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Chatbot.py

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+import gradio as gr
+# import openai
+import os
+import sys
+from utils import *
+from presets import *
+
+my_api_key = os.getenv("OpenAI-API")    # 在这里输入你的 API 密钥
+HIDE_MY_KEY = True # 如果你想在UI中隐藏你的 API 密钥，将此值设置为 True
+
+gr.Chatbot.postprocess = postprocess
+
+#if we are running in Docker
+if os.environ.get('dockerrun') == 'yes':
+    dockerflag = True
+else:
+    dockerflag = False
+
+authflag = False
+
+if dockerflag:
+    my_api_key = os.environ.get('my_api_key')
+    if my_api_key == "empty":
+        print("Please give a api key!")
+        sys.exit(1)
+    #auth
+    username = os.environ.get('USERNAME')
+    password = os.environ.get('PASSWORD')
+    if not (isinstance(username, type(None)) or isinstance(password, type(None))):
+        authflag = True
+else:
+    if os.path.exists("api_key.txt"):
+        with open("api_key.txt", "r") as f:
+            my_api_key = f.read().strip()
+    if os.path.exists("auth.json"):
+        with open("auth.json", "r") as f:
+            auth = json.load(f)
+            username = auth["username"]
+            password = auth["password"]
+            if username != "" and password != "":
+                authflag = True
+
+with gr.Blocks(css=customCSS) as demo:
+    gr.HTML(title)
+    keyTxt = gr.Textbox(show_label=True, placeholder=f"在这里输入你的OpenAI API-key...",
+                        value=my_api_key, label="API Key", type="password", visible=not HIDE_MY_KEY).style(container=True)
+    chatbot = gr.Chatbot()  # .style(color_map=("#1D51EE", "#585A5B"))
+    history = gr.State([])
+    promptTemplates = gr.State(load_template(get_template_names(plain=True)[0], mode=2))
+    TRUECOMSTANT = gr.State(True)
+    FALSECONSTANT = gr.State(False)
+    topic = gr.State("未命名对话历史记录")
+
+    with gr.Row():
+        with gr.Column(scale=12):
+            txt = gr.Textbox(show_label=False, placeholder="在这里输入").style(
+                container=False)
+        with gr.Column(min_width=50, scale=1):
+            submitBtn = gr.Button("🚀", variant="primary")
+    with gr.Row():
+        emptyBtn = gr.Button("🧹 新的对话")
+        retryBtn = gr.Button("🔄 重新生成")
+        delLastBtn = gr.Button("🗑️ 删除上条对话")
+        reduceTokenBtn = gr.Button("♻️ 总结对话")
+    statusDisplay = gr.Markdown("status: ready")
+    systemPromptTxt = gr.Textbox(show_label=True, placeholder=f"在这里输入System Prompt...",
+                                 label="System prompt", value=initial_prompt).style(container=True)
+    with gr.Accordion(label="加载Prompt模板", open=False):
+        with gr.Column():
+            with gr.Row():
+                with gr.Column(scale=6):
+                    templateFileSelectDropdown = gr.Dropdown(label="选择Prompt模板集合文件（.csv）", choices=get_template_names(plain=True), multiselect=False)
+                with gr.Column(scale=1):
+                    templateRefreshBtn = gr.Button("🔄 刷新")
+                    templaeFileReadBtn = gr.Button("📂 读入模板")
+            with gr.Row():
+                with gr.Column(scale=6):
+                    templateSelectDropdown = gr.Dropdown(label="从Prompt模板中加载", choices=load_template(get_template_names(plain=True)[0], mode=1), multiselect=False)
+                with gr.Column(scale=1):
+                    templateApplyBtn = gr.Button("⬇️ 应用")
+    with gr.Accordion(label="保存/加载对话历史记录(在文本框中输入文件名，点击“保存对话”按钮)", open=False):
+        with gr.Column():
+            with gr.Row():
+                with gr.Column(scale=6):
+                    saveFileName = gr.Textbox(
+                        show_label=True, placeholder=f"在这里输入保存的文件名...", label="设置保存文件名", value="对话历史记录").style(container=True)
+                with gr.Column(scale=1):
+                    saveBtn = gr.Button("💾 保存对话")
+            with gr.Row():
+                with gr.Column(scale=6):
+                    historyFileSelectDropdown = gr.Dropdown(label="从列表中加载对话", choices=get_history_names(plain=True), multiselect=False)
+                with gr.Column(scale=1):
+                    historyRefreshBtn = gr.Button("🔄 刷新")
+                    historyReadBtn = gr.Button("📂 读入对话")
+    #inputs, top_p, temperature, top_k, repetition_penalty
+    with gr.Accordion("参数", open=False):
+        top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.05,
+                          interactive=True, label="Top-p (nucleus sampling)",)
+        temperature = gr.Slider(minimum=-0, maximum=5.0, value=1.0,
+                                step=0.1, interactive=True, label="Temperature",)
+        #top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",)
+        #repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", )
+    gr.Markdown(description)
+
+
+    txt.submit(predict, [txt, top_p, temperature, keyTxt,
+               chatbot, history, systemPromptTxt], [chatbot, history, statusDisplay])
+    txt.submit(reset_textbox, [], [txt])
+    submitBtn.click(predict, [txt, top_p, temperature, keyTxt, chatbot,
+                    history, systemPromptTxt], [chatbot, history, statusDisplay], show_progress=True)
+    submitBtn.click(reset_textbox, [], [txt])
+    emptyBtn.click(reset_state, outputs=[chatbot, history])
+    retryBtn.click(predict, [txt, top_p, temperature, keyTxt, chatbot, history,
+                   systemPromptTxt, TRUECOMSTANT], [chatbot, history, statusDisplay], show_progress=True)
+    delLastBtn.click(delete_last_conversation, [chatbot, history], [
+                     chatbot, history], show_progress=True)
+    reduceTokenBtn.click(predict, [txt, top_p, temperature, keyTxt, chatbot, history,
+                         systemPromptTxt, FALSECONSTANT, TRUECOMSTANT], [chatbot, history, statusDisplay], show_progress=True)
+    saveBtn.click(save_chat_history, [
+                  saveFileName, systemPromptTxt, history, chatbot], None, show_progress=True)
+    saveBtn.click(get_history_names, None, [historyFileSelectDropdown])
+    historyRefreshBtn.click(get_history_names, None, [historyFileSelectDropdown])
+    historyReadBtn.click(load_chat_history, [historyFileSelectDropdown],  [saveFileName, systemPromptTxt, history, chatbot], show_progress=True)
+    templateRefreshBtn.click(get_template_names, None, [templateFileSelectDropdown])
+    templaeFileReadBtn.click(load_template, [templateFileSelectDropdown],  [promptTemplates, templateSelectDropdown], show_progress=True)
+    templateApplyBtn.click(lambda x, y: x[y], [promptTemplates, templateSelectDropdown],  [systemPromptTxt], show_progress=True)
+
+print("启动成功！")
+# 默认开启本地服务器，默认可以直接从IP访问，默认不创建公开分享链接
+demo.title = "Chatbot"
+
+#if running in Docker
+if dockerflag:
+    if authflag:
+        demo.queue().launch(server_name="0.0.0.0", server_port=7860,auth=(username, password))
+    else:
+        demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False)
+#if not running in Docker
+else:
+    if authflag:
+        demo.queue().launch(share=False, auth=(username, password))
+    else:
+        #demo.queue().launch(share=False) # 改为 share=True 可以创建公开分享链接
+        #demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False) # 可自定义端口
+        demo.queue().launch(server_name="0.0.0.0", server_port=7860,auth=(os.environ["username"], os.environ["password"])) # 可设置用户名与密码
+        #demo.queue().launch(auth=("在这里填写用户名", "在这里填写密码")) # 适合Nginx反向代理

presets.py

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+title = """<h1 align="center"></h1>"""
+description = """<div align=center>
+
+此网页基于 `gpt-3.5-turbo` 大型自然语言处理模型
+Build by 星河
+
+</div>
+"""
+customCSS = """
+code {
+    display: inline;
+    white-space: break-spaces;
+    border-radius: 6px;
+    margin: 0 2px 0 2px;
+    padding: .2em .4em .1em .4em;
+    background-color: rgba(175,184,193,0.2);
+}
+pre {
+    display: block;
+    white-space: pre;
+    background-color: hsla(0, 0%, 0%, 72%);
+    border: solid 5px var(--color-border-primary) !important;
+    border-radius: 8px;
+    padding: 0 1.2rem 1.2rem;
+    margin-top: 1em !important;
+    color: #FFF;
+    box-shadow: inset 0px 8px 16px hsla(0, 0%, 0%, .2)
+}
+pre code, pre code code {
+    background-color: transparent !important;
+    margin: 0;
+    padding: 0;
+}
+"""

utils.py

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+"""Contains all of the components that can be used with Gradio Interface / Blocks.
+Along with the docs for each component, you can find the names of example demos that use
+each component. These demos are located in the `demo` directory."""
+
+from __future__ import annotations
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Tuple, Type
+import json
+import gradio as gr
+# import openai
+import os
+import traceback
+import requests
+# import markdown
+import csv
+import mdtex2html
+
+if TYPE_CHECKING:
+    from typing import TypedDict
+
+    class DataframeData(TypedDict):
+        headers: List[str]
+        data: List[List[str | int | bool]]
+
+initial_prompt = "你是 ChatGPT，由 OpenAI 训练的大型语言模型。请尽可能简洁地回答问题。你的数据库截止日期为：2021年9。"
+API_URL = "https://api.openai.com/v1/chat/completions"
+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 mdtex2html.convert(message),
+                None if response is None else mdtex2html.convert(response),
+            )
+        return y
+
+def parse_text(text):
+    lines = text.split("\n")
+    lines = [line for line in lines if line != ""]
+    count = 0
+    firstline = False
+    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 predict(inputs, top_p, temperature, openai_api_key, chatbot=[], history=[], system_prompt=initial_prompt, retry=False, summary=False, retry_on_crash = False, stream = True):  # repetition_penalty, top_k
+
+    if retry_on_crash:
+        retry = True
+
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {openai_api_key}"
+    }
+
+    chat_counter = len(history) // 2
+
+    print(f"chat_counter - {chat_counter}")
+
+    messages = []
+    if chat_counter:
+        for index in range(0, 2*chat_counter, 2):
+            temp1 = {}
+            temp1["role"] = "user"
+            temp1["content"] = history[index]
+            temp2 = {}
+            temp2["role"] = "assistant"
+            temp2["content"] = history[index+1]
+            if temp1["content"] != "":
+                if temp2["content"] != "" or retry:
+                    messages.append(temp1)
+                    messages.append(temp2)
+            else:
+                messages[-1]['content'] = temp2['content']
+    if retry and chat_counter:
+        if retry_on_crash:
+            messages = messages[-6:]
+        messages.pop()
+    elif summary:
+        history = [*[i["content"] for i in messages[-2:]], "我们刚刚聊了什么？"]
+        messages.append(compose_user(
+            "请帮我总结一下上述对话的内容，实现减少字数的同时，保证对话的质量。在总结中不要加入这一句话。"))
+    else:
+        temp3 = {}
+        temp3["role"] = "user"
+        temp3["content"] = inputs
+        messages.append(temp3)
+        chat_counter += 1
+    messages = [compose_system(system_prompt), *messages]
+    # messages
+    payload = {
+        "model": "gpt-3.5-turbo",
+        "messages": messages,  # [{"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 not summary:
+        history.append(inputs)
+    else:
+        print("精简中...")
+
+    print(f"payload: {payload}")
+    # make a POST request to the API endpoint using the requests.post method, passing in stream=True
+    try:
+        response = requests.post(API_URL, headers=headers, json=payload, stream=True)
+    except:
+        history.append("")
+        chatbot.append(inputs, "")
+        yield history, chatbot, f"出现了网络错误"
+        return
+
+    token_counter = 0
+    partial_words = ""
+
+    counter = 0
+    if stream:
+        chatbot.append((parse_text(history[-1]), ""))
+        for chunk in response.iter_lines():
+            if counter == 0:
+                counter += 1
+                continue
+            counter += 1
+            # check whether each line is non-empty
+            if chunk:
+                # decode each line as response data is in bytes
+                try:
+                    if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
+                        chunkjson = json.loads(chunk.decode()[6:])
+                        status_text = f"id: {chunkjson['id']}, finish_reason: {chunkjson['choices'][0]['finish_reason']}"
+                        yield chatbot, history, status_text
+                        break
+                except Exception as e:
+                    traceback.print_exc()
+                    if not retry_on_crash:
+                        print("正在尝试使用缩短的context重新生成……")
+                        chatbot.pop()
+                        history.append("")
+                        yield next(predict(inputs, top_p, temperature, openai_api_key, chatbot, history, system_prompt, retry, summary=False, retry_on_crash=True, stream=False))
+                    else:
+                        msg = "☹️发生了错误：生成失败，请检查网络"
+                        print(msg)
+                        history.append(inputs, "")
+                        chatbot.append(inputs, msg)
+                        yield chatbot, history, "status: ERROR"
+                    break
+                chunkjson = json.loads(chunk.decode()[6:])
+                status_text = f"id: {chunkjson['id']}, finish_reason: {chunkjson['choices'][0]['finish_reason']}"
+                partial_words = partial_words + \
+                    json.loads(chunk.decode()[6:])[
+                        'choices'][0]["delta"]["content"]
+                if token_counter == 0:
+                    history.append(" " + partial_words)
+                else:
+                    history[-1] = partial_words
+                chatbot[-1] = (parse_text(history[-2]), parse_text(history[-1]))
+                token_counter += 1
+                yield chatbot, history, status_text
+    else:
+        try:
+            responsejson = json.loads(response.text)
+            content = responsejson["choices"][0]["message"]["content"]
+            history.append(content)
+            chatbot.append((parse_text(history[-2]), parse_text(content)))
+            status_text = "精简完成"
+        except:
+            chatbot.append((parse_text(history[-1]), "☹️发生了错误，请检查网络连接或者稍后再试。"))
+            status_text = "status: ERROR"
+        yield chatbot, history, status_text
+
+
+
+def delete_last_conversation(chatbot, history):
+    if "☹️发生了错误" in chatbot[-1][1]:
+        chatbot.pop()
+        print(history)
+        return chatbot, history
+    history.pop()
+    history.pop()
+    print(history)
+    return chatbot, history
+
+def save_chat_history(filename, system, history, chatbot):
+    if filename == "":
+        return
+    if not filename.endswith(".json"):
+        filename += ".json"
+    os.makedirs(HISTORY_DIR, exist_ok=True)
+    json_s = {"system": system, "history": history, "chatbot": chatbot}
+    print(json_s)
+    with open(os.path.join(HISTORY_DIR, filename), "w") as f:
+        json.dump(json_s, f)
+
+
+def load_chat_history(filename):
+    with open(os.path.join(HISTORY_DIR, filename), "r") as f:
+        json_s = json.load(f)
+    print(json_s)
+    return filename, json_s["system"], json_s["history"], json_s["chatbot"]
+
+
+def get_file_names(dir, plain=False, filetype=".json"):
+    # find all json files in the current directory and return their names
+    try:
+        files = sorted([f for f in os.listdir(dir) if f.endswith(filetype)])
+    except FileNotFoundError:
+        files = []
+    if plain:
+        return files
+    else:
+        return gr.Dropdown.update(choices=files)
+
+def get_history_names(plain=False):
+    return get_file_names(HISTORY_DIR, plain)
+
+def load_template(filename, mode=0):
+    lines = []
+    with open(os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8") as csvfile:
+        reader = csv.reader(csvfile)
+        lines = list(reader)
+    lines = lines[1:]
+    if mode == 1:
+        return sorted([row[0] for row in lines])
+    elif mode == 2:
+        return {row[0]:row[1] for row in lines}
+    else:
+        return {row[0]:row[1] for row in lines}, gr.Dropdown.update(choices=sorted([row[0] for row in lines]))
+
+def get_template_names(plain=False):
+    return get_file_names(TEMPLATES_DIR, plain, filetype=".csv")
+
+def reset_state():
+    return [], []
+
+
+def compose_system(system_prompt):
+    return {"role": "system", "content": system_prompt}
+
+
+def compose_user(user_input):
+    return {"role": "user", "content": user_input}
+
+
+def reset_textbox():
+    return gr.update(value='')