init

app.py

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+import gradio as gr
+from llm import ChatGLM, OpenAI3, OpenAI4
+from pathlib import Path
+
+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 = "📚📚📚📚📚📚📚📚📚📚📚📚* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *    ☘️ * * *智海文心* * *    ☘️ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *📚📚📚📚📚📚📚📚📚📚📚📚"
+
+generate_prompt = 'please generate {generate_number} difficult multiple-choice questions and attach the answer to each ' \
+                  'question according the paragraph below. Each question should have four choices and only one of them ' \
+                  'is the right answer.' \
+
+correct_prompt = 'the following paragraph is a composition of a student. ' \
+                 'please first judge the writing level of the composition, ' \
+                 'then point out the all the problems with examples, ' \
+                 'and finally output the complete revised version.'
+
+llm_name_dict = {'chatgpt-3.5': 'OpenAI3', 'chatgpt-4': 'OpenAI4', 'chatglm—6b': 'ChatGLM'}
+
+
+def files_list_to_texts(files_list, glob="**/[!.]*"):
+    texts = []
+    for i in files_list:
+        if i.name.split('.')[-1] in ['txt']:
+            with open(i.name, encoding='utf-8') as f:
+                text = f.readlines()
+                text = [i for i in text]
+                texts.append(''.join(text))
+    return texts
+
+
+def function_select(mode):
+    if mode == "问题生成":
+        return gr.update(visible=True), gr.update(visible=False)
+    else:
+        return gr.update(visible=False), gr.update(visible=True)
+
+
+def llm_change(name):
+    llm = eval(eval('llm_name_dict[name]'))()
+    return llm
+
+
+def text_generate(chatbot, text, generate_number, llm):
+    prompt = eval('f"' + generate_prompt + '"') + '\n\n' + text
+    answer = llm(prompt)
+    chatbot = chatbot + [[text, answer]]
+    return chatbot
+
+
+def files_generate(chatbot, files_list, generate_number, llm):
+    try:
+        texts = files_list_to_texts(files_list)
+        for text in texts:
+            prompt = eval('f"' + generate_prompt + '"') + '\n\n' + text
+            answer = llm(prompt)
+            chatbot = chatbot + [[text, answer]]
+    except:
+        chatbot = chatbot + [[None, f"所选文件夹中的文件添加失败，请确保文件夹中含有txt类型文件"]]
+    return chatbot
+
+
+def text_correct(chatbot, text, llm):
+    prompt = correct_prompt + '\n\n' + text
+    answer = llm(prompt)
+    chatbot = chatbot + [[text, answer]]
+    return chatbot
+
+
+def files_correct(chatbot, files_list, llm):
+    try:
+        texts = files_list_to_texts(files_list)
+        for text in texts:
+            prompt = correct_prompt + '\n\n' + text
+            answer = llm(prompt)
+            chatbot = chatbot + [[text, answer]]
+    except:
+        chatbot = chatbot + [[None, f"所选文件夹中的文件添加失败，请确保文件夹中含有txt类型文件"]]
+    return chatbot
+
+
+def clear_screen(chatbot):
+
+    return [[None, None]]
+
+
+with gr.Blocks(css=block_css) as demo:
+    gr.Markdown('\n\n\n\n')
+    gr.Markdown(webui_title)
+    gr.Markdown('\n\n\n\n')
+
+    llm = gr.State('')
+    model_mode = gr.Radio(['chatglm—6b', "chatgpt-3.5", "chatgpt-4"], label="请选择驱动模型")
+    model_mode.change(fn=llm_change, inputs=[model_mode], outputs=[llm])
+
+    fun_mode = gr.Radio(["问题生成", "作文批改"], label="请选择功能模式")
+    qg = gr.Row(visible=False)
+    aa = gr.Row(visible=False)
+    fun_mode.change(fn=function_select, inputs=[fun_mode], outputs=[qg, aa])
+
+    with qg:
+
+        with gr.Column(scale=10):
+            chatbot = gr.Chatbot([[None, None]],
+                                 elem_id="chat-box",
+                                 show_label=False).style(height=800)
+            clear_button = gr.Button(value="清屏")
+            clear_button.click(fn=clear_screen, inputs=[chatbot], outputs=[chatbot])
+
+        with gr.Column(scale=10):
+
+            with gr.Tab('生成配置'):
+                generate_number = gr.Slider(1,
+                                            5,
+                                            value=3,
+                                            step=1,
+                                            label="请设定单篇文章需要生成的问题数量",
+                                            interactive=True)
+
+                gr.Markdown(f'单篇生成')
+                texts = gr.Textbox(show_label=False, placeholder="文本内容", lines=12).style(container=False)
+                text_button = gr.Button(value="生成问题")
+                text_button.click(fn=text_generate, inputs=[chatbot, texts, generate_number, llm], outputs=[chatbot])
+
+                gr.Markdown(f'批量生成')
+                folder_address = gr.File(label="添加文件",
+                                file_types=['.txt', '.md', '.docx', '.pdf'],
+                                file_count="multiple",
+                                show_label=False
+                                )
+                file_button = gr.Button(value="生成问题")
+                file_button.click(fn=files_generate, inputs=[chatbot, folder_address, generate_number, llm],
+                                  outputs=[chatbot])
+
+
+    with aa:
+
+        with gr.Column(scale=10):
+            chatbot = gr.Chatbot([[None, None]],
+                                 elem_id="chat-box",
+                                 show_label=False).style(height=800)
+            clear_button = gr.Button(value="清屏")
+            clear_button.click(fn=clear_screen, inputs=[chatbot], outputs=[chatbot])
+
+        with gr.Column(scale=10):
+
+            with gr.Tab('批改配置'):
+
+                gr.Markdown(f'单篇批改')
+                texts = gr.Textbox(show_label=False, placeholder="文本内容", lines=16).style(container=False)
+                text_button = gr.Button(value="批改")
+                text_button.click(fn=text_correct, inputs=[chatbot, texts, llm], outputs=[chatbot])
+
+                gr.Markdown(f'批量批改')
+                folder_address = gr.File(label="添加文件",
+                                         file_types=['.txt', '.md', '.docx', '.pdf'],
+                                         file_count="multiple",
+                                         show_label=False
+                                         )
+                file_button = gr.Button(value="批改")
+                file_button.click(fn=files_correct, inputs=[chatbot, folder_address, llm], outputs=[chatbot])
+
+
+demo.queue(concurrency_count=5).launch(show_api=False)

llm.py

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+from langchain.llms.base import LLM
+from typing import Optional, List
+from langchain.llms.utils import enforce_stop_tokens
+import requests
+
+FORWARD_KEY = 'fk198719-Pmvv22OqZiovaxRq6YxCzkTcd6UVVX5O'
+
+class ChatGLM(LLM):
+    max_length: int = 10000
+    temperature: float = 0
+    top_p = 0.9
+    tokenizer: object = None
+    model: object = None
+    history_len: int = 10
+    history = []
+    URL = 'http://183.131.3.48:9200'
+    HEADERS = {'Content-Type': 'application/json'}
+
+    @property
+    def _llm_type(self) -> str:
+        return "ChatGLM"
+
+    def _call(self,
+              prompt: str,
+              history:  Optional[List[List[str]]] = None,
+              stop: Optional[List[str]] = None) -> str:
+
+        if history:
+            history = [i for i in history if i[0] is not None]  # clear out the system message
+            history = history[-self.history_len:]
+
+        params = {'tokenizers': self.tokenizer, 'prompt': prompt, 'history': history, 'top_p': self.top_p,
+                  'max_length': self.max_length, 'temperature': self.temperature}
+
+        response = requests.post(self.URL, headers=self.HEADERS, json=params).json()
+        answer = response['response']
+        if stop is not None:
+            answer = enforce_stop_tokens(answer, stop)
+        # question = prompt.split('question:\n')[-1]
+        # self.history = self.history+[[prompt, response]]
+
+        return answer
+
+
+class OpenAI3(LLM):
+    max_length: int = 10000
+    temperature: float = 0.2
+    top_p = 0.9
+    tokenizer: object = None
+    model: object = None
+    history_len: int = 10
+    history = []
+    HEADERS = {'Content-Type': 'application/json', 'Authorization': 'Bearer fk198719-Pmvv22OqZiovaxRq6YxCzkTcd6UVVX5O'}
+    URL ='https://openai.api2d.net/v1/chat/completions'
+    MODEL_NAME = "gpt-3.5-turbo"
+
+
+    @property
+    def _llm_type(self) -> str:
+        return "ChatGLM"
+
+    def _call(self,
+              prompt: str,
+              history: Optional[List[List[str]]] = None,
+              stop: Optional[List[str]] = None) -> str:
+
+        if history:
+            history = [i for i in history if i[0] is not None]
+            history = history[-self.history_len:]
+            message = [[{"role": "user", "content": i[0]}, {"role": "assistant", "content": i[1]}] for i in history]
+            message = sum(message, [])
+        else:
+            message = []
+        message.append({"role": "user", "content": prompt})
+        params = {"model": self.MODEL_NAME, "messages": message, 'temperature': self.temperature}
+        response = requests.post(self.URL, headers=self.HEADERS, json=params).json()
+        answer = response['choices'][0]['message']['content']
+        if stop is not None:
+            answer = enforce_stop_tokens(answer, stop)
+        # question = prompt.split('question:\n')[-1]
+        # self.history = self.history+[[question, response]]
+
+        return answer
+
+
+class OpenAI4(LLM):
+    max_length: int = 10000
+    temperature: float = 0.2
+    top_p = 0.9
+    tokenizer: object = None
+    model: object = None
+    history_len: int = 10
+    history = []
+    HEADERS = {'Content-Type': 'application/json', 'Authorization': 'Bearer fk198719-Pmvv22OqZiovaxRq6YxCzkTcd6UVVX5O'}
+    URL ='https://openai.api2d.net/v1/chat/completions'
+    MODEL_NAME = "gpt-4"
+
+
+    @property
+    def _llm_type(self) -> str:
+        return "ChatGLM"
+
+    def _call(self,
+              prompt: str,
+              history: Optional[List[List[str]]] = None,
+              stop: Optional[List[str]] = None) -> str:
+
+        if history:
+            history = [i for i in history if i[0] is not None]
+            history = history[-self.history_len:]
+            message = [[{"role": "user", "content": i[0]}, {"role": "assistant", "content": i[1]}] for i in history]
+            message = sum(message, [])
+        else:
+            message = []
+        message.append({"role": "user", "content": prompt})
+        params = {"model": self.MODEL_NAME, "messages": message, 'temperature': self.temperature}
+        response = requests.post(self.URL, headers=self.HEADERS, json=params).json()
+        answer = response['choices'][0]['message']['content']
+        if stop is not None:
+            answer = enforce_stop_tokens(answer, stop)
+        # question = prompt.split('question:\n')[-1]
+        # self.history = self.history+[[question, response]]
+
+        return answer
+
+

requirements.txt

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+gradio==3.28.3
+langchain==0.0.161
+Requests==2.30.0