app.py

# -*- coding: utf-8 -*-
"""Fujisaki_CPU.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1Damnr0Ha4zZAlKFvne9cu76uuElLNYus

李萌萌的电子骨灰盒
----

这是一个通过ChatGLM模型训练的李萌萌的数字分身，你可以在问题栏目填入内容，或者什么都不填，来观察李萌萌到底会说些什么。
T4级别的GPU已经可以很胜任这个任务了。

### 安装依赖
"""

from modeling_chatglm import ChatGLMForConditionalGeneration
import torch
import sys

from transformers import AutoTokenizer, GenerationConfig

model = ChatGLMForConditionalGeneration.from_pretrained("THUDM/chatglm-6b").float()
tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)

from peft import get_peft_model, LoraConfig, TaskType, PeftModel
peft_path = 'ljsabc/Fujisaki_GLM'      # change it to your own
model = PeftModel.from_pretrained(
       model,
       peft_path,
       torch_dtype=torch.float,
    )

# We have to use full precision, as some tokens are >65535
model.eval()

torch.set_default_tensor_type(torch.FloatTensor)
def evaluate(context, temperature, top_p, top_k):
    generation_config = GenerationConfig(
        temperature=temperature,
        top_p=top_p,
        top_k=top_k,
        #repetition_penalty=1.1,
        num_beams=1,
        do_sample=True,
    )
    with torch.no_grad():
        input_text = f"Context: {context}Answer: " 
        ids = tokenizer.encode(input_text)
        input_ids = torch.LongTensor([ids]).to('cpu')
        out = model.generate(
            input_ids=input_ids,
            max_length=160,
            generation_config=generation_config
        )
        out_text = tokenizer.decode(out[0]).split("Answer: ")[1]
        return out_text

import gradio as gr
gr.Interface(
    fn=evaluate,
    inputs=[
        gr.components.Textbox(
            lines=2, label="问题", placeholder="最近过得怎么样？",
            info="可以在这里输入你的问题。也可以什么都不填写生成随机数据。"
        ),
        #gr.components.Textbox(lines=2, label="Input", placeholder="none"),
        gr.components.Slider(minimum=0, maximum=1.1, value=1.0, label="Temperature",
            info="温度参数，越高的温度生成的内容越丰富，但是有可能出现语法问题。"),
        gr.components.Slider(minimum=0.5, maximum=1.0, value=0.99, label="Top p",
            info="top-p参数，只输出前p>top-p的文字，建议不要修改。"),
        gr.components.Slider(minimum=1, maximum=200, step=1, value=25, label="Top k",
            info="top-k参数，下一个输出的文字会从top-k个文字中进行选择，越大生成的内容越丰富，但也可能出现语法问题。数字越小似乎上下文的衔接性越好。"),
    ],
    outputs=[
        gr.inputs.Textbox(
            lines=5,
            label="Output",
        )
    ],
    title="李萌萌（Alter Ego）",
    description="这是一个通过ChatGLM模型训练的李萌萌的数字分身，你可以在问题栏目填入内容，或者什么都不填，来观察李萌萌到底会说些什么。",
).launch()