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app.py

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+from heapq import nlargest
+import spacy
+from spacy.lang.en.stop_words import STOP_WORDS
+from string import punctuation
+import gradio as gr
+
+
+
+# Stopwords
+stopwords = list(STOP_WORDS)
+nlp = spacy.load('en_core_web_sm')
+punctuation = punctuation + '\n'
+import spacy
+from spacy.lang.en.stop_words import STOP_WORDS
+from string import punctuation
+
+# Prediction 
+def prediction(text):
+  doc = nlp(text)
+  len1 = len(text)
+  tokens = [token.text for token in doc]
+  word_frequencies = {}
+  for word in doc:
+    if word.text.lower() not in stopwords:
+      if word.text.lower() not in punctuation:
+        if word.text not in word_frequencies.keys():
+          word_frequencies[word.text] = 1
+        else:
+          word_frequencies[word.text] += 1
+  max_frequency = max(word_frequencies.values())
+  for word in word_frequencies.keys():
+    word_frequencies[word] = word_frequencies[word]/max_frequency
+  sentence_tokens = [sent for sent in doc.sents]
+  sentence_scores = {}
+  for sent in sentence_tokens:
+    for word in sent:
+      if word.text.lower() in word_frequencies.keys():
+        if sent not in sentence_scores.keys():
+          sentence_scores[sent] = word_frequencies[word.text.lower()]
+        else:
+          sentence_scores[sent] += word_frequencies[word.text.lower()]
+  select_length = int(len(sentence_tokens)*0.3)
+  summary = nlargest(select_length, sentence_scores, key = sentence_scores.get)
+  org_len = len(text.split(' '))
+  summary = (str(summary[0]))
+  sum_len = len(summary.split(' '))
+  return summary,org_len,sum_len
+
+
+
+
+EXAMPLES = [["""
+ Maria Sharapova has basically no friends as tennis players on the WTA Tour. The Russian player has no problems in openly speaking about it and in a recent interview she said: 'I don't really hide any feelings too much. 
+ I think everyone knows this is my job here. When I'm on the courts or when I'm on the court playing, I'm a competitor and I want to beat every single person whether they're in the locker room or across the net.
+ So I'm not the one to strike up a conversation about the weather and know that in the next few minutes I have to go and try to win a tennis match. 
+ I'm a pretty competitive girl. I say my hellos, but I'm not sending any players flowers as well. Uhm, I'm not really friendly or close to many players.
+ I have not a lot of friends away from the courts.' When she said she is not really close to a lot of players, is that something strategic that she is doing? Is it different on the men's tour than the women's tour? 'No, not at all.
+ I think just because you're in the same sport doesn't mean that you have to be friends with everyone just because you're categorized, you're a tennis player, so you're going to get along with tennis players. 
+ I think every person has different interests. I have friends that have completely different jobs and interests, and I've met them in very different parts of my life.
+ I think everyone just thinks because we're tennis players we should be the greatest of friends. But ultimately tennis is just a very small part of what we do. 
+ There are so many other things that we're interested in, that we do.'
+ """],["""The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, 
+ and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. 
+ During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, 
+ a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. 
+ It was the first structure to reach a height of 300 metres. 
+ Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft).
+ Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct."""]]
+
+DESCRIPTION = """We are bombarded with lakhs of characters of text and information and not so much time.
+Text summarization reads the whole documents, based on frequency of words and sentences it understands the 
+important sentences and gives us the summary of text.
+We have used a pre-trained model which is a small English pipeline trained on written web text like news, comments for this demo.
+This can be used in organizations that deal with lots of text documents like a law firm where the documents will be summarized in
+one to two paragraph as per our needs."""
+outputs = [
+    gr.Textbox(lines =5,label = "Summarization of text"),
+    gr.Number(label="Word Count of given Text"),
+    gr.Number(label="Word Count of Summarized Text")
+]
+
+demo_app = gr.Interface(
+    fn=prediction, 
+    inputs=gr.Textbox(lines =10,label = " Enter the Text", max_lines = 20),
+    outputs= outputs,
+    title = "Text Summarization",
+    examples = EXAMPLES,
+    description = DESCRIPTION,
+    #cache_example = True,
+    #live = True,
+    theme = 'huggingface'
+)
+#if __name__ == "__main__":
+demo_app.launch() 
+#demo_app.launch(debug=True, enable_queue = True)

requirements.txt

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+en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.2.0/en_core_web_sm-3.2.0-py3-none-any.whl
+spacy 
+gradio