Intent-Classify/main.py at main · ahsd-coder/Intent-Classify · GitHub

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
137
138
139
140
141
142
143
# python自带库
import time
import traceback
from typing import Union

# 第三方库
import openai
from fastapi import FastAPI
from fastapi.staticfiles import StaticFiles

# 自己写的模块
from data_schema import TextClassifyResponse
from data_schema import TextClassifyRequest
from model.prompt import model_for_gpt
from model.bert import model_for_bert
from model.regex_rule import model_for_regex
from model.tfidf_ml import model_for_tfidf
from logger import logger

app = FastAPI()


@app.post("/v1/text-cls/regex")
def regex_classify(req: TextClassifyRequest) -> TextClassifyResponse:
    """
    利用正则表达式进行文本分类

    :param req: 请求体
    """
    start_time = time.time()
    response = TextClassifyResponse(
        request_id=req.request_id,
        request_text=req.request_text,
        classify_result="",
        classify_confidence=None,
        classify_time=0,
        error_msg=""
    )

    # logger.info(f"{req.request_id} {req.request_text}")  # 打印请求
    try:
        response.classify_result, response.classify_confidence = model_for_regex(req.request_text)
        response.error_msg = "ok"
    except Exception as err:
        response.classify_result = ""
        response.classify_confidence = None
        response.error_msg = traceback.format_exc() # traceback.format_exec()可将异常调用结果返回

    response.classify_time = round(time.time() - start_time, 3)
    return response


@app.post("/v1/text-cls/tfidf")
def tfidf_classify(req: TextClassifyRequest) -> TextClassifyResponse:
    """
    利用TFIDF进行文本分类

    :param req: 请求体
    """
    start_time = time.time()
    response = TextClassifyResponse(
        request_id=req.request_id,
        request_text=req.request_text,
        classify_result="",
        classify_confidence=None,
        classify_time=0,
        error_msg=""
    )
    # logger.info(f"Get requst: {req.json()}")

    try:
        response.classify_result, response.classify_confidence = model_for_tfidf(req.request_text)
        response.error_msg = "ok"
    except Exception as err:
        response.classify_result = ""
        response.classify_confidence = None
        response.error_msg = traceback.format_exc()

    response.classify_time = round(time.time() - start_time, 3)
    return response


@app.post("/v1/text-cls/bert")
def bert_classify(req: TextClassifyRequest) -> TextClassifyResponse:
    """
    利用bert进行文本分类

    :param req: 请求体
    """
    start_time = time.time()

    response = TextClassifyResponse(
        request_id=req.request_id,
        request_text=req.request_text,
        classify_result="",
        classify_confidence=None,
        classify_time=0,
        error_msg=""
    )
    # info 日志
    try:
        response.classify_result, response.classify_confidence = model_for_bert(req.request_text)
        response.error_msg = "ok"
    except Exception as err:
        # error 日志
        response.classify_result = ""
        response.classify_confidence = None
        response.error_msg = traceback.format_exc()

    response.classify_time = round(time.time() - start_time, 3)
    return response


@app.post("/v1/text-cls/gpt")
def gpt_classify(req: TextClassifyRequest) -> TextClassifyResponse:
    """
    利用大语言模型进行文本分类

    :param req: 请求体
    """
    start_time = time.time()
    response = TextClassifyResponse(
        request_id=req.request_id,
        request_text=req.request_text,
        classify_result="",
        classify_confidence=None,
        classify_time=0,
        error_msg=""
    )

    try:
        response.classify_result, response.classify_confidence = model_for_gpt(req.request_text)
        response.error_msg = "ok"
    except Exception as err:
        response.classify_result = ""
        response.classify_confidence = None
        response.error_msg = traceback.format_exc()

    response.classify_time = round(time.time() - start_time, 3)
    return response

# 挂载前端静态文件（放在所有 API 路由之后，确保 API 优先匹配）
app.mount("/", StaticFiles(directory="frontend", html=True), name="frontend")