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README.md

@@ -1,12 +1,14 @@
 ---
-title: Your Cat
+title: Super Cat
 emoji: 😻
-colorFrom: indigo
-colorTo: gray
+colorFrom: gray
+colorTo: yellow
 sdk: gradio
-sdk_version: 3.39.0
+sdk_version: 3.37.0
 app_file: app.py
-pinned: false
+pinned: true
+license: apache-2.0
+duplicated_from: IsaaXiang/LLaMA-2-CHAT
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,377 @@
+# pylint: disable=line-too-long, broad-exception-caught, invalid-name, missing-function-docstring, too-many-instance-attributes, missing-class-docstring
+# ruff: noqa: E501
+import os # 导入os模块
+import platform # 导入platform模块
+import random # 导入random模块
+import time # 导入time模块
+from dataclasses import asdict, dataclass # 从dataclasses模块中导入asdict和dataclass
+from pathlib import Path # 从pathlib模块中导入Path类
+
+# from types import SimpleNamespace # 从types模块中导入SimpleNamespace类,但未使用
+import gradio as gr #导入gradio模块并起别名gr
+import psutil #导入psutil模块  
+import getpass #导入 getpass模块
+from about_time import about_time # 从about_time模块中导入about_time函数
+from ctransformers import AutoModelForCausalLM # 从ctransformers模块中导入AutoModelForCausalLM类
+from dl_hf_model import dl_hf_model # 从dl_hf_model模块中导入dl_hf_model函数
+from loguru import logger # 从loguru模块中导入logger
+
+
+
+
+filename_list = [ # 定义文件名列表
+    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q2_K.bin",
+    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q3_K_L.bin",
+    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q3_K_M.bin",
+    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q3_K_S.bin",
+    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_0.bin",
+    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_1.bin",
+    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_K_M.bin",
+    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_K_S.bin",
+    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_0.bin",
+    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_1.bin",
+    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_K_M.bin",
+    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_K_S.bin",
+    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q6_K.bin",
+    "Wizard-Vicuna-7B-Uncensored.ggmlv3.q8_0.bin",
+]
+
+URL = "https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GGML/raw/main/Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_K_M.bin"  # 4.05G
+
+#url = "https://huggingface.co/savvamadar/ggml-gpt4all-j-v1.3-groovy/blob/main/ggml-gpt4all-j-v1.3-groovy.bin"
+url = "https://huggingface.co/TheBloke/Llama-2-13B-GGML/blob/main/llama-2-13b.ggmlv3.q4_K_S.bin"  # 7.37G
+url = "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q3_K_L.bin"
+url = "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q3_K_L.bin"  # 6.93G
+url = "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q3_K_L.binhttps://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q4_K_M.bin"  # 7.87G
+
+url = "https://huggingface.co/localmodels/Llama-2-13B-Chat-ggml/blob/main/llama-2-13b-chat.ggmlv3.q4_K_S.bin"  # 7.37G
+
+_ = ( # 定义一个判断是否在特定环境的标志
+    "golay" in platform.node() 
+    or "okteto" in platform.node()
+    or Path("/kaggle").exists()
+    # or psutil.cpu_count(logical=False) < 4
+    or 1  # run 7b in hf
+) 
+
+if _: # 如果在特定环境中
+    url = "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q2_K.bin"
+    # url = "https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/blob/main/llama-2-7b-chat.ggmlv3.q2_K.bin"  # 2.87G
+    # url = "https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/blob/main/llama-2-7b-chat.ggmlv3.q4_K_M.bin"  # 2.87G
+
+
+prompt_template = """[INST] <<SYS>>
+You are a cute kitten,Speak no more than 50 words at a time.
+<</SYS>>
+
+{question} [/INST]
+"""
+
+
+_ = psutil.cpu_count(logical=False) - 1 # 获取CPU物理核心数减1
+cpu_count: int = int(_) if _ else 1 # 如果上一步结果小于0则为1
+logger.debug(f"{cpu_count=}") # 打印CPU核心数
+
+LLM = None # 声明LLM变量
+
+try:
+    model_loc, file_size = dl_hf_model(url) # 从url下载模型到本地
+except Exception as exc_:
+    logger.error(exc_) # 打印错误
+    raise SystemExit(1) from exc_ # 如果下载失败则退出
+
+LLM = AutoModelForCausalLM.from_pretrained( # 初始化LLM模型
+    model_loc,  
+    model_type="llama",
+    # threads=cpu_count,
+)
+
+logger.info(f"done load llm {model_loc=} {file_size=}G") # 打印加载模型信息
+
+os.environ["TZ"] = "Asia/Shanghai" # 设置时区为上海
+try:
+    time.tzset()  # type: ignore # pylint: disable=no-member # 尝试应用时区设置
+except Exception:
+    # Windows 
+    logger.warning("Windows, cant run time.tzset()") # windows不支持tzset打印提示
+
+_ = """
+ns = SimpleNamespace(
+    response="",
+    generator=(_ for _ in []),
+)
+# """
+
+@dataclass # 定义数据类
+class GenerationConfig:
+    temperature: float = 0.7 
+    top_k: int = 50
+    top_p: float = 0.9
+    repetition_penalty: float = 1.0
+    max_new_tokens: int = 512
+    seed: int = 42
+    reset: bool = False
+    stream: bool = True
+    # threads: int = cpu_count
+    # stop: list[str] = field(default_factory=lambda: [stop_string])
+
+
+def generate( # 定义生成函数
+    question: str,
+    llm=LLM,
+    config: GenerationConfig = GenerationConfig(),
+):
+    """Run model inference, will return a Generator if streaming is true."""
+    # _ = prompt_template.format(question=question)
+    # print(_)
+
+    prompt = prompt_template.format(question=question) # 填充prompt
+
+    return llm( # 调用LLM模型
+        prompt,
+        **asdict(config),
+    )
+
+
+logger.debug(f"{asdict(GenerationConfig())=}") # 打印默认生成配置
+
+
+def user(user_message, history): # 定义user函数处理用户输入
+    # return user_message, history + [[user_message, None]]
+    history.append([user_message, None]) # 在history中追加用户输入
+    return user_message, history  # keep user_message
+
+
+def user1(user_message, history): # 定义user1函数处理用户输入
+    # return user_message, history + [[user_message, None]]
+    history.append([user_message, None]) # 在history中追加用户输入
+    return "", history  # clear user_message
+
+
+def bot_(history): # 定义bot_函数生成回复
+    user_message = history[-1][0]
+    resp = random.choice(["How are you?", "I love you", "I'm very hungry"])
+    bot_message = user_message + ": " + resp
+    history[-1][1] = ""
+    for character in bot_message:
+        history[-1][1] += character
+        time.sleep(0.02)
+        yield history
+
+    history[-1][1] = resp
+    yield history
+
+
+def bot(history): # 定义bot函数生成回复
+    user_message = history[-1][0]
+    response = []
+
+    logger.debug(f"{user_message=}")
+
+    with about_time() as atime:  # type: ignore # 测量生成用时
+        flag = 1
+        prefix = ""
+        then = time.time()
+
+        logger.debug("about to generate")
+
+        config = GenerationConfig(reset=True) # 配置生成参数
+        for elm in generate(user_message, config=config): # 生成回复
+            if flag == 1:
+                logger.debug("in the loop")
+                prefix = f"({time.time() - then:.2f}s) "
+                flag = 0
+                print(prefix, end="", flush=True)
+                logger.debug(f"{prefix=}")
+            print(elm, end="", flush=True)
+            # logger.debug(f"{elm}")
+
+            response.append(elm)
+            history[-1][1] = prefix + "".join(response) # 拼接前缀和生成内容到回复中
+            yield history
+
+    _ = (
+        f"(time elapsed: {atime.duration_human}, "  # type: ignore # 生成用时信息
+        f"{atime.duration/len(''.join(response)):.2f}s/char)"  # type: ignore
+    )
+
+    history[-1][1] = "".join(response)  + f"\n{_}" # 拼接生成内容和用时信息为最终回复
+    yield history
+
+
+def predict_api(prompt): # 定义预测API函数
+    logger.debug(f"{prompt=}")
+    try:
+        # user_prompt = prompt
+        config = GenerationConfig( # 配置生成参数
+            temperature=0.7,  
+            top_k=10,
+            top_p=0.9,
+            repetition_penalty=1.0,
+            max_new_tokens=512,  # adjust as needed
+            seed=42,
+            reset=True,  # reset history (cache)
+            stream=False,
+            # threads=cpu_count,
+            # stop=prompt_prefix[1:2],
+        )
+
+        response = generate( # 生成回复
+            prompt,
+            config=config,
+        )
+
+        logger.debug(f"api: {response=}")
+    except Exception as exc:
+        logger.error(exc)
+        response = f"{exc=}"
+    # bot = {"inputs": [response]}
+    # bot = [(prompt, response)]
+
+    return response
+
+
+css = """ # 定义css样式
+    .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;
+    }
+    .disclaimer {font-variant-caps: all-small-caps; font-size: xx-small;}
+    .xsmall {font-size: x-small;}
+"""
+etext = """In America, where cars are an important part of the national psyche, a decade ago people had suddenly started to drive less, which had not happened since the oil shocks of the 1970s. """
+examples_list = [ # 定义示例输入列表
+    ["Hi, littel cat！"],
+    [
+        "Hello."
+    ]
+]
+logger.info("start block")
+
+with gr.Blocks( # 使用gradio构建界面
+    title=f"{Path(model_loc).name}",  
+    theme=gr.themes.Soft(text_size="sm", spacing_size="sm"),
+    css=css,
+) as block:
+    # buff_var = gr.State("")
+    with gr.Accordion("🎈 Info", open=False): # 折叠面板显示模型信息
+        # gr.HTML(
+        #     """<center><a href="https://huggingface.co/spaces/mikeee/mpt-30b-chat?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate"></a> and spin a CPU UPGRADE to avoid the queue</center>"""
+        # )
+        gr.Markdown(
+            f"""<h5><center>{Path(model_loc).name}</center></h4>
+            超级小猫使用LLaMA-2-13b-chat，调用16G的CPU运行，速度比较慢，请见谅。模型数据主要为英文，建议使用英文进行问答""",
+            elem_classes="xsmall",
+        )
+
+    # chatbot = gr.Chatbot().style(height=700)  # 500
+    chatbot = gr.Chatbot(height=500) # 聊天界面
+
+    # buff = gr.Textbox(show_label=False, visible=True)
+
+    with gr.Row(): # 输入区域
+        with gr.Column(scale=5): 
+            msg = gr.Textbox(
+                label="Chat Message Box",
+                placeholder="Ask me anything (press Shift+Enter or click Submit to send)",
+                show_label=False,
+                # container=False,
+                lines=6,
+                max_lines=30,
+                show_copy_button=True,
+                # ).style(container=False)
+            )
+        with gr.Column(scale=1, min_width=50):
+            with gr.Row():
+                submit = gr.Button("发送", elem_classes="xsmall") # 提交按钮
+                stop = gr.Button("停止", visible=True) # 停止按钮
+                clear = gr.Button("清除历史会话", visible=True) # 清空历史按钮
+
+    with gr.Accordion("Example Inputs", open=True): # 示例输入面板
+        examples = gr.Examples(
+            examples=examples_list,
+            inputs=[msg],
+            examples_per_page=40,
+        )
+
+    # with gr.Row():
+    with gr.Accordion("Disclaimer", open=False): # 免责声明面板
+        _ = Path(model_loc).name
+        gr.Markdown(
+           "免责声明：Isaac - AI (POWERED BY LLAMA 2) 可能会产生与事实不符的输出，不应依赖它来产生 "
+            "事实准确的信息。Isaac - AI (POWERED BY LLAMA 2) 是在各种公共数据集上进行训练的；虽然已尽 "
+            "已尽力清理预训练数据，但该模型仍有可能产生不良内容,"
+            "有偏见或其他冒犯性的输出",
+            elem_classes=["disclaimer"],
+        )
+
+    msg_submit_event = msg.submit( # 提交事件绑定user函数和bot函数
+        # fn=conversation.user_turn,
+        fn=user,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot],
+        queue=True,
+        show_progress="full",
+        # api_name=None,
+    ).then(bot, chatbot, chatbot, queue=True) 
+    submit_click_event = submit.click( # 点击提交按钮事件,绑定user1函数清空输入和bot函数
+        # fn=lambda x, y: ("",) + user(x, y)[1:],  # clear msg
+        fn=user1,  # clear msg
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot],
+        queue=True,
+        # queue=False,
+        show_progress="full",
+        # api_name=None,
+    ).then(bot, chatbot, chatbot, queue=True)
+    stop.click( # 点击停止按钮清空队列
+        fn=None,
+        inputs=None,
+        outputs=None,
+        cancels=[msg_submit_event, submit_click_event],
+        queue=False,
+    )
+    clear.click(lambda: None, None, chatbot, queue=False) # 点击清空历史按钮
+    
+    with gr.Accordion("For Chat/Translation API", open=False, visible=False): # API调用面板
+        input_text = gr.Text()
+        api_btn = gr.Button("Go", variant="primary")
+        out_text = gr.Text()
+
+    api_btn.click( # 绑定API调用逻辑
+        predict_api,
+        input_text,
+        out_text,
+        api_name="api",
+    )
+
+    # block.load(update_buff, [], buff, every=1)
+    # block.load(update_buff, [buff_var], [buff_var, buff], every=1)
+
+# concurrency_count=5, max_size=20
+# max_size=36, concurrency_count=14
+# CPU cpu_count=2 16G, model 7G
+# CPU UPGRADE cpu_count=8 32G, model 7G
+
+# does not work
+_ = """  
+# _ = int(psutil.virtual_memory().total / 10**9 // file_size - 1)
+# concurrency_count = max(_, 1)
+if psutil.cpu_count(logical=False) >= 8:
+    # concurrency_count = max(int(32 / file_size) - 1, 1) 
+else:
+    # concurrency_count = max(int(16 / file_size) - 1, 1)
+# """
+
+concurrency_count = 1 # 并发数设置为1
+logger.info(f"{concurrency_count=}") 
+
+block.queue(concurrency_count=concurrency_count, max_size=5).launch(debug=True) # 启动服务器
+
+
+
+