update@web_demo

README.md

@@ -11,8 +11,9 @@
 
 ## 🚀 特性
 
-- 🚀 2023/04/19 增加web search功能，需要确保网络畅通！
+- 🐯 2023/04/19 引入ChuanhuChatGPT皮肤
-- 🚀 2023/04/18 webui增加知识库选择功能
+- 📱 2023/04/19 增加web search功能，需要确保网络畅通！
+- 📚 2023/04/18 webui增加知识库选择功能
 - 🚀 2023/04/18 修复推理预测超时5s报错问题
 - 🎉 2023/04/17 支持多种文档上传与内容解析：pdf、docx，ppt等
 - 🎉 2023/04/17 支持知识增量更新

app_modules/overwrites.py

@@ -1,25 +1,12 @@
 from __future__ import annotations
-import logging
 
-from llama_index import Prompt
 from typing import List, Tuple
-import mdtex2html
 
-from app_modules.presets import *
 from app_modules.utils import *
 
-def compact_text_chunks(self, prompt: Prompt, text_chunks: List[str]) -> List[str]:
-    logging.debug("Compacting text chunks...🚀🚀🚀")
-    combined_str = [c.strip() for c in text_chunks if c.strip()]
-    combined_str = [f"[{index+1}] {c}" for index, c in enumerate(combined_str)]
-    combined_str = "\n\n".join(combined_str)
-    # resplit based on self.max_chunk_overlap
-    text_splitter = self.get_text_splitter_given_prompt(prompt, 1, padding=1)
-    return text_splitter.split_text(combined_str)
-
 
 def postprocess(
-    self, y: List[Tuple[str | None, str | None]]
+        self, y: List[Tuple[str | None, str | None]]
 ) -> List[Tuple[str | None, str | None]]:
     """
     Parameters:
@@ -39,13 +26,17 @@ def postprocess(
         temp.append((user, bot))
     return temp
 
-with open("./assets/custom.js", "r", encoding="utf-8") as f, open("./assets/Kelpy-Codos.js", "r", encoding="utf-8") as f2:
+
+with open("./assets/custom.js", "r", encoding="utf-8") as f, open("./assets/Kelpy-Codos.js", "r",
+                                                                  encoding="utf-8") as f2:
     customJS = f.read()
     kelpyCodos = f2.read()
 
+
 def reload_javascript():
     print("Reloading javascript...")
     js = f'<script>{customJS}</script><script>{kelpyCodos}</script>'
+
     def template_response(*args, **kwargs):
         res = GradioTemplateResponseOriginal(*args, **kwargs)
         res.body = res.body.replace(b'</html>', f'{js}</html>'.encode("utf8"))
@@ -54,4 +45,5 @@ def reload_javascript():
 
     gr.routes.templates.TemplateResponse = template_response
 
+
 GradioTemplateResponseOriginal = gr.routes.templates.TemplateResponse

app_modules/utils.py

@@ -1,32 +1,16 @@
 # -*- coding:utf-8 -*-
 from __future__ import annotations
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Tuple, Type
-import logging
-import json
-import os
-import datetime
-import hashlib
-import csv
-import requests
-import re
-import html
-import markdown2
-import torch
-import sys
-import gc
-from pygments.lexers import guess_lexer, ClassNotFound
 
-import gradio as gr
+import html
-from pypinyin import lazy_pinyin
+import logging
-import tiktoken
+import re
+
 import mdtex2html
 from markdown import markdown
 from pygments import highlight
-from pygments.lexers import guess_lexer, get_lexer_by_name
 from pygments.formatters import HtmlFormatter
-import transformers
+from pygments.lexers import ClassNotFound
-from peft import PeftModel
+from pygments.lexers import guess_lexer, get_lexer_by_name
-from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer
 
 from app_modules.presets import *
 
@@ -241,142 +225,3 @@ class State:
 
 
 shared_state = State()
-
-
-# Greedy Search
-def greedy_search(input_ids: torch.Tensor,
-                  model: torch.nn.Module,
-                  tokenizer: transformers.PreTrainedTokenizer,
-                  stop_words: list,
-                  max_length: int,
-                  temperature: float = 1.0,
-                  top_p: float = 1.0,
-                  top_k: int = 25) -> Iterator[str]:
-    generated_tokens = []
-    past_key_values = None
-    current_length = 1
-    for i in range(max_length):
-        with torch.no_grad():
-            if past_key_values is None:
-                outputs = model(input_ids)
-            else:
-                outputs = model(input_ids[:, -1:], past_key_values=past_key_values)
-            logits = outputs.logits[:, -1, :]
-            past_key_values = outputs.past_key_values
-
-            # apply temperature
-            logits /= temperature
-
-            probs = torch.softmax(logits, dim=-1)
-            # apply top_p
-            probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
-            probs_sum = torch.cumsum(probs_sort, dim=-1)
-            mask = probs_sum - probs_sort > top_p
-            probs_sort[mask] = 0.0
-
-            # apply top_k
-            # if top_k is not None:
-            #    probs_sort1, _ = torch.topk(probs_sort, top_k)
-            #    min_top_probs_sort = torch.min(probs_sort1, dim=-1, keepdim=True).values
-            #    probs_sort = torch.where(probs_sort < min_top_probs_sort, torch.full_like(probs_sort, float(0.0)), probs_sort)
-
-            probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
-            next_token = torch.multinomial(probs_sort, num_samples=1)
-            next_token = torch.gather(probs_idx, -1, next_token)
-
-            input_ids = torch.cat((input_ids, next_token), dim=-1)
-
-            generated_tokens.append(next_token[0].item())
-            text = tokenizer.decode(generated_tokens)
-
-            yield text
-            if any([x in text for x in stop_words]):
-                del past_key_values
-                del logits
-                del probs
-                del probs_sort
-                del probs_idx
-                del probs_sum
-                gc.collect()
-                return
-
-
-def generate_prompt_with_history(text, history, tokenizer, max_length=2048):
-    prompt = "The following is a conversation between a human and an AI assistant named Baize (named after a mythical creature in Chinese folklore). Baize is an open-source AI assistant developed by UCSD and Sun Yat-Sen University. The human and the AI assistant take turns chatting. Human statements start with [|Human|] and AI assistant statements start with [|AI|]. The AI assistant always provides responses in as much detail as possible, and in Markdown format. The AI assistant always declines to engage with topics, questions and instructions related to unethical, controversial, or sensitive issues. Complete the transcript in exactly that format.\n[|Human|]Hello!\n[|AI|]Hi!"
-    history = ["\n[|Human|]{}\n[|AI|]{}".format(x[0], x[1]) for x in history]
-    history.append("\n[|Human|]{}\n[|AI|]".format(text))
-    history_text = ""
-    flag = False
-    for x in history[::-1]:
-        if tokenizer(prompt + history_text + x, return_tensors="pt")['input_ids'].size(-1) <= max_length:
-            history_text = x + history_text
-            flag = True
-        else:
-            break
-    if flag:
-        return prompt + history_text, tokenizer(prompt + history_text, return_tensors="pt")
-    else:
-        return None
-
-
-def is_stop_word_or_prefix(s: str, stop_words: list) -> bool:
-    for stop_word in stop_words:
-        if s.endswith(stop_word):
-            return True
-        for i in range(1, len(stop_word)):
-            if s.endswith(stop_word[:i]):
-                return True
-    return False
-
-
-def load_tokenizer_and_model(base_model, adapter_model, load_8bit=False):
-    if torch.cuda.is_available():
-        device = "cuda"
-    else:
-        device = "cpu"
-
-    try:
-        if torch.backends.mps.is_available():
-            device = "mps"
-    except:  # noqa: E722
-        pass
-    tokenizer = LlamaTokenizer.from_pretrained(base_model)
-    if device == "cuda":
-        model = LlamaForCausalLM.from_pretrained(
-            base_model,
-            load_in_8bit=load_8bit,
-            torch_dtype=torch.float16,
-            device_map="auto",
-        )
-        model = PeftModel.from_pretrained(
-            model,
-            adapter_model,
-            torch_dtype=torch.float16,
-        )
-    elif device == "mps":
-        model = LlamaForCausalLM.from_pretrained(
-            base_model,
-            device_map={"": device},
-            torch_dtype=torch.float16,
-        )
-        model = PeftModel.from_pretrained(
-            model,
-            adapter_model,
-            device_map={"": device},
-            torch_dtype=torch.float16,
-        )
-    else:
-        model = LlamaForCausalLM.from_pretrained(
-            base_model, device_map={"": device}, low_cpu_mem_usage=True
-        )
-        model = PeftModel.from_pretrained(
-            model,
-            adapter_model,
-            device_map={"": device},
-        )
-
-    if not load_8bit:
-        model.half()  # seems to fix bugs for some users.
-
-    model.eval()
-    return tokenizer, model, device

main.py

@@ -1,7 +1,6 @@
 import os
 import shutil
 
-import gradio as gr
 from app_modules.presets import *
 from clc.langchain_application import LangChainApplication
 
@@ -93,6 +92,7 @@ def predict(input,
     search_text += web_content
     return '', history, history, search_text
 
+
 with open("assets/custom.css", "r", encoding="utf-8") as f:
     customCSS = f.read()
 with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
@@ -147,14 +147,20 @@ with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
                         outputs=None)
         with gr.Column(scale=4):
             with gr.Row():
-                with gr.Column(scale=4):
+                chatbot = gr.Chatbot(label='Chinese-LangChain').style(height=400)
-                    chatbot = gr.Chatbot(label='Chinese-LangChain').style(height=400)
+            with gr.Row():
-                    message = gr.Textbox(label='请输入问题')
+                message = gr.Textbox(label='请输入问题')
-                    with gr.Row():
+            with gr.Row():
-                        clear_history = gr.Button("🧹 清除历史对话")
+                clear_history = gr.Button("🧹 清除历史对话")
-                        send = gr.Button("🚀 发送")
+                send = gr.Button("🚀 发送")
-                with gr.Column(scale=2):
+            with gr.Row():
-                    search = gr.Textbox(label='搜索结果')
+                gr.Markdown("""提醒：<br>
+                                        [Chinese-LangChain](https://github.com/yanqiangmiffy/Chinese-LangChain) <br>
+                                        有任何使用问题[Github Issue区](https://github.com/yanqiangmiffy/Chinese-LangChain)进行反馈. <br>
+                                        """)
+        with gr.Column(scale=2):
+            search = gr.Textbox(label='搜索结果')
+
         set_kg_btn.click(
             set_knowledge,
             show_progress=True,
@@ -185,10 +191,7 @@ with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
                            state
                        ],
                        outputs=[message, chatbot, state, search])
-    gr.Markdown("""提醒：<br>
+
-            [Chinese-LangChain](https://github.com/yanqiangmiffy/Chinese-LangChain) <br>
-            有任何使用问题[Github Issue区](https://github.com/yanqiangmiffy/Chinese-LangChain)进行反馈. <br>
-            """)
 demo.queue(concurrency_count=2).launch(
     server_name='0.0.0.0',
     server_port=8888,

requirements.txt

@@ -4,4 +4,7 @@ transformers
 sentence_transformers
 faiss-cpu
 unstructured
-duckduckgo_search
+duckduckgo_search
+mdtex2html
+chardet
+cchardet