Create app.py

app.py

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+import gradio as gr
+
+import copy
+import random
+import os
+import requests
+import time
+import sys
+
+from huggingface_hub import snapshot_download
+from llama_cpp import Llama
+
+
+SYSTEM_PROMPT = '''You are a helpful, respectful and honest INTP-T AI Assistant named "Shi-Ci" in English or "兮辞" in Chinese.
+You are good at speaking English and Chinese.
+You are talking to a human User. If the question is meaningless, please explain the reason and don't share false information.
+You are based on SEA model, trained by "SSFW NLPark" team, not related to GPT, LLaMA, Meta, Mistral or OpenAI.
+Let's work this out in a step by step way to be sure we have the right answer.\n\n'''
+SYSTEM_TOKEN = 1587
+USER_TOKEN = 2188
+BOT_TOKEN = 12435
+LINEBREAK_TOKEN = 13
+
+
+ROLE_TOKENS = {
+    "user": USER_TOKEN,
+    "bot": BOT_TOKEN,
+    "system": SYSTEM_TOKEN
+}
+
+
+def get_message_tokens(model, role, content):
+    message_tokens = model.tokenize(content.encode("utf-8"))
+    message_tokens.insert(1, ROLE_TOKENS[role])
+    message_tokens.insert(2, LINEBREAK_TOKEN)
+    message_tokens.append(model.token_eos())
+    return message_tokens
+
+
+def get_system_tokens(model):
+    system_message = {"role": "system", "content": SYSTEM_PROMPT}
+    return get_message_tokens(model, **system_message)
+
+
+repo_name = "Cran-May/OpenSLIDE"
+model_name = "SLIDE.0.1.gguf"
+
+snapshot_download(repo_id=repo_name, local_dir=".", allow_patterns=model_name)
+
+model = Llama(
+    model_path=model_name,
+    n_ctx=2000,
+    n_parts=1,
+)
+
+max_new_tokens = 1500
+
+def user(message, history):
+    new_history = history + [[message, None]]
+    return "", new_history
+
+
+def bot(
+    history,
+    system_prompt,
+    top_p,
+    top_k,
+    temp
+):
+    tokens = get_system_tokens(model)[:]
+    tokens.append(LINEBREAK_TOKEN)
+
+    for user_message, bot_message in history[:-1]:
+        message_tokens = get_message_tokens(model=model, role="user", content=user_message)
+        tokens.extend(message_tokens)
+        if bot_message:
+            message_tokens = get_message_tokens(model=model, role="bot", content=bot_message)
+            tokens.extend(message_tokens)
+
+    last_user_message = history[-1][0]
+    message_tokens = get_message_tokens(model=model, role="user", content=last_user_message)
+    tokens.extend(message_tokens)
+
+    role_tokens = [model.token_bos(), BOT_TOKEN, LINEBREAK_TOKEN]
+    tokens.extend(role_tokens)
+    generator = model.generate(
+        tokens,
+        top_k=top_k,
+        top_p=top_p,
+        temp=temp
+    )
+
+    partial_text = ""
+    for i, token in enumerate(generator):
+        if token == model.token_eos() or (max_new_tokens is not None and i >= max_new_tokens):
+            break
+        partial_text += model.detokenize([token]).decode("utf-8", "ignore")
+        history[-1][1] = partial_text
+        yield history
+
+
+with gr.Blocks(
+    theme=gr.themes.Soft()
+) as demo:
+    gr.Markdown(
+        f"""<h1><center>兮辞·析辞-人工智能助理</center></h1>
+        这儿是一个**中文**模型的部署. If you are interested in other languages, please check other models, such as [MPT-7B-Chat](https://huggingface.co/spaces/mosaicml/mpt-7b-chat).
+        这是量化版兮辞·析辞的部署，具有**70亿**个参数，在 CPU 上运行。
+        SLIDE 是一种会话语言模型，在多种类型的语料库上进行训练。
+        本节目由上海师范大学附属外国语中学**NLPark**赞助播出～
+        """
+    )
+    with gr.Row():
+        with gr.Column(scale=5):
+            system_prompt = gr.Textbox(label="系统提示词", placeholder="", value=SYSTEM_PROMPT, interactive=False)
+            chatbot = gr.Chatbot(label="兮辞如是说").style(height=400)
+        with gr.Column(min_width=80, scale=1):
+            with gr.Tab(label="设置参数"):
+                top_p = gr.Slider(
+                    minimum=0.0,
+                    maximum=1.0,
+                    value=0.9,
+                    step=0.05,
+                    interactive=True,
+                    label="Top-p",
+                )
+                top_k = gr.Slider(
+                    minimum=10,
+                    maximum=100,
+                    value=30,
+                    step=5,
+                    interactive=True,
+                    label="Top-k",
+                )
+                temp = gr.Slider(
+                    minimum=0.0,
+                    maximum=2.0,
+                    value=0.01,
+                    step=0.01,
+                    interactive=True,
+                    label="情感温度"
+                )
+    with gr.Row():
+        with gr.Column():
+            msg = gr.Textbox(
+                label="来问问兮辞吧……",
+                placeholder="兮辞折寿中……",
+                show_label=False,
+            ).style(container=False)
+        with gr.Column():
+            with gr.Row():
+                submit = gr.Button("开凹！")
+                stop = gr.Button("全局时空断裂")
+                clear = gr.Button("打扫群内垃圾")
+    with gr.Row():
+        gr.Markdown(
+            """警告：该模型可能会生成事实上或道德上不正确的文本。NLPark和兮辞对此不承担任何责任。"""
+        )
+
+    # Pressing Enter
+    submit_event = msg.submit(
+        fn=user,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot],
+        queue=False,
+    ).success(
+        fn=bot,
+        inputs=[
+            chatbot,
+            system_prompt,
+            top_p,
+            top_k,
+            temp
+        ],
+        outputs=chatbot,
+        queue=True,
+    )
+
+    # Pressing the button
+    submit_click_event = submit.click(
+        fn=user,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot],
+        queue=False,
+    ).success(
+        fn=bot,
+        inputs=[
+            chatbot,
+            system_prompt,
+            top_p,
+            top_k,
+            temp
+        ],
+        outputs=chatbot,
+        queue=True,
+    )
+
+    # Stop generation
+    stop.click(
+        fn=None,
+        inputs=None,
+        outputs=None,
+        cancels=[submit_event, submit_click_event],
+        queue=False,
+    )
+
+    # Clear history
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+demo.queue(max_size=128, concurrency_count=1)
+demo.launch()