File size: 10,408 Bytes
390b2d8
70303d6
 
 
f3c6abd
6b2498f
 
70303d6
837b4d7
c7f7675
f3c6abd
efad059
ba0e651
efad059
 
 
 
70303d6
 
f3c6abd
efad059
 
f3c6abd
70303d6
36f8ff0
3522491
efad059
f3c6abd
af1593d
746bea5
 
af1593d
f3c6abd
 
 
70303d6
efad059
f3c6abd
efad059
 
717cbd4
70303d6
 
 
 
 
 
 
456234e
efad059
 
 
 
5091b3f
5696d33
5091b3f
456234e
 
 
efad059
e3e3a86
f08e0a4
497c76e
70303d6
 
efad059
f3c6abd
ba0e651
70303d6
 
 
36f8ff0
837b4d7
ba0e651
efad059
 
 
70303d6
f3c6abd
70303d6
 
 
 
456234e
65a0b31
 
70303d6
 
 
 
 
719360a
759c1e9
70303d6
efad059
f3c6abd
837b4d7
6b2498f
70303d6
65a0b31
 
 
70303d6
456234e
65a0b31
 
f3c6abd
 
 
 
607b0b8
ea51917
f3c6abd
70303d6
efad059
f3c6abd
 
70303d6
 
efad059
33e0532
 
 
 
efad059
f3c6abd
efad059
 
837b4d7
717cbd4
5091b3f
 
5696d33
5091b3f
bf84d89
f3c6abd
efad059
f3c6abd
efad059
 
 
 
 
f3c6abd
af1593d
 
746bea5
 
af1593d
 
 
 
f3c6abd
efad059
d12268b
 
efad059
83ade71
d12268b
f3c6abd
efad059
 
f3c6abd
70303d6
f3c6abd
60e6fe2
 
f3c6abd
efad059
7b2b08e
ba0e651
abf6849
ba0e651
 
efad059
ba0e651
 
efad059
 
ba0e651
 
 
efad059
 
f3c6abd
 
 
3522491
efad059
f3c6abd
 
 
efad059
 
f3c6abd
 
 
efad059
f3c6abd
 
70303d6
f3c6abd
 
6eafe13
f3c6abd
7b2b08e
d0ba71a
68a0312
6eafe13
 
 
497c76e
bf84d89
5091b3f
5696d33
f3c6abd
3522491
f3c6abd
 
 
 
 
efad059
 
 
f3c6abd
 
efad059
 
ba0e651
efad059
7b2b08e
f3c6abd
 
ba0e651
efad059
717cbd4
f3c6abd
497c76e
bf84d89
5696d33
f3c6abd
efad059
f3c6abd
 
 
 
497c76e
f3c6abd
70303d6
390b2d8
70d3e10
efad059
7b2b08e
70d3e10
 
 
 
 
 
 
 
 
6eafe13
efad059
baa6b34
6eafe13
baa6b34
6eafe13
efad059
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
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
import streamlit as st
import streamlit.components.v1 as components
import rebel
import wikipedia
from utils import clip_text
from datetime import datetime as dt
import os

MAX_TOPICS = 3

wiki_state_variables = {
    'has_run_wiki': False,
    'wiki_suggestions': [],
    'wiki_text': [],
    'nodes': [],
    "topics": [],
    "html_wiki": ""
}

free_text_state_variables = {
    'has_run_free': False,
    "html_free": ""
}

BUTTON_COLUMS = 4


def wiki_init_state_variables():
    for k in free_text_state_variables.keys():
        if k in st.session_state:
            del st.session_state[k]

    for k, v in wiki_state_variables.items():
        if k not in st.session_state:
            st.session_state[k] = v


def wiki_generate_graph():
    st.session_state["GRAPH_FILENAME"] = str(
        dt.now().timestamp()*1000) + ".html"

    if 'wiki_text' not in st.session_state:
        return
    if len(st.session_state['wiki_text']) == 0:
        st.error("please enter a topic and select a wiki page first")
        return
    with st.spinner(text="Generating graph..."):
        texts = st.session_state['wiki_text']
        st.session_state['nodes'] = []
        nodes = rebel.generate_knowledge_graph(
            texts, st.session_state["GRAPH_FILENAME"])
        HtmlFile = open(
            st.session_state["GRAPH_FILENAME"], 'r', encoding='utf-8')
        source_code = HtmlFile.read()
        st.session_state["html_wiki"] = source_code
        os.remove(st.session_state["GRAPH_FILENAME"])
        for n in nodes:
            n = n.lower()
            if n not in st.session_state['topics']:
                possible_topics = wikipedia.search(n, results=2)
                st.session_state['nodes'].extend(possible_topics)
        st.session_state['nodes'] = list(set(st.session_state['nodes']))
        st.session_state['has_run_wiki'] = True
    st.success('Done!')


def wiki_show_suggestion():
    st.session_state['wiki_suggestions'] = []
    with st.spinner(text="fetching wiki topics..."):
        if st.session_state['input_method'] == "wikipedia":
            text = st.session_state.text
            if (text is not None) and (text != ""):
                subjects = text.split(",")[:MAX_TOPICS]
                for subj in subjects:
                    st.session_state['wiki_suggestions'] += wikipedia.search(
                        subj, results=3)


def wiki_show_text(page_title):
    with st.spinner(text="fetching wiki page..."):
        try:
            page = wikipedia.page(title=page_title, auto_suggest=False)
            st.session_state['wiki_text'].append(clip_text(page.summary))
            st.session_state['topics'].append(page_title.lower())
            st.session_state['wiki_suggestions'].remove(page_title)

        except wikipedia.DisambiguationError as e:
            with st.spinner(text="Woops, ambigious term, recalculating options..."):
                st.session_state['wiki_suggestions'].remove(page_title)
                temp = st.session_state['wiki_suggestions'] + e.options[:3]
                st.session_state['wiki_suggestions'] = list(set(temp))
        except wikipedia.WikipediaException:
            st.session_state['wiki_suggestions'].remove(page_title)


def wiki_add_text(term):
    if len(st.session_state['wiki_text']) > MAX_TOPICS:
        return
    try:
        page = wikipedia.page(title=term, auto_suggest=False)
        extra_text = clip_text(page.summary)

        st.session_state['wiki_text'].append(extra_text)
        st.session_state['topics'].append(term.lower())
        st.session_state['nodes'].remove(term)

    except wikipedia.DisambiguationError as e:
        with st.spinner(text="Woops, ambigious term, recalculating options..."):
            st.session_state['nodes'].remove(term)
            temp = st.session_state['nodes'] + e.options[:3]
            st.session_state['nodes'] = list(set(temp))
    except wikipedia.WikipediaException as e:
        st.session_state['nodes'].remove(term)


def wiki_reset_session():
    for k in wiki_state_variables:
        del st.session_state[k]


def free_reset_session():
    for k in free_text_state_variables:
        del st.session_state[k]


def free_text_generate():
    st.session_state["GRAPH_FILENAME"] = str(
        dt.now().timestamp()*1000) + ".html"
    text = st.session_state['free_text'][0:100]
    rebel.generate_knowledge_graph([text], st.session_state["GRAPH_FILENAME"])
    HtmlFile = open(st.session_state["GRAPH_FILENAME"], 'r', encoding='utf-8')
    source_code = HtmlFile.read()
    st.session_state["html_free"] = source_code
    os.remove(st.session_state["GRAPH_FILENAME"])
    st.session_state['has_run_free'] = True


def free_text_layout():
    st.text_area("Free text", key="free_text", height=5,
                 value="Tardigrades, known colloquially as water bears or moss piglets, are a phylum of eight-legged segmented micro-animals.")
    st.button("Generate", on_click=free_text_generate,
              key="free_text_generate")


def free_test_init_state_variables():
    for k in wiki_state_variables.keys():
        if k in st.session_state:
            del st.session_state[k]

    for k, v in free_text_state_variables.items():
        if k not in st.session_state:
            st.session_state[k] = v


st.title('RE:Belle')
st.markdown(
    """

### Building Beautiful Knowledge Graphs With REBEL

""")
st.selectbox(
    'input method',
    ('wikipedia', 'free text'),  key="input_method")


def show_wiki_hub_page():
    st.sidebar.button("Reset", on_click=wiki_reset_session, key="reset_key")

    st.sidebar.markdown(
        """

## How To Create a Graph:

- Enter wikipedia search terms, separated by comma's

- Choose one or more of the suggested topics (max 3)

- Click generate!

"""
    )
    cols = st.columns([8, 1])
    with cols[0]:
        st.text_input("wikipedia search term", on_change=wiki_show_suggestion,
                      key="text", value="graphs, are, awesome")
    with cols[1]:
        st.text('')
        st.text('')
        st.button("Search", on_click=wiki_show_suggestion,
                  key="show_suggestion_key")

    if len(st.session_state['wiki_suggestions']) != 0:
        num_buttons = len(st.session_state['wiki_suggestions'])
        num_cols = num_buttons if 0 < num_buttons < BUTTON_COLUMS else BUTTON_COLUMS
        columns = st.columns([1] * num_cols)
        for q in range(1 + num_buttons//num_cols):
            for i, (c, s) in enumerate(zip(columns, st.session_state['wiki_suggestions'][q*num_cols: (q+1)*num_cols])):
                with c:
                    st.button(s, on_click=wiki_show_text, args=(
                        s,), key=str(i)+s+"wiki_suggestion")

    if len(st.session_state['wiki_text']) != 0:
        for i, t in enumerate(st.session_state['wiki_text']):
            new_expander = st.expander(label=t[:30] + "...", expanded=(i == 0))
            with new_expander:
                st.markdown(t)

    if len(st.session_state['wiki_text']) > 0:
        st.button("Generate", on_click=wiki_generate_graph, key="gen_graph")
    st.sidebar.markdown(
        """

    ## How to expand the graph

    - Click a button below the graph to expand that node

     (Only nodes that have wiki pages will be expanded)

    - Hit the Generate button again to expand your graph!

    """
    )

    if st.session_state['has_run_wiki']:

        components.html(st.session_state["html_wiki"], width=720, height=600)
        num_buttons = len(st.session_state["nodes"])
        num_cols = num_buttons if 0 < num_buttons < BUTTON_COLUMS else BUTTON_COLUMS
        columns = st.columns([1] * num_cols + [1])

        for q in range(1 + num_buttons//num_cols):
            for i, (c, s) in enumerate(zip(columns, st.session_state["nodes"][q*num_cols: (q+1)*num_cols])):
                with c:
                    st.button(s, on_click=wiki_add_text,
                              args=(s,), key=str(i)+s)


def show_free_text_hub_page():
    st.sidebar.button("Reset", on_click=free_reset_session,
                      key="free_reset_key")
    st.sidebar.markdown(
        """

## How To Create a Graph:

- Enter a text you'd like to see as a graph.

- Click generate!

"""
    )

    free_text_layout()

    if st.session_state['has_run_free']:
        components.html(st.session_state["html_free"], width=720, height=600)


if st.session_state['input_method'] == "wikipedia":
    wiki_init_state_variables()
    show_wiki_hub_page()
else:
    free_test_init_state_variables()
    show_free_text_hub_page()


st.sidebar.markdown(
    """

## What This Is And Why We Built it



This space shows how a transformer network can be used to convert *human* text into a computer-queryable format: a **knowledge graph**. Knowledge graphs are graphs where each node (or *vertex* if you're fancy) represent a concept/person/thing and each edge the link between those concepts. If you'd like to know more, you can read [this blogpost](https://www.ml6.eu/knowhow/knowledge-graphs-an-introduction-and-business-applications).



Knowledge graphs aren't just cool to look at, they are an extremely versatile way of storing data, and are used in machine learning to perform tasks like fraud detection. You can read more about the applications of knowledge graphs in ML in [this blogpost](https://blog.ml6.eu/how-are-knowledge-graphs-and-machine-learning-related-ff6f5c1760b5).



There is one problem though: building knowledge graphs from scratch is a time-consuming and tedious task, so it would be a lot easier if we could leverage machine learning to **create** them from existing texts. This demo shows how a model named **REBEL** has been trained to do just that: it reads summaries from Wikipedia (or any other text you input), and generates a graph containing the information it distills from the text.

"""
)

st.sidebar.markdown(
    """

*Credits for the REBEL model go out to Pere-Lluís Huguet Cabot and Roberto Navigli.

The code can be found [here](https://github.com/Babelscape/rebel),

and the original paper [here](https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf)*

"""
)