回滚版本

app.py

@@ -1,17 +1,14 @@
 # -*- coding:utf-8 -*-
+import gradio as gr
 import os
 import logging
 import sys
-
-import gradio as gr
-
+import argparse
 from utils import *
 from presets import *
-from overwrites import *
-from chat_func import *
 
 logging.basicConfig(
-    level=logging.DEBUG,
+    level=logging.INFO,
     format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s",
 )
 
@@ -52,7 +49,6 @@ else:
                 authflag = True
 
 gr.Chatbot.postprocess = postprocess
-PromptHelper.compact_text_chunks = compact_text_chunks
 
 with open("custom.css", "r", encoding="utf-8") as f:
     customCSS = f.read()
@@ -160,7 +156,7 @@ with gr.Blocks(
                         value=hide_middle_chars(my_api_key),
                         type="password",
                         visible=not HIDE_MY_KEY,
-                        label="API-Key(按Enter提交)",
+                        label="API-Key",
                     )
                     model_select_dropdown = gr.Dropdown(
                         label="选择模型", choices=MODELS, multiselect=False, value=MODELS[0]
@@ -169,7 +165,7 @@ with gr.Blocks(
                         label="实时传输回答", value=True, visible=enable_streaming_option
                     )
                     use_websearch_checkbox = gr.Checkbox(label="使用在线搜索", value=False)
-                    index_files = gr.Files(label="上传索引文件", type="file", multiple=True)
+                    index_files = gr.File(label="上传索引文件", type="file", multiple=True)
 
                 with gr.Tab(label="Prompt"):
                     systemPromptTxt = gr.Textbox(
@@ -290,7 +286,6 @@ with gr.Blocks(
             use_streaming_checkbox,
             model_select_dropdown,
             use_websearch_checkbox,
-            index_files
         ],
         [chatbot, history, status_display, token_count],
         show_progress=True,
@@ -311,7 +306,6 @@ with gr.Blocks(
             use_streaming_checkbox,
             model_select_dropdown,
             use_websearch_checkbox,
-            index_files
         ],
         [chatbot, history, status_display, token_count],
         show_progress=True,

presets.py

@@ -1,23 +1,4 @@
 # -*- coding:utf-8 -*-
-# 错误信息
-standard_error_msg = "☹️发生了错误："  # 错误信息的标准前缀
-error_retrieve_prompt = "请检查网络连接，或者API-Key是否有效。"  # 获取对话时发生错误
-connection_timeout_prompt = "连接超时，无法获取对话。"  # 连接超时
-read_timeout_prompt = "读取超时，无法获取对话。"  # 读取超时
-proxy_error_prompt = "代理错误，无法获取对话。"  # 代理错误
-ssl_error_prompt = "SSL错误，无法获取对话。"  # SSL 错误
-no_apikey_msg = "API key长度不是51位，请检查是否输入正确。"  # API key 长度不足 51 位
-
-max_token_streaming = 3500  # 流式对话时的最大 token 数
-timeout_streaming = 30  # 流式对话时的超时时间
-max_token_all = 3500  # 非流式对话时的最大 token 数
-timeout_all = 200  # 非流式对话时的超时时间
-enable_streaming_option = True  # 是否启用选择选择是否实时显示回答的勾选框
-HIDE_MY_KEY = False  # 如果你想在UI中隐藏你的 API 密钥，将此值设置为 True
-
-SIM_K = 5
-INDEX_QUERY_TEMPRATURE = 1.0
-
 title = """<h1 align="left" style="min-width:200px; margin-top:0;">川虎ChatGPT 🚀</h1>"""
 description = """\
 <div align="center" style="margin:16px 0">
@@ -31,7 +12,6 @@ description = """\
 """
 
 summarize_prompt = "你是谁？我们刚才聊了什么？"  # 总结对话时的 prompt
-
 MODELS = [
     "gpt-3.5-turbo",
     "gpt-3.5-turbo-0301",
@@ -41,8 +21,7 @@ MODELS = [
     "gpt-4-32k-0314",
 ]  # 可选的模型
 
-
-WEBSEARCH_PTOMPT_TEMPLATE = """\
+websearch_prompt = """\
 Web search results:
 
 {web_results}
@@ -52,29 +31,18 @@ Instructions: Using the provided web search results, write a comprehensive reply
 Query: {query}
 Reply in 中文"""
 
-PROMPT_TEMPLATE = """\
-Context information is below.
----------------------
-{context_str}
----------------------
-Current date: {current_date}.
-Using the provided context information, write a comprehensive reply to the given query.
-Make sure to cite results using [number] notation after the reference.
-If the provided context information refer to multiple subjects with the same name, write separate answers for each subject.
-Use prior knowledge only if the given context didn't provide enough information.
-Answer the question: {query_str}
-Reply in 中文
-"""
+# 错误信息
+standard_error_msg = "☹️发生了错误："  # 错误信息的标准前缀
+error_retrieve_prompt = "请检查网络连接，或者API-Key是否有效。"  # 获取对话时发生错误
+connection_timeout_prompt = "连接超时，无法获取对话。"  # 连接超时
+read_timeout_prompt = "读取超时，无法获取对话。"  # 读取超时
+proxy_error_prompt = "代理错误，无法获取对话。"  # 代理错误
+ssl_error_prompt = "SSL错误，无法获取对话。"  # SSL 错误
+no_apikey_msg = "API key长度不是51位，请检查是否输入正确。"  # API key 长度不足 51 位
 
-REFINE_TEMPLATE = """\
-The original question is as follows: {query_str}
-We have provided an existing answer: {existing_answer}
-We have the opportunity to refine the existing answer
-(only if needed) with some more context below.
-------------
-{context_msg}
-------------
-Given the new context, refine the original answer to better
-Answer in the same language as the question, such as English, 中文, 日本語, Español, Français, or Deutsch.
-If the context isn't useful, return the original answer.
-"""
+max_token_streaming = 3500  # 流式对话时的最大 token 数
+timeout_streaming = 30  # 流式对话时的超时时间
+max_token_all = 3500  # 非流式对话时的最大 token 数
+timeout_all = 200  # 非流式对话时的超时时间
+enable_streaming_option = True  # 是否启用选择选择是否实时显示回答的勾选框
+HIDE_MY_KEY = False  # 如果你想在UI中隐藏你的 API 密钥，将此值设置为 True

requirements.txt

@@ -6,6 +6,4 @@ socksio
 tqdm
 colorama
 duckduckgo_search
-Pygments
-llama_index
-langchain
+Pygments

utils.py

@@ -3,16 +3,23 @@ from __future__ import annotations
 from typing import TYPE_CHECKING, Any, Callable, Dict, List, Tuple, Type
 import logging
 import json
+import gradio as gr
+
+# import openai
 import os
-import datetime
-import hashlib
-import csv
+import traceback
+import requests
 
-import gradio as gr
+# import markdown
+import csv
+import mdtex2html
 from pypinyin import lazy_pinyin
-import tiktoken
-
 from presets import *
+import tiktoken
+from tqdm import tqdm
+import colorama
+from duckduckgo_search import ddg
+import datetime
 
 # logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s")
 
@@ -30,6 +37,27 @@ HISTORY_DIR = "history"
 TEMPLATES_DIR = "templates"
 
 
+def postprocess(
+    self, y: List[Tuple[str | None, str | None]]
+) -> List[Tuple[str | None, str | None]]:
+    """
+    Parameters:
+        y: List of tuples representing the message and response pairs. Each message and response should be a string, which may be in Markdown format.
+    Returns:
+        List of tuples representing the message and response. Each message and response will be a string of HTML.
+    """
+    if y is None:
+        return []
+    for i, (message, response) in enumerate(y):
+        y[i] = (
+            # None if message is None else markdown.markdown(message),
+            # None if response is None else markdown.markdown(response),
+            None if message is None else message,
+            None if response is None else mdtex2html.convert(response, extensions=['fenced_code','codehilite','tables']),
+        )
+    return y
+
+
 def count_token(message):
     encoding = tiktoken.get_encoding("cl100k_base")
     input_str = f"role: {message['role']}, content: {message['content']}"
@@ -74,6 +102,389 @@ def construct_token_message(token, stream=False):
     return f"Token 计数: {token}"
 
 
+def get_response(
+    openai_api_key, system_prompt, history, temperature, top_p, stream, selected_model
+):
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {openai_api_key}",
+    }
+
+    history = [construct_system(system_prompt), *history]
+
+    payload = {
+        "model": selected_model,
+        "messages": history,  # [{"role": "user", "content": f"{inputs}"}],
+        "temperature": temperature,  # 1.0,
+        "top_p": top_p,  # 1.0,
+        "n": 1,
+        "stream": stream,
+        "presence_penalty": 0,
+        "frequency_penalty": 0,
+    }
+    if stream:
+        timeout = timeout_streaming
+    else:
+        timeout = timeout_all
+
+    # 获取环境变量中的代理设置
+    http_proxy = os.environ.get("HTTP_PROXY") or os.environ.get("http_proxy")
+    https_proxy = os.environ.get("HTTPS_PROXY") or os.environ.get("https_proxy")
+
+    # 如果存在代理设置，使用它们
+    proxies = {}
+    if http_proxy:
+        logging.info(f"Using HTTP proxy: {http_proxy}")
+        proxies["http"] = http_proxy
+    if https_proxy:
+        logging.info(f"Using HTTPS proxy: {https_proxy}")
+        proxies["https"] = https_proxy
+
+    # 如果有代理，使用代理发送请求，否则使用默认设置发送请求
+    if proxies:
+        response = requests.post(
+            API_URL,
+            headers=headers,
+            json=payload,
+            stream=True,
+            timeout=timeout,
+            proxies=proxies,
+        )
+    else:
+        response = requests.post(
+            API_URL,
+            headers=headers,
+            json=payload,
+            stream=True,
+            timeout=timeout,
+        )
+    return response
+
+
+def stream_predict(
+    openai_api_key,
+    system_prompt,
+    history,
+    inputs,
+    chatbot,
+    all_token_counts,
+    top_p,
+    temperature,
+    selected_model,
+):
+    def get_return_value():
+        return chatbot, history, status_text, all_token_counts
+
+    logging.info("实时回答模式")
+    partial_words = ""
+    counter = 0
+    status_text = "开始实时传输回答……"
+    history.append(construct_user(inputs))
+    history.append(construct_assistant(""))
+    chatbot.append((parse_text(inputs), ""))
+    user_token_count = 0
+    if len(all_token_counts) == 0:
+        system_prompt_token_count = count_token(construct_system(system_prompt))
+        user_token_count = (
+            count_token(construct_user(inputs)) + system_prompt_token_count
+        )
+    else:
+        user_token_count = count_token(construct_user(inputs))
+    all_token_counts.append(user_token_count)
+    logging.info(f"输入token计数: {user_token_count}")
+    yield get_return_value()
+    try:
+        response = get_response(
+            openai_api_key,
+            system_prompt,
+            history,
+            temperature,
+            top_p,
+            True,
+            selected_model,
+        )
+    except requests.exceptions.ConnectTimeout:
+        status_text = (
+            standard_error_msg + connection_timeout_prompt + error_retrieve_prompt
+        )
+        yield get_return_value()
+        return
+    except requests.exceptions.ReadTimeout:
+        status_text = standard_error_msg + read_timeout_prompt + error_retrieve_prompt
+        yield get_return_value()
+        return
+
+    yield get_return_value()
+    error_json_str = ""
+
+    for chunk in tqdm(response.iter_lines()):
+        if counter == 0:
+            counter += 1
+            continue
+        counter += 1
+        # check whether each line is non-empty
+        if chunk:
+            chunk = chunk.decode()
+            chunklength = len(chunk)
+            try:
+                chunk = json.loads(chunk[6:])
+            except json.JSONDecodeError:
+                logging.info(chunk)
+                error_json_str += chunk
+                status_text = f"JSON解析错误。请重置对话。收到的内容: {error_json_str}"
+                yield get_return_value()
+                continue
+            # decode each line as response data is in bytes
+            if chunklength > 6 and "delta" in chunk["choices"][0]:
+                finish_reason = chunk["choices"][0]["finish_reason"]
+                status_text = construct_token_message(
+                    sum(all_token_counts), stream=True
+                )
+                if finish_reason == "stop":
+                    yield get_return_value()
+                    break
+                try:
+                    partial_words = (
+                        partial_words + chunk["choices"][0]["delta"]["content"]
+                    )
+                except KeyError:
+                    status_text = (
+                        standard_error_msg
+                        + "API回复中找不到内容。很可能是Token计数达到上限了。请重置对话。当前Token计数: "
+                        + str(sum(all_token_counts))
+                    )
+                    yield get_return_value()
+                    break
+                history[-1] = construct_assistant(partial_words)
+                chatbot[-1] = (parse_text(inputs), parse_text(partial_words))
+                all_token_counts[-1] += 1
+                yield get_return_value()
+
+
+def predict_all(
+    openai_api_key,
+    system_prompt,
+    history,
+    inputs,
+    chatbot,
+    all_token_counts,
+    top_p,
+    temperature,
+    selected_model,
+):
+    logging.info("一次性回答模式")
+    history.append(construct_user(inputs))
+    history.append(construct_assistant(""))
+    chatbot.append((parse_text(inputs), ""))
+    all_token_counts.append(count_token(construct_user(inputs)))
+    try:
+        response = get_response(
+            openai_api_key,
+            system_prompt,
+            history,
+            temperature,
+            top_p,
+            False,
+            selected_model,
+        )
+    except requests.exceptions.ConnectTimeout:
+        status_text = (
+            standard_error_msg + connection_timeout_prompt + error_retrieve_prompt
+        )
+        return chatbot, history, status_text, all_token_counts
+    except requests.exceptions.ProxyError:
+        status_text = standard_error_msg + proxy_error_prompt + error_retrieve_prompt
+        return chatbot, history, status_text, all_token_counts
+    except requests.exceptions.SSLError:
+        status_text = standard_error_msg + ssl_error_prompt + error_retrieve_prompt
+        return chatbot, history, status_text, all_token_counts
+    response = json.loads(response.text)
+    content = response["choices"][0]["message"]["content"]
+    history[-1] = construct_assistant(content)
+    chatbot[-1] = (parse_text(inputs), parse_text(content))
+    total_token_count = response["usage"]["total_tokens"]
+    all_token_counts[-1] = total_token_count - sum(all_token_counts)
+    status_text = construct_token_message(total_token_count)
+    return chatbot, history, status_text, all_token_counts
+
+
+def predict(
+    openai_api_key,
+    system_prompt,
+    history,
+    inputs,
+    chatbot,
+    all_token_counts,
+    top_p,
+    temperature,
+    stream=False,
+    selected_model=MODELS[0],
+    use_websearch_checkbox=False,
+    should_check_token_count=True,
+):  # repetition_penalty, top_k
+    logging.info("输入为：" + colorama.Fore.BLUE + f"{inputs}" + colorama.Style.RESET_ALL)
+    if use_websearch_checkbox:
+        results = ddg(inputs, max_results=3)
+        web_results = []
+        for idx, result in enumerate(results):
+            logging.info(f"搜索结果{idx + 1}：{result}")
+            web_results.append(f'[{idx+1}]"{result["body"]}"\nURL: {result["href"]}')
+        web_results = "\n\n".join(web_results)
+        today = datetime.datetime.today().strftime("%Y-%m-%d")
+        inputs = (
+            websearch_prompt.replace("{current_date}", today)
+            .replace("{query}", inputs)
+            .replace("{web_results}", web_results)
+        )
+    if len(openai_api_key) != 51:
+        status_text = standard_error_msg + no_apikey_msg
+        logging.info(status_text)
+        chatbot.append((parse_text(inputs), ""))
+        if len(history) == 0:
+            history.append(construct_user(inputs))
+            history.append("")
+            all_token_counts.append(0)
+        else:
+            history[-2] = construct_user(inputs)
+        yield chatbot, history, status_text, all_token_counts
+        return
+    if stream:
+        yield chatbot, history, "开始生成回答……", all_token_counts
+    if stream:
+        logging.info("使用流式传输")
+        iter = stream_predict(
+            openai_api_key,
+            system_prompt,
+            history,
+            inputs,
+            chatbot,
+            all_token_counts,
+            top_p,
+            temperature,
+            selected_model,
+        )
+        for chatbot, history, status_text, all_token_counts in iter:
+            yield chatbot, history, status_text, all_token_counts
+    else:
+        logging.info("不使用流式传输")
+        chatbot, history, status_text, all_token_counts = predict_all(
+            openai_api_key,
+            system_prompt,
+            history,
+            inputs,
+            chatbot,
+            all_token_counts,
+            top_p,
+            temperature,
+            selected_model,
+        )
+        yield chatbot, history, status_text, all_token_counts
+    logging.info(f"传输完毕。当前token计数为{all_token_counts}")
+    if len(history) > 1 and history[-1]["content"] != inputs:
+        logging.info(
+            "回答为："
+            + colorama.Fore.BLUE
+            + f"{history[-1]['content']}"
+            + colorama.Style.RESET_ALL
+        )
+    if stream:
+        max_token = max_token_streaming
+    else:
+        max_token = max_token_all
+    if sum(all_token_counts) > max_token and should_check_token_count:
+        status_text = f"精简token中{all_token_counts}/{max_token}"
+        logging.info(status_text)
+        yield chatbot, history, status_text, all_token_counts
+        iter = reduce_token_size(
+            openai_api_key,
+            system_prompt,
+            history,
+            chatbot,
+            all_token_counts,
+            top_p,
+            temperature,
+            stream=False,
+            selected_model=selected_model,
+            hidden=True,
+        )
+        for chatbot, history, status_text, all_token_counts in iter:
+            status_text = f"Token 达到上限，已自动降低Token计数至 {status_text}"
+            yield chatbot, history, status_text, all_token_counts
+
+
+def retry(
+    openai_api_key,
+    system_prompt,
+    history,
+    chatbot,
+    token_count,
+    top_p,
+    temperature,
+    stream=False,
+    selected_model=MODELS[0],
+):
+    logging.info("重试中……")
+    if len(history) == 0:
+        yield chatbot, history, f"{standard_error_msg}上下文是空的", token_count
+        return
+    history.pop()
+    inputs = history.pop()["content"]
+    token_count.pop()
+    iter = predict(
+        openai_api_key,
+        system_prompt,
+        history,
+        inputs,
+        chatbot,
+        token_count,
+        top_p,
+        temperature,
+        stream=stream,
+        selected_model=selected_model,
+    )
+    logging.info("重试完毕")
+    for x in iter:
+        yield x
+
+
+def reduce_token_size(
+    openai_api_key,
+    system_prompt,
+    history,
+    chatbot,
+    token_count,
+    top_p,
+    temperature,
+    stream=False,
+    selected_model=MODELS[0],
+    hidden=False,
+):
+    logging.info("开始减少token数量……")
+    iter = predict(
+        openai_api_key,
+        system_prompt,
+        history,
+        summarize_prompt,
+        chatbot,
+        token_count,
+        top_p,
+        temperature,
+        stream=stream,
+        selected_model=selected_model,
+        should_check_token_count=False,
+    )
+    logging.info(f"chatbot: {chatbot}")
+    for chatbot, history, status_text, previous_token_count in iter:
+        history = history[-2:]
+        token_count = previous_token_count[-1:]
+        if hidden:
+            chatbot.pop()
+        yield chatbot, history, construct_token_message(
+            sum(token_count), stream=stream
+        ), token_count
+    logging.info("减少token数量完毕")
+
+
 def delete_last_conversation(chatbot, history, previous_token_count):
     if len(chatbot) > 0 and standard_error_msg in chatbot[-1][1]:
         logging.info("由于包含报错信息，只删除chatbot记录")
@@ -232,7 +643,6 @@ def reset_state():
 def reset_textbox():
     return gr.update(value="")
 
-
 def reset_default():
     global API_URL
     API_URL = "https://api.openai.com/v1/chat/completions"
@@ -240,7 +650,6 @@ def reset_default():
     os.environ.pop("https_proxy", None)
     return gr.update(value=API_URL), gr.update(value=""), "API URL 和代理已重置"
 
-
 def change_api_url(url):
     global API_URL
     API_URL = url
@@ -248,37 +657,22 @@ def change_api_url(url):
     logging.info(msg)
     return msg
 
-
 def change_proxy(proxy):
     os.environ["HTTPS_PROXY"] = proxy
     msg = f"代理更改为了{proxy}"
     logging.info(msg)
     return msg
 
-
 def hide_middle_chars(s):
     if len(s) <= 8:
         return s
     else:
         head = s[:4]
         tail = s[-4:]
-        hidden = "*" * (len(s) - 8)
+        hidden = '*' * (len(s) - 8)
         return head + hidden + tail
 
-
 def submit_key(key):
-    key = key.strip()
     msg = f"API密钥更改为了{hide_middle_chars(key)}"
     logging.info(msg)
     return key, msg
-
-
-def sha1sum(filename):
-    sha1 = hashlib.sha1()
-    sha1.update(filename.encode("utf-8"))
-    return sha1.hexdigest()
-
-
-def replace_today(prompt):
-    today = datetime.datetime.today().strftime("%Y-%m-%d")
-    return prompt.replace("{current_date}", today)