Added Application Code

README.md

@@ -10,28 +10,28 @@ pinned: false
 
 # Configuration
 
-`title`: _string_  
+`title`: `Wikisummarizer`
 Display title for the Space
 
 `emoji`: _string_  
 Space emoji (emoji-only character allowed)
 
-`colorFrom`: _string_  
+`colorFrom`: `indigo`
 Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
 
-`colorTo`: _string_  
+`colorTo`: `pink`
 Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
 
-`sdk`: _string_  
+`sdk`: `streamlit`
 Can be either `gradio` or `streamlit`
 
 `sdk_version` : _string_  
 Only applicable for `streamlit` SDK.  
 See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
 
-`app_file`: _string_  
+`app_file`: `app.py`
 Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
 Path is relative to the root of the repository.
 
-`pinned`: _boolean_  
+`pinned`: `True`
 Whether the Space stays on top of your list.

app.py

@@ -0,0 +1,64 @@
+import streamlit as st
+import bs4 as bs  
+import urllib.request  
+import re
+
+def main():
+    st.title("Wikipedia Summarizer")
+    url_topull= st.text_input("Enter the Wikipedia URL to pull - ")
+    if url_topull!='':
+        scraped_data = urllib.request.urlopen(url_topull)  
+        article = scraped_data.read()
+
+        parsed_article=bs.BeautifulSoup(article,'lxml')
+
+        paragraphs = parsed_article.find_all('p')
+
+        article_text = ""
+
+        for p in paragraphs:  
+            article_text += p.text
+        article_text = re.sub(r'\[[0-9]*\]', ' ', article_text)  
+        article_text = re.sub(r'\s+', ' ', article_text)
+
+        import nltk
+        nltk.download('punkt')
+        nltk.download('stopwords')
+        import heapq
+        number=st.text_input('How many sentences long do you want your summary to be?')
+        if number!='':    
+            sent_num = int(number)
+            formatted_article_text = re.sub('[^a-zA-Z]', ' ', article_text )  
+            formatted_article_text = re.sub(r'\s+', ' ', formatted_article_text)
+            sentence_list = nltk.sent_tokenize(article_text)  
+
+            stopwords = nltk.corpus.stopwords.words('english')
+            word_frequencies = {}  
+            for word in nltk.word_tokenize(formatted_article_text):  
+                if word not in stopwords:
+                    if word not in word_frequencies.keys():
+                        word_frequencies[word] = 1
+                    else:
+                        word_frequencies[word] += 1
+
+            maximum_frequncy = max(word_frequencies.values())
+
+            for word in word_frequencies.keys():  
+                word_frequencies[word] = (word_frequencies[word]/maximum_frequncy)
+            sentence_scores = {}  
+            for sent in sentence_list:  
+                for word in nltk.word_tokenize(sent.lower()):
+                    if word in word_frequencies.keys():
+                        if len(sent.split(' ')) < 30:
+                            if sent not in sentence_scores.keys():
+                                sentence_scores[sent] = word_frequencies[word]
+                            else:
+                                sentence_scores[sent] += word_frequencies[word]
+
+            summary_sentences = heapq.nlargest(sent_num, sentence_scores, key=sentence_scores.get)
+            summary = ' '.join(summary_sentences)  
+            st.markdown("# Summary: ")
+            st.write(summary)   
+    
+if __name__ == '__main__':
+    main()

requirements.txt

@@ -0,0 +1,4 @@
+gensim
+bs4
+lxml
+nltk