Create educhat_gradio.py

educhat_gradio.py

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+#!/usr/bin/env python3
+import argparse
+
+import torch
+import transformers
+from distutils.util import strtobool
+from tokenizers import pre_tokenizers
+
+from transformers.generation.utils import logger
+import mdtex2html
+import gradio as gr
+import warnings
+import os
+
+logger.setLevel("ERROR")
+warnings.filterwarnings("ignore")
+
+import os
+os.system("export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:20")
+os.system("export batch_size=1")
+
+warnings.filterwarnings("ignore")
+
+
+def _strtobool(x):
+    return bool(strtobool(x))
+
+
+QA_SPECIAL_TOKENS = {
+    "Question": "<|prompter|>",
+    "Answer": "<|assistant|>",
+    "System": "<|system|>",
+    "StartPrefix": "<|prefix_begin|>",
+    "EndPrefix": "<|prefix_end|>",
+    "InnerThought": "<|inner_thoughts|>",
+    "EndOfThought": "<eot>"
+}
+
+
+def format_pairs(pairs, eos_token, add_initial_reply_token=False):
+    conversations = [
+        "{}{}{}".format(
+            QA_SPECIAL_TOKENS["Question" if i % 2 == 0 else "Answer"], pairs[i], eos_token)
+        for i in range(len(pairs))
+    ]
+    if add_initial_reply_token:
+        conversations.append(QA_SPECIAL_TOKENS["Answer"])
+    return conversations
+
+
+def format_system_prefix(prefix, eos_token):
+    return "{}{}{}".format(
+        QA_SPECIAL_TOKENS["System"],
+        prefix,
+        eos_token,
+    )
+
+
+def get_specific_model(
+    model_name, seq2seqmodel=False, without_head=False, cache_dir=".cache", quantization=False, **kwargs
+):
+    # encoder-decoder support for Flan-T5 like models
+    # for now, we can use an argument but in the future,
+    # we can automate this
+
+    model = transformers.LlamaForCausalLM.from_pretrained(model_name,torch_dtype=torch.float16, ).half().cuda()
+    
+    return model
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--model_path", type=str, required=True)
+parser.add_argument("--max_new_tokens", type=int, default=200)
+parser.add_argument("--top_k", type=int, default=40)
+parser.add_argument("--do_sample", type=_strtobool, default=True)
+# parser.add_argument("--system_prefix", type=str, default=None)
+parser.add_argument("--per-digit-tokens", action="store_true")
+
+
+args = parser.parse_args()
+
+# # 开放问答
+# system_prefix = \
+# "<|system|>"'''你是一个人工智能助手，名字叫EduChat。
+# - EduChat是一个由华东师范大学开发的对话式语言模型。
+# EduChat的工具
+# - Web search: Disable.
+# - Calculators: Disable.
+# EduChat的能力
+# - Inner Thought: Disable.
+# 对话主题
+# - General: Enable.
+# - Psychology: Disable.
+# - Socrates: Disable.'''"</s>"
+
+# # 启发式教学
+# system_prefix = \
+# "<|system|>"'''你是一个人工智能助手，名字叫EduChat。
+# - EduChat是一个由华东师范大学开发的对话式语言模型。
+# EduChat的工具
+# - Web search: Disable.
+# - Calculators: Disable.
+# EduChat的能力
+# - Inner Thought: Disable.
+# 对话主题
+# - General: Disable.
+# - Psychology: Disable.
+# - Socrates: Enable.'''"</s>"
+
+# 情感支持
+system_prefix = \
+    "<|system|>"'''你是一个人工智能助手，名字叫EduChat。
+- EduChat是一个由华东师范大学开发的对话式语言模型。
+EduChat的工具
+- Web search: Disable.
+- Calculators: Disable.
+EduChat的能力
+- Inner Thought: Disable.
+对话主题
+- General: Disable.
+- Psychology: Enable.
+- Socrates: Disable.'''"</s>"
+
+# # 情感支持(with InnerThought)
+# system_prefix = \
+# "<|system|>"'''你是一个人工智能助手，名字叫EduChat。
+# - EduChat是一个由华东师范大学开发的对话式语言模型。
+# EduChat的工具
+# - Web search: Disable.
+# - Calculators: Disable.
+# EduChat的能力
+# - Inner Thought: Enable.
+# 对话主题
+# - General: Disable.
+# - Psychology: Enable.
+# - Socrates: Disable.'''"</s>"
+
+
+print('Loading model......')
+
+model = get_specific_model(args.model_path)
+
+model.gradient_checkpointing_enable()  # reduce number of stored activations
+
+print('Loading tokenizer...')
+tokenizer = transformers.LlamaTokenizer.from_pretrained(args.model_path)
+
+tokenizer.add_special_tokens(
+    {
+        "pad_token": "</s>",
+        "eos_token": "</s>",
+        "sep_token": "<s>",
+    }
+)
+additional_special_tokens = (
+    []
+    if "additional_special_tokens" not in tokenizer.special_tokens_map
+    else tokenizer.special_tokens_map["additional_special_tokens"]
+)
+additional_special_tokens = list(
+    set(additional_special_tokens + list(QA_SPECIAL_TOKENS.values())))
+
+print("additional_special_tokens:", additional_special_tokens)
+
+tokenizer.add_special_tokens(
+    {"additional_special_tokens": additional_special_tokens})
+
+if args.per_digit_tokens:
+    tokenizer._tokenizer.pre_processor = pre_tokenizers.Digits(True)
+
+human_token_id = tokenizer.additional_special_tokens_ids[
+    tokenizer.additional_special_tokens.index(QA_SPECIAL_TOKENS["Question"])
+]
+
+print('Type "quit" to exit')
+print("Press Control + C to restart conversation (spam to exit)")
+
+conversation_history = []
+
+
+"""Override Chatbot.postprocess"""
+
+
+def postprocess(self, y):
+    if y is None:
+        return []
+    for i, (message, response) in enumerate(y):
+        y[i] = (
+            None if message is None else mdtex2html.convert((message)),
+            None if response is None else mdtex2html.convert(response),
+        )
+    return y
+
+
+gr.Chatbot.postprocess = postprocess
+
+
+def parse_text(text):
+    """copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""
+    lines = text.split("\n")
+    lines = [line for line in lines if line != ""]
+    count = 0
+    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(input, chatbot, max_length, top_p, temperature, history):
+    query = parse_text(input)
+    chatbot.append((query, ""))
+    conversation_history = []
+    for i, (old_query, response) in enumerate(history):
+        conversation_history.append(old_query)
+        conversation_history.append(response)
+
+    conversation_history.append(query)
+
+    query_str = "".join(format_pairs(conversation_history,
+                        tokenizer.eos_token, add_initial_reply_token=True))
+
+    if system_prefix:
+        query_str = system_prefix + query_str
+    print("query:", query_str)
+
+    batch = tokenizer.encode(
+        query_str,
+        return_tensors="pt",
+    )
+
+    with torch.cuda.amp.autocast():
+        out = model.generate(
+            input_ids=batch.to(model.device),
+            # The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt.
+            max_new_tokens=args.max_new_tokens,
+            do_sample=args.do_sample,
+            max_length=max_length,
+            top_k=args.top_k,
+            top_p=top_p,
+            temperature=temperature,
+            eos_token_id=tokenizer.eos_token_id,
+            pad_token_id=tokenizer.eos_token_id,
+        )
+
+    if out[0][-1] == tokenizer.eos_token_id:
+        response = out[0][:-1]
+    else:
+        response = out[0]
+
+    response = tokenizer.decode(out[0]).split(QA_SPECIAL_TOKENS["Answer"])[-1]
+
+    conversation_history.append(response)
+
+    with open("./educhat_query_record.txt", 'a+') as f:
+        f.write(str(conversation_history) + '\n')
+
+    chatbot[-1] = (query, parse_text(response))
+    history = history + [(query, response)]
+    print(f"chatbot is {chatbot}")
+    print(f"history is {history}")
+
+    return chatbot, history
+
+
+def reset_user_input():
+    return gr.update(value='')
+
+
+def reset_state():
+    return [], []
+
+
+with gr.Blocks() as demo:
+    gr.HTML("""<h1 align="center">欢迎使用 EduChat 人工智能助手！</h1>""")
+
+    chatbot = gr.Chatbot()
+    with gr.Row():
+        with gr.Column(scale=4):
+            with gr.Column(scale=12):
+                user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10).style(
+                    container=False)
+            with gr.Column(min_width=32, scale=1):
+                submitBtn = gr.Button("Submit", variant="primary")
+        with gr.Column(scale=1):
+            emptyBtn = gr.Button("Clear History")
+            max_length = gr.Slider(
+                0, 2048, value=2048, step=1.0, label="Maximum length", interactive=True)
+            top_p = gr.Slider(0, 1, value=0.2, step=0.01,
+                              label="Top P", interactive=True)
+            temperature = gr.Slider(
+                0, 1, value=1, step=0.01, label="Temperature", interactive=True)
+
+    history = gr.State([])  # (message, bot_message)
+
+    submitBtn.click(predict, [user_input, chatbot, max_length, top_p, temperature, history], [chatbot, history],
+                    show_progress=True)
+    submitBtn.click(reset_user_input, [], [user_input])
+
+    emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True)
+
+demo.queue().launch(inbrowser=True, share=True)