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| import gradio as gr | |
| import nltk | |
| import simplemma | |
| from nltk.tokenize import word_tokenize | |
| from nltk.tokenize import sent_tokenize | |
| from nltk.probability import FreqDist | |
| from simplemma import text_lemmatizer | |
| nltk.download('punkt') | |
| file = "text.txt" | |
| import spacy | |
| nlp_IT = spacy.load("it_core_news_sm") | |
| def get_lists(file): | |
| with open(file, 'r', encoding='utf-8') as f: | |
| text = f.read() | |
| sent_tokenized_text = sent_tokenize(text, language='italian') | |
| sent_tokenized_text_lower = [sent.lower() for sent in sent_tokenized_text] | |
| return sent_tokenized_text, sent_tokenized_text_lower | |
| sentences, sentences_lower = get_lists(file) | |
| def search_engine_collocations(target = 'scarto' , colloc = 'azioni' , nlp = nlp_IT, sentences_lower = sentences_lower, sentences = sentences): | |
| verbs = [] | |
| adjectives = [] | |
| nouns = [] | |
| result = 0 | |
| for i,sent in enumerate(sentences_lower): | |
| if target.lower() in sent: | |
| result += 1 | |
| doc = nlp(sent) | |
| for token in doc: | |
| if 'VERB' in token.pos_: | |
| verbs.append(token.lemma_) | |
| elif 'ADJ' in token.pos_: | |
| adjectives.append(token.lemma_) | |
| elif 'NOUN' in token.pos_: | |
| nouns.append(token.lemma_) | |
| if result == 0: | |
| return "Non ho trovato la parola '{target}'.\n\n" | |
| else: | |
| if colloc == 'azioni' and verbs != []: | |
| verbs_fdist = FreqDist(verbs) | |
| stringed_results = '' | |
| for n,r in enumerate(verbs_fdist.most_common()): | |
| stringed_results += str(n+1) + ': ' + str(r) + '\n\n' | |
| return f"Ho trovato {len(verbs)} azioni legate a '{target}'\n{stringed_results}\n\n" | |
| elif verbs == []: | |
| return f"Non ho trovato azioni legate a '{target}'" | |
| if colloc == 'caratteristiche' and adjectives != []: | |
| adj_fdist = FreqDist(adjectives) | |
| stringed_results = '' | |
| for n,r in enumerate(adj_fdist.most_common()): | |
| stringed_results += str(n+1) + ': ' + str(r) + '\n\n' | |
| return f"Ho trovato {len(adjectives)} caratteristiche legate a '{target}'\n{stringed_results}\n\n" | |
| elif adjectives == []: | |
| return f"Non ho trovato caratteristiche legate a '{target}'" | |
| if colloc == 'concetti' and nouns != []: | |
| nouns_fdist = FreqDist(nouns) | |
| stringed_results = '' | |
| for n,r in enumerate(nouns_fdist.most_common()): | |
| stringed_results += str(n+1) + ': ' + str(r) + '\n\n' | |
| return f"Ho trovato {len(nouns)} concetti legati a '{target}'\n{stringed_results}\n\n" | |
| elif nouns == []: | |
| return f"Non ho trovato concetti legati a '{target}'" | |
| demo = gr.Interface( | |
| search_engine_collocations, | |
| [ | |
| gr.Textbox(), | |
| gr.Radio(["azioni", "caratteristiche", "concetti"]), | |
| ], | |
| "text", | |
| examples=[ | |
| ["scarto", "azioni"], | |
| ["rifiuto", "caratteristiche"], | |
| ["sostenibilità", "concetti"], | |
| ], | |
| ) | |
| demo.launch() |