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import nltk |
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import re |
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import nltkmodule |
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from newspaper import Article |
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from newspaper import fulltext |
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import requests |
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from nltk.tokenize import word_tokenize |
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from sentence_transformers import SentenceTransformer, models, losses, LoggingHandler |
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import pandas as pd |
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import numpy as np |
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from torch.utils.data import DataLoader |
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import math |
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from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator |
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from sentence_transformers.readers import * |
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from nltk.corpus import stopwords |
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stop_words = stopwords.words('english') |
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from sklearn.metrics.pairwise import cosine_similarity |
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import networkx as nx |
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from nltk.tokenize import sent_tokenize |
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import scispacy |
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import en_core_sci_lg |
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import string |
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import gradio as gr |
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import inflect |
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inflect_op = inflect.engine() |
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nlp = en_core_sci_lg.load() |
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sp = en_core_sci_lg.load() |
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all_stopwords = sp.Defaults.stop_words |
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def remove_stopwords(sen): |
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sen_new = " ".join([i for i in sen if i not in stop_words]) |
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return sen_new |
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def keyphrase_generator(article_link, model_1, model_2, max_num_keywords): |
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element=[] |
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final_textrank_list=[] |
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document=[] |
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text_doc=[] |
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score_list=[] |
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sum_list=[] |
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model_1 = SentenceTransformer(model_1) |
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model_2 = SentenceTransformer(model_2) |
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url = article_link |
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html = requests.get(url).text |
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article = fulltext(html) |
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corpus=sent_tokenize(article) |
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indicator_list=['concluded','concludes','in a study', 'concluding','conclude','in sum','in a recent study','therefore','thus','so','hence', |
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'as a result','accordingly','consequently','in short','proves that','shows that','suggests that','demonstrates that','found that','observed that', |
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'indicated that','suggested that','demonstrated that'] |
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count_dict={} |
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for l in corpus: |
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c=0 |
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for l2 in indicator_list: |
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if l.find(l2)!=-1: |
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c=1 |
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break |
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if c: |
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count_dict[l]=1 |
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else: |
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count_dict[l]=0 |
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for sent, score in count_dict.items(): |
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score_list.append(score) |
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clean_sentences_new = pd.Series(corpus).str.replace("[^a-zA-Z]", " ", regex=True).tolist() |
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corpus_embeddings = model_1.encode(clean_sentences_new) |
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sim_mat = np.zeros([len(clean_sentences_new), len(clean_sentences_new)]) |
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for i in range(len(clean_sentences_new)): |
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len_embeddings=(len(corpus_embeddings[i])) |
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for j in range(len(clean_sentences_new)): |
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if i != j: |
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if(len_embeddings == 1024): |
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sim_mat[i][j] = cosine_similarity(corpus_embeddings[i].reshape(1,1024), corpus_embeddings[j].reshape(1,1024))[0,0] |
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elif(len_embeddings == 768): |
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sim_mat[i][j] = cosine_similarity(corpus_embeddings[i].reshape(1,768), corpus_embeddings[j].reshape(1,768))[0,0] |
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nx_graph = nx.from_numpy_array(sim_mat) |
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scores = nx.pagerank(nx_graph) |
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sentences=((scores[i],s) for i,s in enumerate(corpus)) |
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for elem in sentences: |
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element.append(elem[0]) |
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for sc, lst in zip(score_list, element): |
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sum1=sc+lst |
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sum_list.append(sum1) |
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x=sorted(((sum_list[i],s) for i,s in enumerate(corpus)), reverse=True) |
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for elem in x: |
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final_textrank_list.append(elem[1]) |
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a=int((10*len(final_textrank_list))/100.0) |
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if(a<5): |
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total=5 |
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else: |
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total=int(a) |
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for i in range(total): |
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document.append(final_textrank_list[i]) |
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doc=" ".join(document) |
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for i in document: |
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doc_1=nlp(i) |
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text_doc.append([X.text for X in doc_1.ents]) |
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entity_list = [item for sublist in text_doc for item in sublist] |
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entity_list = [word for word in entity_list if not word in all_stopwords] |
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entity_list = [word_entity for word_entity in entity_list if(inflect_op.singular_noun(word_entity) == False)] |
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entity_list=list(dict.fromkeys(entity_list)) |
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doc_embedding = model_2.encode([doc]) |
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candidates=entity_list |
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candidate_embeddings = model_2.encode(candidates) |
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distances = cosine_similarity(doc_embedding, candidate_embeddings) |
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top_n = max_num_keywords |
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keyword_list = [candidates[index] for index in distances.argsort()[0][-top_n:]] |
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keywords = '\n'.join(keyword_list) |
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return keywords |
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igen=gr.Interface(keyphrase_generator, |
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inputs=[gr.components.Textbox(lines=1, placeholder="Provide an online health article web link here",value="", label="Article web link"), |
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gr.components.Dropdown(choices=['sentence-transformers/all-mpnet-base-v2', |
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'sentence-transformers/all-mpnet-base-v1', |
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'sentence-transformers/all-distilroberta-v1', |
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'sentence-transformers/gtr-t5-large', |
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'pritamdeka/S-Bluebert-snli-multinli-stsb', |
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'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb', |
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'sentence-transformers/stsb-mpnet-base-v2', |
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'sentence-transformers/all-roberta-large-v1', |
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'sentence-transformers/stsb-roberta-base-v2', |
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'sentence-transformers/stsb-distilroberta-base-v2', |
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'sentence-transformers/sentence-t5-large', |
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'sentence-transformers/sentence-t5-base'], |
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type="value", |
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value='pritamdeka/S-Biomed-Roberta-snli-multinli-stsb', |
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label="Select any SBERT model for TextRank from the list below"), |
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gr.components.Dropdown(choices=['sentence-transformers/paraphrase-mpnet-base-v2', |
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'sentence-transformers/all-mpnet-base-v1', |
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'sentence-transformers/paraphrase-distilroberta-base-v1', |
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'sentence-transformers/paraphrase-xlm-r-multilingual-v1', |
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'sentence-transformers/paraphrase-multilingual-mpnet-base-v2', |
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'sentence-transformers/paraphrase-albert-small-v2', |
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'sentence-transformers/paraphrase-albert-base-v2', |
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'sentence-transformers/paraphrase-MiniLM-L12-v2', |
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'sentence-transformers/paraphrase-MiniLM-L6-v2', |
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'sentence-transformers/all-MiniLM-L12-v2', |
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'sentence-transformers/all-distilroberta-v1', |
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'sentence-transformers/paraphrase-TinyBERT-L6-v2', |
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'sentence-transformers/paraphrase-MiniLM-L3-v2', |
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'sentence-transformers/all-MiniLM-L6-v2'], |
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type="value", |
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value='sentence-transformers/all-mpnet-base-v1', |
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label="Select any SBERT model for keyphrases from the list below"), |
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gr.components.Slider(minimum=5, maximum=30, step=1, value=10, label="Max Keywords")], |
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outputs=gr.components.Textbox(type="text", label="Output"), theme="peach", |
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title="Health Article Keyphrase Generator", |
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description="Generates the keyphrases from an online health article which best describes the article. Examples are provided below for demo purposes. Choose any one example to see the results. ", |
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examples=[ |
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["https://www.cancer.news/2021-12-22-mrna-vaccines-weaken-immune-system-cause-cancer.html", |
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'sentence-transformers/all-mpnet-base-v1', |
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'sentence-transformers/paraphrase-MiniLM-L12-v2', |
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10], |
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["https://www.cancer.news/2022-02-04-doctors-testifying-covid-vaccines-causing-cancer-aids.html#", |
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'sentence-transformers/all-mpnet-base-v1', |
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'sentence-transformers/all-mpnet-base-v1', |
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12], |
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["https://www.medicalnewstoday.com/articles/alzheimers-addressing-sleep-disturbance-may-alleviate-symptoms", |
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'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb', |
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'sentence-transformers/all-mpnet-base-v1', |
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10], |
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["https://www.medicalnewstoday.com/articles/omicron-what-do-we-know-about-the-stealth-variant", |
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'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb', |
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'sentence-transformers/all-mpnet-base-v1', |
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15] |
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], |
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article= "The work is based on a part of the paper provided <a href=https://dl.acm.org/doi/10.1145/3487664.3487701>here</a>." |
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"\t It uses the TextRank algorithm with <a href=https://www.sbert.net/>SBERT</a> to first find the top ranked sentences and then extracts the keyphrases" |
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"\t from those sentences using <a href = https://allenai.github.io/scispacy/>scispaCy</a> and SBERT." |
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"\t The list of SBERT models provided can be found in <a href=www.sbert.net/docs/pretrained_models.html>SBERT Pre-trained models hub</a>." |
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"\t The default model names are provided which can be changed from the list of models available. " |
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"\t The value of output keyphrases can be changed. The default value is 10, minimum is 5 and a maximum value of 30.") |
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igen.launch(share=False) |
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