File size: 5,445 Bytes
10a832a
 
 
cb7cdd4
10a832a
 
 
 
 
 
 
700acd6
10a832a
499a604
 
 
 
 
 
 
 
 
10a832a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
cb7cdd4
 
 
10a832a
 
93d0613
cb7cdd4
 
10a832a
cb7cdd4
 
 
 
 
10a832a
cb7cdd4
 
 
93d0613
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
cb7cdd4
 
 
 
 
 
 
93d0613
cb7cdd4
 
 
 
93d0613
cb7cdd4
93d0613
cb7cdd4
 
93d0613
cb7cdd4
 
93d0613
cb7cdd4
 
 
 
 
93d0613
 
 
10a832a
 
ce4c2ae
 
 
 
 
10a832a
ce4c2ae
 
 
cb7cdd4
ce4c2ae
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
import json
import os
import random
import re

import numpy as np
import streamlit as st
import torch
from transformers import FlaxAutoModelForSeq2SeqLM, AutoTokenizer


@st.cache_resource(show_spinner=False)
def load_model(model_name, tokenizer_name):
    try:
        model = FlaxAutoModelForSeq2SeqLM.from_pretrained(model_name)
        tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
    except Exception as e:
        st.error(f"Error loading model: {e}")
        st.error(f"Model not found. Use {DEFAULT_MODEL} instead")
        model_path = DEFAULT_MODEL
        model = FlaxAutoModelForSeq2SeqLM.from_pretrained(model_path)
        tokenizer = AutoTokenizer.from_pretrained(DEFAULT_MODEL)
    return model, tokenizer


def load_json(file_path):
    with open(file_path, 'r', encoding='utf-8') as f:
        data = json.load(f)
    return data


def preprocess(input_text, tokenizer, src_lang, tgt_lang):
    # task_prefix = f"translate {src_lang} to {tgt_lang}: "
    # input_text = task_prefix + input_text
    model_inputs = tokenizer(
        input_text, max_length=MAX_SEQ_LEN, padding="max_length", truncation=True, return_tensors="np"
    )
    return model_inputs


def translate(input_text, model, tokenizer, src_lang, tgt_lang):
    model_inputs = preprocess(input_text, tokenizer, src_lang, tgt_lang)
    model_outputs = model.generate(**model_inputs, num_beams=NUM_BEAMS)
    prediction = tokenizer.batch_decode(model_outputs.sequences, skip_special_tokens=True)
    return prediction[0]


def hold_deterministic(seed):
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    random.seed(seed)


def postprocess(output_text):
    output = re.sub(r"<extra_id[^>]*>", "", output_text)
    return output


def display_ui():
    st.set_page_config(page_title="DP-NMT DEMO", layout="wide")
    st.title("Neural Machine Translation with DP-SGD")

    st.write(
        "[![Star](https://img.shields.io/github/stars/trusthlt/dp-nmt.svg?logo=github&style=social)](https://github.com/trusthlt/dp-nmt)"
        "&nbsp;&nbsp;&nbsp;"
        "[![ACL](https://img.shields.io/badge/ACL-Link-red.svg?logo=data:image/svg%2bxml;base64,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&link=https%3A%2F%2Faclanthology.org%2F2024.eacl-demo.11%2F)](https://aclanthology.org/2024.eacl-demo.11/)"
    )

    st.write("This is a demo for private neural machine translation with DP-SGD.")

    left, right = st.columns(2)
    return left, right


def load_selected_model(config, dataset, language_pair, epsilon):
    ckpt = config[dataset]['languages pairs'][language_pair]['epsilon'][str(epsilon)]
    if "privalingo" in ckpt:
        model_path = ckpt  # load model from huggingface hub
    else:
        model_name = DEFAULT_MODEL.split('/')[-1]
        model_path = os.path.join(CHECKPOINTS_DIR, ckpt, model_name)
        if not os.path.exists(model_path):
            st.error(f"Model not found. Using default model: {DEFAULT_MODEL}")
            model_path = DEFAULT_MODEL
    return model_path


def main():
    hold_deterministic(SEED)

    left, right = display_ui()

    with left:
        dataset = st.selectbox("Choose a dataset used for fine-tuning", list(DATASETS_MODEL_INFO.keys()))
        language_pairs_list = list(DATASETS_MODEL_INFO[dataset]["languages pairs"].keys())
        language_pair = st.selectbox("Language pair for translation", language_pairs_list)
        src_lang, tgt_lang = language_pair.split("-")
        epsilon_options = list(DATASETS_MODEL_INFO[dataset]['languages pairs'][language_pair]['epsilon'].keys())
        epsilon = st.radio("Select a privacy budget epsilon", epsilon_options, horizontal=True)
        model_status_box = st.empty()

    with right:
        input_text = st.text_area("Enter Text", "Enter Text Here", max_chars=MAX_INPUT_LEN)
        btn_translate = st.button("Translate")
        result_container = st.empty()

    model_path = load_selected_model(config, dataset, language_pair, epsilon)

    with left:
        model_status_box.write("")
        with st.spinner(f'Loading model trained on {dataset} with epsilon {epsilon}...'):
            model, tokenizer = load_model(model_path, tokenizer_name=DEFAULT_MODEL)
        model_status_box.success('Model loaded!')

    if btn_translate:
        with right:
            with st.spinner("Translating..."):
                prediction = translate(input_text, model, tokenizer, src_lang, tgt_lang)
            result_container.write("**Translation:**")
            output_container = result_container.container(border=True)
            output_container.write("".join([postprocess(prediction)]))


if __name__ == '__main__':
    DATASETS_MODEL_INFO_PATH = os.path.join(os.getcwd(), "dataset_and_model_info.json")
    print(DATASETS_MODEL_INFO_PATH)
    DATASETS_MODEL_INFO = load_json(DATASETS_MODEL_INFO_PATH)
    DEFAULT_MODEL = 'google/mt5-small'

    MAX_SEQ_LEN = 512
    NUM_BEAMS = 3
    SEED = 2023
    MAX_INPUT_LEN = 500
    main()