├── .streamlit
└── config.toml
├── LICENSE
├── README.md
├── app.py
├── functionforDownloadButtons.py
├── logo.png
└── requirements.txt
/.streamlit/config.toml:
--------------------------------------------------------------------------------
1 | [theme]
2 | primaryColor = "#00B4B3"
3 | backgroundColor = "#FFFFFF"
4 | secondaryBackgroundColor = "#F0F2F6"
5 | textColor = "#262730"
6 | font = "sans serif"
7 |
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
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/README.md:
--------------------------------------------------------------------------------
1 | [](https://share.streamlit.io/streamlit/example-app-bert-keyword-extractor/main/app.py)
2 |
3 |
4 | # Bert Keyword Extractor
5 |
6 | - The BERT Keyword Extractor app is an easy-to-use interface built in
7 | Streamlit for the amazing KeyBERT library from Maarten Grootendorst!
8 | - It uses a minimal keyword extraction technique that leverages
9 | multiple NLP embeddings and relies on Transformers 🤗 to create
10 | keywords/keyphrases that are most similar to a document.
11 |
12 | 
13 |
--------------------------------------------------------------------------------
/app.py:
--------------------------------------------------------------------------------
1 | import streamlit as st
2 | import numpy as np
3 | from pandas import DataFrame
4 | from keybert import KeyBERT
5 | # For Flair (Keybert)
6 | from flair.embeddings import TransformerDocumentEmbeddings
7 | import seaborn as sns
8 | # For download buttons
9 | from functionforDownloadButtons import download_button
10 | import os
11 | import json
12 |
13 | st.set_page_config(
14 | page_title="BERT Keyword Extractor",
15 | page_icon="🎈",
16 | )
17 |
18 |
19 | def _max_width_():
20 | max_width_str = f"max-width: 1400px;"
21 | st.markdown(
22 | f"""
23 |
28 | """,
29 | unsafe_allow_html=True,
30 | )
31 |
32 |
33 | _max_width_()
34 |
35 | c30, c31, c32 = st.columns([2.5, 1, 3])
36 |
37 | with c30:
38 | # st.image("logo.png", width=400)
39 | st.title("🔑 BERT Keyword Extractor")
40 | st.header("")
41 |
42 |
43 |
44 | with st.expander("ℹ️ - About this app", expanded=True):
45 |
46 | st.write(
47 | """
48 | - The *BERT Keyword Extractor* app is an easy-to-use interface built in Streamlit for the amazing [KeyBERT](https://github.com/MaartenGr/KeyBERT) library from Maarten Grootendorst!
49 | - It uses a minimal keyword extraction technique that leverages multiple NLP embeddings and relies on [Transformers] (https://huggingface.co/transformers/) 🤗 to create keywords/keyphrases that are most similar to a document.
50 | """
51 | )
52 |
53 | st.markdown("")
54 |
55 | st.markdown("")
56 | st.markdown("## **📌 Paste document **")
57 | with st.form(key="my_form"):
58 |
59 |
60 | ce, c1, ce, c2, c3 = st.columns([0.07, 1, 0.07, 5, 0.07])
61 | with c1:
62 | ModelType = st.radio(
63 | "Choose your model",
64 | ["DistilBERT (Default)", "Flair"],
65 | help="At present, you can choose between 2 models (Flair or DistilBERT) to embed your text. More to come!",
66 | )
67 |
68 | if ModelType == "Default (DistilBERT)":
69 | # kw_model = KeyBERT(model=roberta)
70 |
71 | @st.cache(allow_output_mutation=True)
72 | def load_model():
73 | return KeyBERT(model=roberta)
74 |
75 | kw_model = load_model()
76 |
77 | else:
78 | @st.cache(allow_output_mutation=True)
79 | def load_model():
80 | return KeyBERT("distilbert-base-nli-mean-tokens")
81 |
82 | kw_model = load_model()
83 |
84 | top_N = st.slider(
85 | "# of results",
86 | min_value=1,
87 | max_value=30,
88 | value=10,
89 | help="You can choose the number of keywords/keyphrases to display. Between 1 and 30, default number is 10.",
90 | )
91 | min_Ngrams = st.number_input(
92 | "Minimum Ngram",
93 | min_value=1,
94 | max_value=4,
95 | help="""The minimum value for the ngram range.
96 |
97 | *Keyphrase_ngram_range* sets the length of the resulting keywords/keyphrases.
98 |
99 | To extract keyphrases, simply set *keyphrase_ngram_range* to (1, 2) or higher depending on the number of words you would like in the resulting keyphrases.""",
100 | # help="Minimum value for the keyphrase_ngram_range. keyphrase_ngram_range sets the length of the resulting keywords/keyphrases. To extract keyphrases, simply set keyphrase_ngram_range to (1, # 2) or higher depending on the number of words you would like in the resulting keyphrases.",
101 | )
102 |
103 | max_Ngrams = st.number_input(
104 | "Maximum Ngram",
105 | value=2,
106 | min_value=1,
107 | max_value=4,
108 | help="""The maximum value for the keyphrase_ngram_range.
109 |
110 | *Keyphrase_ngram_range* sets the length of the resulting keywords/keyphrases.
111 |
112 | To extract keyphrases, simply set *keyphrase_ngram_range* to (1, 2) or higher depending on the number of words you would like in the resulting keyphrases.""",
113 | )
114 |
115 | StopWordsCheckbox = st.checkbox(
116 | "Remove stop words",
117 | help="Tick this box to remove stop words from the document (currently English only)",
118 | )
119 |
120 | use_MMR = st.checkbox(
121 | "Use MMR",
122 | value=True,
123 | help="You can use Maximal Margin Relevance (MMR) to diversify the results. It creates keywords/keyphrases based on cosine similarity. Try high/low 'Diversity' settings below for interesting variations.",
124 | )
125 |
126 | Diversity = st.slider(
127 | "Keyword diversity (MMR only)",
128 | value=0.5,
129 | min_value=0.0,
130 | max_value=1.0,
131 | step=0.1,
132 | help="""The higher the setting, the more diverse the keywords.
133 |
134 | Note that the *Keyword diversity* slider only works if the *MMR* checkbox is ticked.
135 |
136 | """,
137 | )
138 |
139 | with c2:
140 | doc = st.text_area(
141 | "Paste your text below (max 500 words)",
142 | height=510,
143 | )
144 |
145 | MAX_WORDS = 500
146 | import re
147 | res = len(re.findall(r"\w+", doc))
148 | if res > MAX_WORDS:
149 | st.warning(
150 | "⚠️ Your text contains "
151 | + str(res)
152 | + " words."
153 | + " Only the first 500 words will be reviewed. Stay tuned as increased allowance is coming! 😊"
154 | )
155 |
156 | doc = doc[:MAX_WORDS]
157 |
158 | submit_button = st.form_submit_button(label="✨ Get me the data!")
159 |
160 | if use_MMR:
161 | mmr = True
162 | else:
163 | mmr = False
164 |
165 | if StopWordsCheckbox:
166 | StopWords = "english"
167 | else:
168 | StopWords = None
169 |
170 | if not submit_button:
171 | st.stop()
172 |
173 | if min_Ngrams > max_Ngrams:
174 | st.warning("min_Ngrams can't be greater than max_Ngrams")
175 | st.stop()
176 |
177 | keywords = kw_model.extract_keywords(
178 | doc,
179 | keyphrase_ngram_range=(min_Ngrams, max_Ngrams),
180 | use_mmr=mmr,
181 | stop_words=StopWords,
182 | top_n=top_N,
183 | diversity=Diversity,
184 | )
185 |
186 | st.markdown("## **🎈 Check & download results **")
187 |
188 | st.header("")
189 |
190 | cs, c1, c2, c3, cLast = st.columns([2, 1.5, 1.5, 1.5, 2])
191 |
192 | with c1:
193 | CSVButton2 = download_button(keywords, "Data.csv", "📥 Download (.csv)")
194 | with c2:
195 | CSVButton2 = download_button(keywords, "Data.txt", "📥 Download (.txt)")
196 | with c3:
197 | CSVButton2 = download_button(keywords, "Data.json", "📥 Download (.json)")
198 |
199 | st.header("")
200 |
201 | df = (
202 | DataFrame(keywords, columns=["Keyword/Keyphrase", "Relevancy"])
203 | .sort_values(by="Relevancy", ascending=False)
204 | .reset_index(drop=True)
205 | )
206 |
207 | df.index += 1
208 |
209 | # Add styling
210 | cmGreen = sns.light_palette("green", as_cmap=True)
211 | cmRed = sns.light_palette("red", as_cmap=True)
212 | df = df.style.background_gradient(
213 | cmap=cmGreen,
214 | subset=[
215 | "Relevancy",
216 | ],
217 | )
218 |
219 | c1, c2, c3 = st.columns([1, 3, 1])
220 |
221 | format_dictionary = {
222 | "Relevancy": "{:.1%}",
223 | }
224 |
225 | df = df.format(format_dictionary)
226 |
227 | with c2:
228 | st.table(df)
229 |
--------------------------------------------------------------------------------
/functionforDownloadButtons.py:
--------------------------------------------------------------------------------
1 | import streamlit as st
2 | import pickle
3 | import pandas as pd
4 | import json
5 | import base64
6 | import uuid
7 | import re
8 |
9 | import importlib.util
10 |
11 |
12 | def import_from_file(module_name: str, filepath: str):
13 | """
14 | Imports a module from file.
15 |
16 | Args:
17 | module_name (str): Assigned to the module's __name__ parameter (does not
18 | influence how the module is named outside of this function)
19 | filepath (str): Path to the .py file
20 |
21 | Returns:
22 | The module
23 | """
24 | spec = importlib.util.spec_from_file_location(module_name, filepath)
25 | module = importlib.util.module_from_spec(spec)
26 | spec.loader.exec_module(module)
27 | return module
28 |
29 |
30 | def notebook_header(text):
31 | """
32 | Insert section header into a jinja file, formatted as notebook cell.
33 |
34 | Leave 2 blank lines before the header.
35 | """
36 | return f"""# # {text}
37 |
38 | """
39 |
40 |
41 | def code_header(text):
42 | """
43 | Insert section header into a jinja file, formatted as Python comment.
44 |
45 | Leave 2 blank lines before the header.
46 | """
47 | seperator_len = (75 - len(text)) / 2
48 | seperator_len_left = math.floor(seperator_len)
49 | seperator_len_right = math.ceil(seperator_len)
50 | return f"# {'-' * seperator_len_left} {text} {'-' * seperator_len_right}"
51 |
52 |
53 | def to_notebook(code):
54 | """Converts Python code to Jupyter notebook format."""
55 | notebook = jupytext.reads(code, fmt="py")
56 | return jupytext.writes(notebook, fmt="ipynb")
57 |
58 |
59 | def open_link(url, new_tab=True):
60 | """Dirty hack to open a new web page with a streamlit button."""
61 | # From: https://discuss.streamlit.io/t/how-to-link-a-button-to-a-webpage/1661/3
62 | if new_tab:
63 | js = f"window.open('{url}')" # New tab or window
64 | else:
65 | js = f"window.location.href = '{url}'" # Current tab
66 | html = ''.format(js)
67 | div = Div(text=html)
68 | st.bokeh_chart(div)
69 |
70 |
71 | def download_button(object_to_download, download_filename, button_text):
72 | """
73 | Generates a link to download the given object_to_download.
74 |
75 | From: https://discuss.streamlit.io/t/a-download-button-with-custom-css/4220
76 |
77 | Params:
78 | ------
79 | object_to_download: The object to be downloaded.
80 | download_filename (str): filename and extension of file. e.g. mydata.csv,
81 | some_txt_output.txt download_link_text (str): Text to display for download
82 | link.
83 |
84 | button_text (str): Text to display on download button (e.g. 'click here to download file')
85 | pickle_it (bool): If True, pickle file.
86 |
87 | Returns:
88 | -------
89 | (str): the anchor tag to download object_to_download
90 |
91 | Examples:
92 | --------
93 | download_link(your_df, 'YOUR_DF.csv', 'Click to download data!')
94 | download_link(your_str, 'YOUR_STRING.txt', 'Click to download text!')
95 |
96 | """
97 | # if pickle_it:
98 | # try:
99 | # object_to_download = pickle.dumps(object_to_download)
100 | # except pickle.PicklingError as e:
101 | # st.write(e)
102 | # return None
103 |
104 | # if:
105 | if isinstance(object_to_download, bytes):
106 | pass
107 |
108 | elif isinstance(object_to_download, pd.DataFrame):
109 | object_to_download = object_to_download.to_csv(index=False)
110 | # Try JSON encode for everything else
111 | else:
112 | object_to_download = json.dumps(object_to_download)
113 |
114 | try:
115 | # some strings <-> bytes conversions necessary here
116 | b64 = base64.b64encode(object_to_download.encode()).decode()
117 | except AttributeError as e:
118 | b64 = base64.b64encode(object_to_download).decode()
119 |
120 | button_uuid = str(uuid.uuid4()).replace("-", "")
121 | button_id = re.sub("\d+", "", button_uuid)
122 |
123 | custom_css = f"""
124 | """
150 |
151 | dl_link = (
152 | custom_css
153 | + f'{button_text}