Devika Nair M
commited on
Add files via upload
Browse files- Briefly.ipynb +556 -0
- Briefly.py +119 -0
- Procfile +1 -0
- requirements.txt +6 -0
- setup.sh +13 -0
Briefly.ipynb
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1 |
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{
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2 |
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"cells": [
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3 |
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{
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4 |
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"cell_type": "markdown",
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5 |
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"metadata": {},
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6 |
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"source": [
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7 |
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"## Briefly\n",
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8 |
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"\n",
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9 |
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"\n",
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10 |
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"### __ Problem Statement __\n",
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11 |
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"- Obtain news from google news articles\n",
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12 |
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"- Sammarize the articles within 60 words\n",
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13 |
+
"- Obtain keywords from the articles\n",
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14 |
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"\n",
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15 |
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"\n",
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16 |
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"\n",
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17 |
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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24 |
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"##### Importing all the necessary libraries required to run the following code "
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25 |
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]
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26 |
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},
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27 |
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{
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28 |
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"cell_type": "code",
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29 |
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"execution_count": 1,
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30 |
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"metadata": {},
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31 |
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"outputs": [],
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32 |
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"source": [
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33 |
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"from gnewsclient import gnewsclient # for fetching google news\n",
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34 |
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"from newspaper import Article # to obtain text from news articles\n",
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35 |
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"from transformers import pipeline # to summarize text\n",
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36 |
+
"import spacy # for named entity recognition\n",
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37 |
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"import spacy.displacy as displacy # display keywords"
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38 |
+
]
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39 |
+
},
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40 |
+
{
|
41 |
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"cell_type": "markdown",
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42 |
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"metadata": {},
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43 |
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"source": [
|
44 |
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"##### Load sshleifer/distilbart-cnn-12-6 model"
|
45 |
+
]
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46 |
+
},
|
47 |
+
{
|
48 |
+
"cell_type": "code",
|
49 |
+
"execution_count": 2,
|
50 |
+
"metadata": {},
|
51 |
+
"outputs": [],
|
52 |
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"source": [
|
53 |
+
"def load_model(): \n",
|
54 |
+
" model = pipeline('summarization')\n",
|
55 |
+
" return model\n",
|
56 |
+
"data = gnewsclient.NewsClient(max_results=0)\n",
|
57 |
+
"nlp = spacy.load(\"en_core_web_lg\") "
|
58 |
+
]
|
59 |
+
},
|
60 |
+
{
|
61 |
+
"cell_type": "markdown",
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62 |
+
"metadata": {},
|
63 |
+
"source": [
|
64 |
+
"##### Obtain urls and it's content"
|
65 |
+
]
|
66 |
+
},
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67 |
+
{
|
68 |
+
"cell_type": "code",
|
69 |
+
"execution_count": 3,
|
70 |
+
"metadata": {},
|
71 |
+
"outputs": [],
|
72 |
+
"source": [
|
73 |
+
"def getNews(topic,location): \n",
|
74 |
+
" count=0\n",
|
75 |
+
" contents=[]\n",
|
76 |
+
" titles=[]\n",
|
77 |
+
" authors=[]\n",
|
78 |
+
" urls=[]\n",
|
79 |
+
" data = gnewsclient.NewsClient(language='english',location=location,topic=topic,max_results=10) \n",
|
80 |
+
" news = data.get_news() \n",
|
81 |
+
" for item in news:\n",
|
82 |
+
" url=item['link']\n",
|
83 |
+
" article = Article(url)\n",
|
84 |
+
" try:\n",
|
85 |
+
" article.download()\n",
|
86 |
+
" article.parse()\n",
|
87 |
+
" temp=item['title'][::-1]\n",
|
88 |
+
" index=temp.find(\"-\")\n",
|
89 |
+
" temp=temp[:index-1][::-1]\n",
|
90 |
+
" urls.append(url)\n",
|
91 |
+
" contents.append(article.text)\n",
|
92 |
+
" titles.append(item['title'][:-index-1]) \n",
|
93 |
+
" authors.append(temp)\n",
|
94 |
+
" count+=1\n",
|
95 |
+
" if(count==5):\n",
|
96 |
+
" break\n",
|
97 |
+
" except:\n",
|
98 |
+
" continue \n",
|
99 |
+
" return contents,titles,authors,urls "
|
100 |
+
]
|
101 |
+
},
|
102 |
+
{
|
103 |
+
"cell_type": "markdown",
|
104 |
+
"metadata": {},
|
105 |
+
"source": [
|
106 |
+
"##### Summarizes the content- minimum word limit 30 and maximum 60"
|
107 |
+
]
|
108 |
+
},
|
109 |
+
{
|
110 |
+
"cell_type": "code",
|
111 |
+
"execution_count": 4,
|
112 |
+
"metadata": {},
|
113 |
+
"outputs": [],
|
114 |
+
"source": [
|
115 |
+
"def getNewsSummary(contents,summarizer): \n",
|
116 |
+
" summaries=[] \n",
|
117 |
+
" for content in contents:\n",
|
118 |
+
" minimum=len(content.split())\n",
|
119 |
+
" summaries.append(summarizer(content,max_length=60,min_length=min(30,minimum),do_sample=False,truncation=True)[0]['summary_text']) \n",
|
120 |
+
" return summaries"
|
121 |
+
]
|
122 |
+
},
|
123 |
+
{
|
124 |
+
"cell_type": "markdown",
|
125 |
+
"metadata": {},
|
126 |
+
"source": [
|
127 |
+
"##### Named Entity Recognition"
|
128 |
+
]
|
129 |
+
},
|
130 |
+
{
|
131 |
+
"cell_type": "code",
|
132 |
+
"execution_count": 5,
|
133 |
+
"metadata": {},
|
134 |
+
"outputs": [],
|
135 |
+
"source": [
|
136 |
+
"# Obtain 4 keywords from content (person,organisation or geopolitical entity) \n",
|
137 |
+
"def generateKeyword(contents): \n",
|
138 |
+
" keywords=[]\n",
|
139 |
+
" words=[] \n",
|
140 |
+
" labels=[\"PERSON\",\"ORG\",\"GPE\"]\n",
|
141 |
+
" for content in contents:\n",
|
142 |
+
" doc=nlp(content)\n",
|
143 |
+
" keys=[]\n",
|
144 |
+
" limit=0\n",
|
145 |
+
" for ent in doc.ents:\n",
|
146 |
+
" key=ent.text.upper()\n",
|
147 |
+
" label=ent.label_\n",
|
148 |
+
" if(key not in words and key not in keywords and label in labels): \n",
|
149 |
+
" keys.append(key)\n",
|
150 |
+
" limit+=1\n",
|
151 |
+
" for element in key.split():\n",
|
152 |
+
" words.append(element)\n",
|
153 |
+
" if(limit==4):\n",
|
154 |
+
" keywords.append(keys)\n",
|
155 |
+
" break \n",
|
156 |
+
" return keywords\n",
|
157 |
+
" "
|
158 |
+
]
|
159 |
+
},
|
160 |
+
{
|
161 |
+
"cell_type": "markdown",
|
162 |
+
"metadata": {},
|
163 |
+
"source": [
|
164 |
+
"##### Displaying keywords "
|
165 |
+
]
|
166 |
+
},
|
167 |
+
{
|
168 |
+
"cell_type": "code",
|
169 |
+
"execution_count": 6,
|
170 |
+
"metadata": {},
|
171 |
+
"outputs": [],
|
172 |
+
"source": [
|
173 |
+
"def printKeywords(keywords):\n",
|
174 |
+
" for keyword in keywords:\n",
|
175 |
+
" print(keyword)"
|
176 |
+
]
|
177 |
+
},
|
178 |
+
{
|
179 |
+
"cell_type": "markdown",
|
180 |
+
"metadata": {},
|
181 |
+
"source": [
|
182 |
+
"##### Displaying the Summary with keywords in it highlighted"
|
183 |
+
]
|
184 |
+
},
|
185 |
+
{
|
186 |
+
"cell_type": "code",
|
187 |
+
"execution_count": 7,
|
188 |
+
"metadata": {},
|
189 |
+
"outputs": [],
|
190 |
+
"source": [
|
191 |
+
"def printSummary(summaries,titles):\n",
|
192 |
+
" colors = {\"ORG\": \"linear-gradient(90deg, #aa9cfc, #fc9ce7)\"}\n",
|
193 |
+
" options = {\"ents\": [\"PERSON\",\"ORG\",\"GPE\",\"NORP\",\"PERCENT\"],\"colors\": colors} \n",
|
194 |
+
" for summary,title in zip(summaries,titles):\n",
|
195 |
+
" doc=nlp(summary) \n",
|
196 |
+
" print('\\033[1m' + title.upper() + '\\033[0m\\n')\n",
|
197 |
+
" displacy.render(doc, style=\"ent\", options=options,jupyter=True)\n",
|
198 |
+
" print(\"\\n\\n\")"
|
199 |
+
]
|
200 |
+
},
|
201 |
+
{
|
202 |
+
"cell_type": "code",
|
203 |
+
"execution_count": 8,
|
204 |
+
"metadata": {},
|
205 |
+
"outputs": [
|
206 |
+
{
|
207 |
+
"name": "stderr",
|
208 |
+
"output_type": "stream",
|
209 |
+
"text": [
|
210 |
+
"No model was supplied, defaulted to sshleifer/distilbart-cnn-12-6 (https://huggingface.co/sshleifer/distilbart-cnn-12-6)\n"
|
211 |
+
]
|
212 |
+
}
|
213 |
+
],
|
214 |
+
"source": [
|
215 |
+
"summarizer=load_model() "
|
216 |
+
]
|
217 |
+
},
|
218 |
+
{
|
219 |
+
"cell_type": "code",
|
220 |
+
"execution_count": 9,
|
221 |
+
"metadata": {},
|
222 |
+
"outputs": [],
|
223 |
+
"source": [
|
224 |
+
"contents,titles,authors,urls=getNews(\"Sports\",\"India\")"
|
225 |
+
]
|
226 |
+
},
|
227 |
+
{
|
228 |
+
"cell_type": "code",
|
229 |
+
"execution_count": 10,
|
230 |
+
"metadata": {},
|
231 |
+
"outputs": [],
|
232 |
+
"source": [
|
233 |
+
"summaries=getNewsSummary(contents,summarizer)"
|
234 |
+
]
|
235 |
+
},
|
236 |
+
{
|
237 |
+
"cell_type": "code",
|
238 |
+
"execution_count": 11,
|
239 |
+
"metadata": {},
|
240 |
+
"outputs": [],
|
241 |
+
"source": [
|
242 |
+
"keywords=generateKeyword(contents)"
|
243 |
+
]
|
244 |
+
},
|
245 |
+
{
|
246 |
+
"cell_type": "code",
|
247 |
+
"execution_count": 12,
|
248 |
+
"metadata": {},
|
249 |
+
"outputs": [
|
250 |
+
{
|
251 |
+
"name": "stdout",
|
252 |
+
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536 |
+
"kernelspec": {
|
537 |
+
"display_name": "Python 3",
|
538 |
+
"language": "python",
|
539 |
+
"name": "python3"
|
540 |
+
},
|
541 |
+
"language_info": {
|
542 |
+
"codemirror_mode": {
|
543 |
+
"name": "ipython",
|
544 |
+
"version": 3
|
545 |
+
},
|
546 |
+
"file_extension": ".py",
|
547 |
+
"mimetype": "text/x-python",
|
548 |
+
"name": "python",
|
549 |
+
"nbconvert_exporter": "python",
|
550 |
+
"pygments_lexer": "ipython3",
|
551 |
+
"version": "3.8.5"
|
552 |
+
}
|
553 |
+
},
|
554 |
+
"nbformat": 4,
|
555 |
+
"nbformat_minor": 4
|
556 |
+
}
|
Briefly.py
ADDED
@@ -0,0 +1,119 @@
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|
|
1 |
+
import streamlit as st #Web App
|
2 |
+
from gnewsclient import gnewsclient # for fetching google news
|
3 |
+
from newspaper import Article # to obtain text from news articles
|
4 |
+
from transformers import pipeline # to summarize text
|
5 |
+
import spacy # to obtain keyword
|
6 |
+
from annotated_text import annotated_text # to display keywords
|
7 |
+
|
8 |
+
|
9 |
+
# Load sshleifer/distilbart-cnn-12-6 model
|
10 |
+
@st.cache(allow_output_mutation=True)
|
11 |
+
def load_model():
|
12 |
+
model = pipeline('summarization')
|
13 |
+
return model
|
14 |
+
|
15 |
+
data = gnewsclient.NewsClient(max_results=0)
|
16 |
+
|
17 |
+
|
18 |
+
# obtain urls and it's content
|
19 |
+
def getNews(topic,location):
|
20 |
+
count=0
|
21 |
+
contents=[]
|
22 |
+
titles=[]
|
23 |
+
authors=[]
|
24 |
+
urls=[]
|
25 |
+
data = gnewsclient.NewsClient(language='english',location=location,topic=topic,max_results=10)
|
26 |
+
news = data.get_news()
|
27 |
+
for item in news:
|
28 |
+
url=item['link']
|
29 |
+
article = Article(url)
|
30 |
+
try:
|
31 |
+
article.download()
|
32 |
+
article.parse()
|
33 |
+
temp=item['title'][::-1]
|
34 |
+
index=temp.find("-")
|
35 |
+
temp=temp[:index-1][::-1]
|
36 |
+
urls.append(url)
|
37 |
+
contents.append(article.text)
|
38 |
+
titles.append(item['title'][:-index-1])
|
39 |
+
authors.append(temp)
|
40 |
+
count+=1
|
41 |
+
if(count==5):
|
42 |
+
break
|
43 |
+
except:
|
44 |
+
continue
|
45 |
+
return contents,titles,authors,urls
|
46 |
+
|
47 |
+
|
48 |
+
# Summarizes the content- minimum word limit 30 and maximum 60
|
49 |
+
def getNewsSummary(contents,summarizer):
|
50 |
+
summaries=[]
|
51 |
+
for content in contents:
|
52 |
+
minimum=len(content.split())
|
53 |
+
summaries.append(summarizer(content,max_length=60,min_length=min(30,minimum),do_sample=False,truncation=True)[0]['summary_text'])
|
54 |
+
return summaries
|
55 |
+
|
56 |
+
|
57 |
+
# Obtain 4 keywords from content (person,organisation or geopolitical entity)
|
58 |
+
def generateKeyword(contents):
|
59 |
+
keywords=[]
|
60 |
+
words=[]
|
61 |
+
nlp = spacy.load("en_core_web_lg")
|
62 |
+
labels=["PERSON","ORG","GPE"]
|
63 |
+
for content in contents:
|
64 |
+
doc=nlp(content)
|
65 |
+
keys=[]
|
66 |
+
limit=0
|
67 |
+
for ent in doc.ents:
|
68 |
+
key=ent.text.upper()
|
69 |
+
label=ent.label_
|
70 |
+
if(key not in words and key not in keywords and label in labels):
|
71 |
+
keys.append(key)
|
72 |
+
limit+=1
|
73 |
+
for element in key.split():
|
74 |
+
words.append(element)
|
75 |
+
if(limit==4):
|
76 |
+
keywords.append(keys)
|
77 |
+
break
|
78 |
+
return keywords
|
79 |
+
|
80 |
+
|
81 |
+
# Display title,author and summary in streamlit
|
82 |
+
def DisplaySummary(titles,authors,summaries,keywords,urls):
|
83 |
+
for i in range(5):
|
84 |
+
if(i+1<=len(summaries) and i+1<=len(keywords)):
|
85 |
+
st.text("")
|
86 |
+
st.subheader(f'[{titles[i]}] ({urls[i]})')
|
87 |
+
st.markdown(f'<b>{authors[i]}</b>',unsafe_allow_html=True)
|
88 |
+
st.write(summaries[i])
|
89 |
+
if(len(keywords[i])==4):
|
90 |
+
annotated_text("KEYWORDS :",(keywords[i][0],"","#faa")," ",(keywords[i][1],"","#faa")," ",(keywords[i][2],"","#faa")," ",(keywords[i][3],"","#faa"))
|
91 |
+
elif(len(keywords[i])==3):
|
92 |
+
annotated_text("KEYWORDS :",(keywords[i][0],"","#faa")," ",(keywords[i][1],"","#faa")," ",(keywords[i][2],"","#faa"))
|
93 |
+
elif(len(keywords[i])==2):
|
94 |
+
annotated_text("KEYWORDS :",(keywords[i][0],"","#faa")," ",(keywords[i][1],"","#faa"))
|
95 |
+
elif(len(keywords[i])==1):
|
96 |
+
annotated_text("KEYWORDS :",(keywords[i][0],"","#faa"))
|
97 |
+
st.text("")
|
98 |
+
st.text("")
|
99 |
+
|
100 |
+
|
101 |
+
def main():
|
102 |
+
summarizer=load_model()
|
103 |
+
st.title('Briefly')
|
104 |
+
with st.expander('Read trending news in less than 60 words...', expanded=True):
|
105 |
+
with st.form(key='form1'):
|
106 |
+
topic=st.selectbox('Category:',data.topics[2:]+["World"])
|
107 |
+
location=st.selectbox('Location:',data.locations)
|
108 |
+
submit_button=st.form_submit_button()
|
109 |
+
|
110 |
+
if submit_button:
|
111 |
+
with st.spinner('Fetching news...'):
|
112 |
+
contents,titles,authors,urls=getNews(topic,location)
|
113 |
+
summaries=getNewsSummary(contents,summarizer)
|
114 |
+
keywords=generateKeyword(contents)
|
115 |
+
DisplaySummary(titles,authors,summaries,keywords,urls)
|
116 |
+
|
117 |
+
|
118 |
+
if __name__ == '__main__':
|
119 |
+
main()
|
Procfile
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
web: sh setup.sh && streamlit run Briefly.py
|
requirements.txt
ADDED
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
streamlit
|
2 |
+
gnewsclient
|
3 |
+
transformers
|
4 |
+
newspaper3k
|
5 |
+
spacy
|
6 |
+
annotated_text
|
setup.sh
ADDED
@@ -0,0 +1,13 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
mkdir -p ~/.streamlit/
|
2 |
+
|
3 |
+
echo "\
|
4 |
+
[general]\n\
|
5 |
+
email = \"[email protected]\"\n\
|
6 |
+
" > ~/.streamlit/credentials.toml
|
7 |
+
|
8 |
+
echo "\
|
9 |
+
[server]\n\
|
10 |
+
headless = true\n\
|
11 |
+
enableCORS=false\n\
|
12 |
+
port = $PORT\n\
|
13 |
+
" > ~/.streamlit/config.toml
|