File size: 4,346 Bytes
8cef51d
 
 
 
 
 
 
 
 
 
 
 
 
7e4c181
8cef51d
1146910
f0d994d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8cef51d
f0d994d
8cef51d
 
 
f0d994d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8cef51d
f0d994d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8cef51d
f0d994d
 
 
8cef51d
f0d994d
 
8cef51d
f0d994d
 
 
 
8cef51d
f0d994d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8cef51d
 
 
f0d994d
 
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
144
145
146
147
148
149
import streamlit as st
from transformers import pipeline

unmasker = pipeline('fill-mask', model='dsfsi/zabantu-ven-120m')

def fill_mask(sentences):
    results = {}
    for sentence in sentences:
        unmasked = unmasker(sentence)
        results[sentence] = unmasked
    return results

def replace_mask(sentence, predicted_word):
    return sentence.replace("<mask>", f"**{predicted_word}**")

st.title("Fill Mask | Zabantu-ven-120m")
st.write(f"")

col1, col2 = st.columns(2)

with col1:
    sample_sentences = [
        "Vhana vhane vha kha ḓi bva u bebwa vha kha khombo ya u <mask> nga Listeriosis"
    ]

    text_input = st.text_area(
        "Enter sentences with <mask> token (one per line):",
        "\n".join(sample_sentences)
    )

    input_sentences = text_input.split(",")

    if st.button("Submit"):
        result = fill_mask(input_sentences)

with col2:
    if 'result' in locals():  
        if result:
            for sentence, predictions in result.items():
                for prediction in predictions:
                    predicted_word = prediction['token_str']
                    score = prediction['score'] * 100

                    st.markdown(f"""
                    <div class="bar">
                        <div class="bar-fill" style="width: {score}%;"></div>
                    </div>
                    <div class="container">
                        <div style="align-items: left;">{predicted_word}</div>
                        <div style="align-items: right;">{score:.2f}%</div>
                    </div>
                    """, unsafe_allow_html=True)

if 'result' in locals():  
        if result:
            for sentence, predictions in result.items():
                predicted_word = predictions[0]['token_str']
                full_sentence = replace_mask(sentence, predicted_word)
                st.write(f"**Sentence:** {full_sentence }")

css = """
<style>
footer {display:none !important;}

.gr-button-primary {
    z-index: 14;
    height: 43px;
    width: 130px;
    left: 0px;
    top: 0px;
    padding: 0px;
    cursor: pointer !important; 
    background: none rgb(17, 20, 45) !important;
    border: none !important;
    text-align: center !important;
    font-family: Poppins !important;
    font-size: 14px !important;
    font-weight: 500 !important;
    color: rgb(255, 255, 255) !important;
    line-height: 1 !important;
    border-radius: 12px !important;
    transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
    box-shadow: none !important;
}
.gr-button-primary:hover{
    z-index: 14;
    height: 43px;
    width: 130px;
    left: 0px;
    top: 0px;
    padding: 0px;
    cursor: pointer !important;
    background: none rgb(66, 133, 244) !important;
    border: none !important;
    text-align: center !important;
    font-family: Poppins !important;
    font-size: 14px !important;
    font-weight: 500 !important;
    color: rgb(255, 255, 255) !important;
    line-height: 1 !important;
    border-radius: 12px !important;
    transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
    box-shadow: rgb(0 0 0 / 23%) 0px 1px 7px 0px !important;
}
.hover\:bg-orange-50:hover {
    --tw-bg-opacity: 1 !important;
    background-color: rgb(229,225,255) !important;
}
.to-orange-200 {
    --tw-gradient-to: rgb(37 56 133 / 37%) !important;
}
.from-orange-400 {
    --tw-gradient-from: rgb(17, 20, 45) !important;
    --tw-gradient-to: rgb(255 150 51 / 0);
    --tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to) !important;
}
.group-hover\:from-orange-500{
    --tw-gradient-from:rgb(17, 20, 45) !important; 
    --tw-gradient-to: rgb(37 56 133 / 37%);
    --tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to) !important;
}
.group:hover .group-hover\:text-orange-500{
    --tw-text-opacity: 1 !important;
    color:rgb(37 56 133 / var(--tw-text-opacity)) !important;
}
.container {
    display: flex;
    justify-content: space-between;
    align-items: center;
    margin-bottom: 5px;
    width: 100%;
}
.bar {
    width: 70%;
    background-color: #e6e6e6;
    border-radius: 12px;
    overflow: hidden;
    margin-right: 10px;
    height: 5px;
}
.bar-fill {
    background-color: #17152e;
    height: 100%;
    border-radius: 12px;
}
</style>
"""

st.markdown(css, unsafe_allow_html=True)