + ls = [i for i in text.split("\n") if i] + for ele in ls: + if len(ele) > self.sentence_size: + ele1 = re.sub(r'([,,.]["’”」』]{0,2})([^,,.])', r'\1\n\2', ele) + ele1_ls = ele1.split("\n") + for ele_ele1 in ele1_ls: + if len(ele_ele1) > self.sentence_size: + ele_ele2 = re.sub(r'([\n]{1,}| {2,}["’”」』]{0,2})([^\s])', r'\1\n\2', ele_ele1) + ele2_ls = ele_ele2.split("\n") + for ele_ele2 in ele2_ls: + if len(ele_ele2) > self.sentence_size: + ele_ele3 = re.sub('( ["’”」』]{0,2})([^ ])', r'\1\n\2', ele_ele2) + ele2_id = ele2_ls.index(ele_ele2) + ele2_ls = ele2_ls[:ele2_id] + [i for i in ele_ele3.split("\n") if i] + ele2_ls[ + ele2_id + 1:] + ele_id = ele1_ls.index(ele_ele1) + ele1_ls = ele1_ls[:ele_id] + [i for i in ele2_ls if i] + ele1_ls[ele_id + 1:] + + id = ls.index(ele) + ls = ls[:id] + [i for i in ele1_ls if i] + ls[id + 1:] + return ls + +class UnstructuredPaddlePDFLoader(UnstructuredFileLoader): + """Loader that uses unstructured to load image files, such as PNGs and JPGs.""" + + def _get_elements(self) -> List: + def pdf_ocr_txt(filepath, dir_path="tmp_files"): + full_dir_path = os.path.join(os.path.dirname(filepath), dir_path) + if not os.path.exists(full_dir_path): + os.makedirs(full_dir_path) + ocr = PaddleOCR(use_angle_cls=True, lang="ch", use_gpu=False, show_log=False) + doc = fitz.open(filepath) + txt_file_path = os.path.join(full_dir_path, f"{os.path.split(filepath)[-1]}.txt") + img_name = os.path.join(full_dir_path, 'tmp.png') + with open(txt_file_path, 'w', encoding='utf-8') as fout: + for i in range(doc.page_count): + page = doc[i] + text = page.get_text("") + fout.write(text) + fout.write("\n") + + img_list = page.get_images() + for img in img_list: + pix = fitz.Pixmap(doc, img[0]) + if pix.n - pix.alpha >= 4: + pix = fitz.Pixmap(fitz.csRGB, pix) + pix.save(img_name) + + result = ocr.ocr(img_name) + ocr_result = [i[1][0] for line in result for i in line] + fout.write("\n".join(ocr_result)) + if os.path.exists(img_name): + os.remove(img_name) + return txt_file_path + + txt_file_path = pdf_ocr_txt(self.file_path) + from unstructured.partition.text import partition_text + return partition_text(filename=txt_file_path, **self.unstructured_kwargs) + +def load_caidan(filepath, sentence_size=args.chunk_size): + + if filepath.lower().endswith(".pdf"): + # 暂且将paddle相关的loader改为动态加载,可以在不上传pdf/image知识文件的前提下使用protobuf=4.x + loader = UnstructuredPaddlePDFLoader(filepath) + textsplitter = ChineseTextSplitter(pdf=True, sentence_size=sentence_size) + docs = loader.load_and_split(textsplitter) + elif filepath.lower().endswith(".jpg") or filepath.lower().endswith(".png"): + # 暂且将paddle相关的loader改为动态加载,可以在不上传pdf/image知识文件的前提下使用protobuf=4.x + loader = UnstructuredPaddleImageLoader(filepath, mode="elements") + textsplitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size) + docs = loader.load_and_split(text_splitter=textsplitter) + else: + loader = UnstructuredFileLoader(filepath, mode="elements") + textsplitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size) + docs = loader.load_and_split(text_splitter=textsplitter) + return docs + +def tupian_shibie(mode1,filepath,chatbot): + from aip import AipImageClassify + """ 可选参数 """ + options = {} + options["top_num"] = args.top_num + options["filter_threshold"] = str(args.filter_threshold) + options["baike_num"] = args.baike_num + + image = get_file_content(filepath) + if mode1 == '菜品识别': + APP_ID = args.APP_ID_c + API_KEY = args.API_KEY_c + SECRET_KEY = args.SECRET_KEY_c + AipImageClassify = AipImageClassify(APP_ID, API_KEY, SECRET_KEY) + result = AipImageClassify.dishDetect(image, options) + result = result["result"] + num = len(result) + name_list = [] + probability = [] + baike_url = [] + description = [] + for i in result: + name_list.append(i['name']) + probability.append(i['probability']) + try: + baike_url.append(i['baike_info']['baike_url']) + description.append(i['baike_info']['description']) + except: + baike_url.append('没有百科链接') + description.append('没有百科信息') + inf = '根据预测有以下几种可能:' + for i in range(num): + one = f""" + {i + 1}: {name_list[i]} + 可能性为:{probability[i]} + 百科链接:{baike_url[i]} + 相关描述:{description[i]} + """ + inf += one + "\n" + + else: + APP_ID = args.APP_ID_g + API_KEY = args.API_KEY_g + SECRET_KEY = args.SECRET_KEY_g + AipImageClassify = AipImageClassify(APP_ID, API_KEY, SECRET_KEY) + result = AipImageClassify.ingredient(image, options) + + result = result["result"] + num = len(result) + name_list = [] + score = [] + for i in result: + name_list.append(i['name']) + score.append(i['score']) + + inf = '根据预测有以下几种可能:' + for i in range(num): + one = f""" + {i + 1}: {name_list[i]} + 可能性为:{score[i]} + """ + inf += one + "\n" + + b = [None, inf] + chatbot.append(b) + + return chatbot +def get_file_content(filePath): + with open(filePath, 'rb') as fp: + return fp.read() + + + + diff --git a/UI界面.py b/UI界面.py new file mode 100644 index 0000000..a33f306 --- /dev/null +++ b/UI界面.py @@ -0,0 +1,464 @@ +# coding=utf-8 +from UI工具 import * +import gradio as gr + + + +webui_title = """▶▶▶▶智能菜单系统◀◀◀◀""" + +with gr.Blocks() as demo: + gr.Markdown(webui_title) + with gr.Tab("菜单推荐"): + with gr.Tab("基于知识库的推荐"): + with gr.Row(): + with gr.Column(scale=10): + chatbot = gr.Chatbot([[None,zhishiku_init1]],label='基于知识库的菜单推荐',show_label=True,height=850) + query = gr.Textbox(show_label=True, label="请输入你的喜好,按回车提交") + + with gr.Column(scale=5): + knowledge_set = gr.Accordion("知识库更新", visible=True,open=False) + answer_set = gr.Accordion("回答参数设置", visible=True,open=False) + with knowledge_set: + O_data_path = gr.Textbox(label="数据储存地址", + placeholder=args.original_data_path, + lines=1, + interactive=True, + visible=True) + + P_data_path = gr.Textbox(label="向量库保存地址", + placeholder=args.preprocessed_data_path, + lines=1, + interactive=True, + visible=True) + + emb_name = gr.Textbox(label="embedding模型", + placeholder='默认模型:'+args.embeddings_model_name, + lines=1, + interactive=True, + visible=True) + chunk_size = gr.Number(value=args.chunk_size, + label="数据切分最大长度", + interactive=True, + visible=True, + minimum=1, + precision=0) + chunk_overlap = gr.Number(value=args.chunk_overlap, + label="数据片段最大重合", + interactive=True, + visible=True, + minimum=0, + precision=0) + chunk_size.change(fn=change_chunk_size, + inputs=[chunk_size], + outputs=[]) + chunk_overlap.change(fn=change_chunk_overlap, + inputs=[chunk_overlap], + outputs=[]) + + db_update = gr.Button(value="更新知识库", visible=True) + + O_data_path.change(fn=change_data_path, + inputs=[O_data_path], + outputs=[]) + P_data_path.change(fn=change_P_data_path, + inputs=[P_data_path], + outputs=[]) + emb_name.change(fn=change_emb_name, + inputs=[emb_name], + outputs=[]) + db_update.click(fn=db_update_click1, + inputs=[chatbot], + outputs=[chatbot]) + with answer_set: + search_top_k = gr.Number(value=args.topk_1, + label="获取知识库内容条数", + interactive=True, + visible=True, + minimum=1, + maximum=50, + precision=0) + model_name = gr.Textbox(label="模型名字", + placeholder=args.pretrained_model_name, + lines=1, + interactive=True, + visible=True) + model_top_k = gr.Number(value=args.topk, + label="模型回答tok", + interactive=True, + visible=True, + minimum=1, + maximum=50, + precision=0) + model_temperature = gr.Slider(0, 1, + value=args.temperature, + step=0.01, + label="模型回答温度", + interactive=True) + + search_top_k.change(fn=change_search_top_k, + inputs=[search_top_k], + outputs=[]) + model_top_k.change(fn=change_model_top_k, + inputs=[model_top_k], + outputs=[]) + model_temperature.change(fn=change_model_temperature, + inputs=[model_temperature], + outputs=[]) + model_name.change(fn=change_model_name, + inputs=[model_name], + outputs=[]) + query.submit(fn=get_answer, + inputs=[query, chatbot], + outputs=[chatbot, query]) + clear = gr.ClearButton([chatbot], value='清除记录') + clear_cash = gr.Button(value="清理显存", visible=True) + clear_cash.click(fn=clean_memory, + inputs=[chatbot], + outputs=[chatbot]) + + with gr.Tab("基于菜单的推荐"): + with gr.Row(): + with gr.Column(scale=10): + chatbot = gr.Chatbot([[None,caidan_init0]],label='基于菜单的推荐', show_label=True,height=850) + query = gr.Textbox(show_label=True, label="请输入你的喜好,按回车提交") + + with gr.Column(scale=5): + with gr.Tab("上传pdf菜单"): + caidan = gr.Textbox(label="菜单地址", + placeholder=args.path_caidan, + lines=1, + interactive=True, + visible=True) + caidan.change(fn=change_caidan_path, + inputs=[caidan]) + with gr.Tab("添加图片文件"): + tu_caidan = gr.Image(label="添加图片文件", show_label=False, type='filepath') + tu_caidan.change(fn=change_caidan_path, + inputs=[tu_caidan]) + + answer_set = gr.Accordion("回答参数设置", visible=True, open=True) + with answer_set: + model_name = gr.Textbox(label="模型名字", + placeholder="默认模型:"+args.pretrained_model_name, + lines=1, + interactive=True, + visible=True) + model_top_k = gr.Number(value=args.topk, + label="模型回答tok", + interactive=True, + visible=True, + minimum=1, + maximum=50, + precision=0,) + model_temperature = gr.Slider(0, 1, + value=args.temperature, + step=0.01, + label="模型回答温度", + interactive=True) + model_top_k.change(fn=change_model_top_k, + inputs=[model_top_k], + outputs=[]) + model_temperature.change(fn=change_model_temperature, + inputs=[model_temperature], + outputs=[]) + model_name.change(fn=change_model_name, + inputs=[model_name], + outputs=[]) + query.submit(fn=get_answer_caidan, + inputs=[query, chatbot], + outputs=[chatbot, query]) + clear1 = gr.ClearButton([chatbot], value='清除记录') + clear_cash = gr.Button(value="清理显存", visible=True) + clear_cash.click(fn=clean_memory, + inputs=[chatbot], + outputs=[chatbot]) + + with gr.Tab("大模型随机推荐"): + with gr.Row(): + with gr.Column(scale=10): + chatbot = gr.Chatbot([[None,damodel_init0]],label='大模型随机推荐', show_label=True, height=850) + query = gr.Textbox(label='请输入你的喜好,按回车提交',show_label=True) + + with gr.Column(scale=5): + answer_set = gr.Accordion("回答参数设置", visible=True) + with answer_set: + model_name = gr.Textbox(label="模型名字", + placeholder="默认模型:"+ args.pretrained_model_name, + lines=1, + interactive=True, + visible=True) + model_top_k = gr.Number(value=args.topk, + label="模型回答tok", + interactive=True, + visible=True, + minimum=1, + maximum=50, + precision=0, + ) + model_temperature = gr.Slider(0, 1, + value=args.temperature, + step=0.01, + label="模型回答温度", + interactive=True) + model_top_k.change(fn=change_model_top_k, + inputs=[model_top_k], + outputs=[]) + model_temperature.change(fn=change_model_temperature, + inputs=[model_temperature], + outputs=[]) + model_name.change(fn=change_model_name, + inputs=[model_name], + outputs=[]) + + query.submit(fn=get_answer_model, + inputs=[query, chatbot], + outputs=[chatbot, query]) + + clear2 = gr.ClearButton([chatbot], value='清除记录') + clear_cash = gr.Button(value="清理显存", visible=True) + clear_cash.click(fn=clean_memory, + inputs=[chatbot], + outputs=[chatbot]) + + with gr.Tab("自定义推荐"): + with gr.Row(): + with gr.Column(scale=10): + chatbot = gr.Chatbot(label='自定义推荐', show_label=True, height=850) + clear3 = gr.ClearButton([chatbot], value='清除记录') + with gr.Column(scale=5): + with gr.Tab("从文件中获取随机内容"): + suijidz = gr.Textbox(label="随机内容地址", + placeholder='默认地址:'+ args.path_zidingyi, + lines=1, + interactive=True, + visible=True) + suiji_num = gr.Number(value=args.num_select, + label="随机数目", + minimum=1, + precision=0, + interactive=True) + suijidz.change(fn=change_path_zidingyi, + inputs=[suijidz], + outputs=[suijidz]) + + suiji_num.input(fn=change_suiji_num, + inputs=[suiji_num]) + + suiji_1 = gr.Button(value="开始随机", visible=True) + + suiji_1.click(fn=Random_selection1, + inputs=[chatbot], + outputs=[chatbot]) + with gr.Tab("手动输入随机内容"): + suiji_nr2 = gr.Textbox(label="请输入随机内容", + placeholder='在这输入随机内容,一行是一条内容', + lines=12, + interactive=True, + visible=True) + suiji_num = gr.Number(value=args.num_select, + label="随机数目", + minimum=1, + precision=0, + interactive=True) + suiji_num.input(fn=change_suiji_num, + inputs=[suiji_num]) + + suiji_2 = gr.Button(value="开始随机", visible=True) + + suiji_2.click(fn=Random_selection2, + inputs=[chatbot,suiji_nr2], + outputs=[chatbot]) + + with gr.Tab("智能识别"): + with gr.Row(): + with gr.Column(scale=10): + Chatbot = gr.Chatbot([[None,zhinengshibie]],label='智能识别', show_label=True, height=850) + clear4 = gr.ClearButton([Chatbot], value='清除记录') + with gr.Column(scale=4): + mode1 = gr.Radio(["菜品识别", "果蔬识别"],label="识别模式",value='菜品识别') + answer_set = gr.Accordion("参数设置", visible=True,open=False) + with answer_set: + shitu_top_k = gr.Number(value=args.top_num, + label="模型回答tok", + interactive=True, + visible=True, + minimum=1, + maximum=20, + precision=0, + ) + shitu_filter_threshold = gr.Slider(0, 1, + value=args.filter_threshold, + step=0.01, + label="图片识别阈值", + interactive=True) + shitu_baike_num = gr.Number(value=args.baike_num, + label="百科回答数", + interactive=True, + visible=True, + minimum=1, + maximum=20, + precision=0) + shitu_top_k.change(fn=change_shitu_top_k, + inputs=[shitu_top_k]) + shitu_filter_threshold.change(fn=change_shitu_filter_threshold, + inputs=[shitu_filter_threshold]) + shitu_baike_num.change(fn=change_shitu_baike_num, + inputs=[shitu_baike_num]) + + with gr.Tab("添加图片文件"): + filepath = gr.Image(label="添加图片文件",show_label=False,type='filepath') + tupian_sb = gr.Button(value="开启图片识别", visible=True) + tupian_sb.click(fn=tupian_shibie, + inputs=[mode1,filepath,Chatbot], + outputs=[Chatbot]) + + with gr.Tab("菜单规划"): + with gr.Row(): + with gr.Column(scale=10): + chatbot = gr.Chatbot([],label='菜单规划',show_label=True, height=850) + query = gr.Textbox(show_label=True, label="请输入你的食材,按回车提交") + query2 = gr.Textbox(show_label=True, label="请输入你想制作的菜肴进行询问,按回车提交") + with gr.Column(scale=5): + mode2 = gr.Radio(["启用", "不启用"], label="是否启用知识库", value='启用') + knowledge_set = gr.Accordion("知识库设定", visible=True,open=False) + answer_set = gr.Accordion("回答参数设置", visible=True, open=False) + with knowledge_set: + O_data_path = gr.Textbox(label="数据储存地址", + placeholder=args.original_data_path_g, + lines=1, + interactive=True, + visible=True) + + P_data_path = gr.Textbox(label="向量库保存地址", + placeholder=args.preprocessed_data_path_g, + lines=1, + interactive=True, + visible=True) + + emb_name = gr.Textbox(label="embedding模型", + placeholder='默认模型:' + args.embeddings_model_name, + lines=1, + interactive=True, + visible=True) + chunk_size = gr.Number(value=args.chunk_size, + label="数据切分最大长度", + interactive=True, + visible=True, + minimum=1, + precision=0) + chunk_overlap = gr.Number(value=args.chunk_overlap, + label="数据片段最大重合", + interactive=True, + visible=True, + minimum=0, + precision=0) + chunk_size.change(fn=change_chunk_size, + inputs=[chunk_size], + outputs=[]) + chunk_overlap.change(fn=change_chunk_overlap, + inputs=[chunk_overlap], + outputs=[]) + + db_update = gr.Button(value="更新知识库", visible=True) + + O_data_path.change(fn=change_data_path_g, + inputs=[O_data_path], + outputs=[]) + P_data_path.change(fn=change_P_data_path_g, + inputs=[P_data_path], + outputs=[]) + emb_name.change(fn=change_emb_name, + inputs=[emb_name], + outputs=[]) + db_update.click(fn=db_update_click, + inputs=[chatbot], + outputs=[chatbot]) + with answer_set: + search_top_k = gr.Number(value=args.topk_1, + label="获取知识库内容条数", + interactive=True, + visible=True, + minimum=1, + maximum=50, + precision=0) + model_name = gr.Textbox(label="模型名字", + placeholder=args.pretrained_model_name, + lines=1, + interactive=True, + visible=True) + model_top_k = gr.Number(value=args.topk, + label="模型回答tok", + interactive=True, + visible=True, + minimum=1, + maximum=50, + precision=0) + model_temperature = gr.Slider(0, 1, + value=args.temperature, + step=0.01, + label="模型回答温度", + interactive=True) + search_top_k.change(fn=change_search_top_k, + inputs=[search_top_k], + outputs=[]) + model_top_k.change(fn=change_model_top_k, + inputs=[model_top_k], + outputs=[]) + model_temperature.change(fn=change_model_temperature, + inputs=[model_temperature], + outputs=[]) + model_name.change(fn=change_model_name, + inputs=[model_name], + outputs=[]) + query.submit(fn=get_answer_guihua, + inputs=[query, chatbot,mode2], + outputs=[chatbot, query]) + query2.submit(fn=get_answer_guihua2, + inputs=[query2, chatbot, mode2], + outputs=[chatbot, query2]) + + clear5 = gr.ClearButton([chatbot], value='清除记录') + clear_cash = gr.Button(value="清理显存", visible=True) + clear_cash.click(fn=clean_memory, + inputs=[chatbot], + outputs=[chatbot]) + + with gr.Tab("自由对话"): + with gr.Row(): + with gr.Column(scale=10): + chatbot = gr.Chatbot([],label='自由对话',show_label=True, height=950) + query = gr.Textbox(show_label=True, label="在这输入,开始自由对话") + with gr.Column(scale=5): + model_name = gr.Textbox(label="模型名字", + placeholder=args.pretrained_model_name, + lines=1, + interactive=True, + visible=True) + model_top_k = gr.Number(value=args.topk, + label="模型回答tok", + interactive=True, + visible=True, + minimum=1, + maximum=50, + precision=0) + model_temperature = gr.Slider(0, 1, + value=args.temperature, + step=0.01, + label="模型回答温度", + interactive=True) + + model_top_k.change(fn=change_model_top_k, + inputs=[model_top_k], + outputs=[]) + model_temperature.change(fn=change_model_temperature, + inputs=[model_temperature], + outputs=[]) + model_name.change(fn=change_model_name, + inputs=[model_name], + outputs=[]) + query.submit(fn=ziyouduihau, + inputs=[query, chatbot], + outputs=[chatbot, query]) + clear6 = gr.ClearButton([chatbot], value='清除记录') + +demo.queue(concurrency_count=3).launch(server_name='localhost',server_port=7989) \ No newline at end of file diff --git a/args.py b/args.py new file mode 100644 index 0000000..ccec858 --- /dev/null +++ b/args.py @@ -0,0 +1,48 @@ +import argparse +import torch + + + +parser = argparse.ArgumentParser() + +parser.add_argument('--original_data_path', type=str, default='模型数据/菜单推荐') +parser.add_argument('--preprocessed_data_path', type=str, default='vectorstores/caidantuijian') +parser.add_argument('--original_data_path_g', type=str, default='模型数据/菜单规划') +parser.add_argument('--preprocessed_data_path_g', type=str, default='vectorstores/caidanguihua') +parser.add_argument('--embeddings_model_name', type=str, default='moka-ai/m3e-large') +parser.add_argument("--topk", type=int, default=5) #模型回答topk +parser.add_argument("--topk_1", type=int, default=5) #知识库的返回的tok +parser.add_argument("--lenhistory", type=int, default=5) #上下文历史长度 +#parser.add_argument("--fenci", type=int, default=150) #文档切分最大长度 +parser.add_argument('--pretrained_model_name',type=str,default='THUDM/chatglm2-6b-int4') +parser.add_argument("--temperature", type=float, default=0.1) #模型输出温度 +parser.add_argument('--path_caidan', type=str, default='模型数据/商户菜单/小饭店菜单.pdf') +parser.add_argument('--path_zidingyi', type=str, default='模型数据/菜品清单.txt') + +parser.add_argument("--num_select", type=int, default=2) +parser.add_argument("--chunk_size", type=int, default=100) #数据切分的长度 +parser.add_argument("--chunk_overlap", type=int, default=10) #数据切分允许的重叠长度 +parser.add_argument("--top_num", type=int, default=3) #图片识别topk +parser.add_argument("--filter_threshold", type=float, default=0.7) #图片识别阈值 +parser.add_argument("--baike_num", type=int, default=3) #图片识别展示的百科数 + + +args = parser.parse_args() + +args.device = "cuda" if torch.cuda.is_available() else "cpu" +args.xihao = '豆芽,牛肉,川菜,豆腐,蔬菜' + + +'''api申请方法见:https://blog.csdn.net/2303_79001442/article/details/132093208 ''' +##菜品识别API +args.APP_ID_c = '36759749' +args.API_KEY_c= 'G2eUrTDLXsVYNeSLVPSQQsDR' +args.SECRET_KEY_c = 'T7TBOPP8EOprcWhKzOGIu0Tdtdwjrgar' + +##果蔬识别API +args.APP_ID_g = '36759878' +args.API_KEY_g = 'bwdn6X3RouhTxGm3njcwpFdA' +args.SECRET_KEY_g = 'BRgG1YtEcEkASrXGL4bw3TvwZkDhTwDq' + +#windows系统NLP模型下载地址 C:\Users\Administrator\.cache\huggingface +#windows系统Embedding模型下载地址 C:\Users\Administrator\.cache\torch\sentence_transformers diff --git a/demo/果蔬识别.png b/demo/果蔬识别.png new file mode 100644 index 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