Hugging Face's logo

Spaces:
amber0628h
/
gpt-academic
Sleeping

App Files Community
gpt-academic
File size: 5,517 Bytes 
e200709
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
 
from toolbox import update_ui
from toolbox import CatchException, report_execption
fast_debug = True


class PaperFileGroup():
    def __init__(self):
        self.file_paths = []
        self.file_contents = []
        self.sp_file_contents = []
        self.sp_file_index = []
        self.sp_file_tag = []

        # count_token
        from request_llm.bridge_all import model_info
        enc = model_info["gpt-3.5-turbo"]['tokenizer']
        def get_token_num(txt): return len(
            enc.encode(txt, disallowed_special=()))
        self.get_token_num = get_token_num

    def run_file_split(self, max_token_limit=1900):
        """
        将长文本分离开来
        """
        for index, file_content in enumerate(self.file_contents):
            if self.get_token_num(file_content) < max_token_limit:
                self.sp_file_contents.append(file_content)
                self.sp_file_index.append(index)
                self.sp_file_tag.append(self.file_paths[index])
            else:
                from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(
                    file_content, self.get_token_num, max_token_limit)
                for j, segment in enumerate(segments):
                    self.sp_file_contents.append(segment)
                    self.sp_file_index.append(index)
                    self.sp_file_tag.append(
                        self.file_paths[index] + f".part-{j}.txt")



def parseNotebook(filename, enable_markdown=1):
    import json

    CodeBlocks = []
    with open(filename, 'r', encoding='utf-8', errors='replace') as f:
        notebook = json.load(f)
    for cell in notebook['cells']:
        if cell['cell_type'] == 'code' and cell['source']:
            # remove blank lines
            cell['source'] = [line for line in cell['source'] if line.strip()
                              != '']
            CodeBlocks.append("".join(cell['source']))
        elif enable_markdown and cell['cell_type'] == 'markdown' and cell['source']:
            cell['source'] = [line for line in cell['source'] if line.strip()
                              != '']
            CodeBlocks.append("Markdown:"+"".join(cell['source']))

    Code = ""
    for idx, code in enumerate(CodeBlocks):
        Code += f"This is {idx+1}th code block: \n"
        Code += code+"\n"

    return Code 


def ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency

    pfg = PaperFileGroup()

    print(file_manifest)
    for fp in file_manifest:
        file_content = parseNotebook(fp, enable_markdown=1)
        pfg.file_paths.append(fp)
        pfg.file_contents.append(file_content)

    #  <-------- 拆分过长的IPynb文件 ---------->
    pfg.run_file_split(max_token_limit=1024)
    n_split = len(pfg.sp_file_contents)

    inputs_array = [f"This is a  Jupyter Notebook file, tell me about Each Block in Chinese. Focus Just On Code." +
                    f"If a block starts with `Markdown` which means it's a markdown block in ipynbipynb. " +
                    f"Start a new line for a block and block num use Chinese." +
                    f"\n\n{frag}" for frag in pfg.sp_file_contents]
    inputs_show_user_array = [f"{f}的分析如下" for f in pfg.sp_file_tag]
    sys_prompt_array = ["You are a professional programmer."] * n_split

    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
        inputs_array=inputs_array,
        inputs_show_user_array=inputs_show_user_array,
        llm_kwargs=llm_kwargs,
        chatbot=chatbot,
        history_array=[[""] for _ in range(n_split)],
        sys_prompt_array=sys_prompt_array,
        # max_workers=5,  # OpenAI所允许的最大并行过载
        scroller_max_len=80
    )

    #  <-------- 整理结果，退出 ---------->
    block_result = "  \n".join(gpt_response_collection)
    chatbot.append(("解析的结果如下", block_result))
    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面


@CatchException
def 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
    chatbot.append([
        "函数插件功能？",
        "对IPynb文件进行解析"])
    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面

    history = []    # 清空历史
    import glob
    import os
    if os.path.exists(txt):
        project_folder = txt
    else:
        if txt == "":
            txt = '空空如也的输入栏'
        report_execption(chatbot, history,
                         a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
        return
    if txt.endswith('.ipynb'):
        file_manifest = [txt]
    else:
        file_manifest = [f for f in glob.glob(
            f'{project_folder}/**/*.ipynb', recursive=True)]
    if len(file_manifest) == 0:
        report_execption(chatbot, history,
                         a=f"解析项目: {txt}", b=f"找不到任何.ipynb文件: {txt}")
        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
        return
    yield from ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, )