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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) | |
| 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) | |
| return summary | |
| #text = """ | |
| # 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.' | |
| #""" | |
| #predicted_label, score = occ_predict("img1.jpg") | |
| #inputs = gr.inputs.text(label) | |
| #label = gr.outputs.Label(num_top_classes=2) | |
| #EXAMPLES = ["img1.png","img2.png","img3.png","img10.png","img8.png","img9.png"] | |
| #DESCRIPTION = "Occlusion means the act of closing, blocking or shutting something or the state of being closed or blocked" | |
| #summary = prediction(text) | |
| #print(summary) | |
| demo_app = gr.Interface( | |
| fn=prediction, | |
| inputs=gr.input.text(label = " Text", max_lines = 20), | |
| outputs= "text", | |
| title = "Text Summarization", | |
| #description = DESCRIPTION, | |
| examples = EXAMPLES, | |
| cache_example = True, | |
| live = True, | |
| theme = 'huggingface' | |
| ) | |
| demo_app.launch(debug=True, enable_queue = True) | |