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| import gradio as gr | |
| import nltk | |
| from nltk.corpus import cess_esp, conll2002 | |
| from nltk.tokenize import word_tokenize | |
| import stylecloud | |
| import matplotlib.pyplot as plt | |
| from fpdf import FPDF | |
| import re | |
| from collections import Counter | |
| import spacy | |
| import random | |
| import csv | |
| # Descargar recursos necesarios de nltk | |
| nltk.download('punkt') | |
| nltk.download('stopwords') | |
| nltk.download('cess_esp') | |
| nltk.download('conll2002') | |
| # Cargar el modelo de spaCy para español | |
| nlp = spacy.load('es_core_news_md') | |
| additional_stopwords = [ | |
| # Aquí puedes agregar más stopwords si lo deseas | |
| ] | |
| # Función de preprocesamiento | |
| def preprocess_text(text): | |
| text = text.lower() | |
| text = re.sub(r'\W', ' ', text) | |
| doc = nlp(text) | |
| stop_words = set(nltk.corpus.stopwords.words('spanish')).union(set(additional_stopwords)) | |
| filtered_words = [token.lemma_ for token in doc if token.text not in stop_words and token.pos_ in ['VERB', 'ADJ', 'NOUN']] | |
| return filtered_words | |
| # Obtener oraciones de ejemplo de múltiples corpus de nltk | |
| def get_example_sentences(word, num_sentences=1): | |
| sentences = [] | |
| for corpus in [cess_esp, conll2002]: | |
| for sent in corpus.sents(): | |
| if word in sent and len(word) > 1: | |
| sentences.append(' '.join(sent)) | |
| if len(sentences) >= num_sentences: | |
| break | |
| if len(sentences) >= num_sentences: | |
| break | |
| return sentences | |
| # Función para generar la nube de palabras con estilo aleatorio | |
| def generate_random_style_cloud(words, filename): | |
| text = ' '.join(words) | |
| icons = ['fas fa-cloud', 'fas fa-star', 'fas fa-heart', 'fas fa-tree', 'fas fa-sun', 'fas fa-moon'] | |
| random_icon = random.choice(icons) | |
| stylecloud.gen_stylecloud(text=text, icon_name=random_icon, output_name=filename) | |
| img = plt.imread(filename) | |
| plt.imshow(img) | |
| plt.axis('off') | |
| plt.show() | |
| # Crear el documento PDF | |
| class PDF(FPDF): | |
| def header(self): | |
| self.set_fill_color(200, 220, 255) | |
| self.rect(0, 0, 10, 297, 'F') | |
| self.rect(200, 0, 10, 297, 'F') | |
| def footer(self): | |
| self.set_y(-15) | |
| self.set_font('Arial', 'I', 8) | |
| self.cell(0, 10, f'Page {self.page_no()}', 0, 0, 'C') | |
| def add_text_to_pdf(pdf, text, title): | |
| filtered_words = preprocess_text(text) | |
| word_freq = Counter(filtered_words) | |
| word_freq_file = f"word_freq_{title}.csv" | |
| with open(word_freq_file, 'w') as f: | |
| writer = csv.writer(f) | |
| writer.writerow(['word', 'frequency']) | |
| for word, freq in word_freq.items(): | |
| writer.writerow([word, freq]) | |
| cloud_filename = f'wordcloud_{title}.png' | |
| generate_random_style_cloud(filtered_words, cloud_filename) | |
| pdf.add_page() | |
| pdf.set_font('Arial', 'B', 16) | |
| pdf.cell(0, 10, title, ln=True, align='C') | |
| pdf.set_draw_color(0, 0, 0) | |
| pdf.set_line_width(0.5) | |
| pdf.line(10, 25, 200, 25) | |
| pdf.image(cloud_filename, x=15, y=30, w=180) | |
| pdf.add_page() | |
| pdf.set_font('Arial', 'B', 16) | |
| pdf.cell(0, 10, "Oraciones de ejemplo", ln=True, align='C') | |
| high_freq_words = sorted([word.upper() for word, freq in word_freq.most_common(20)]) | |
| pdf.set_font('Arial', 'B', 12) | |
| pdf.set_fill_color(200, 200, 200) | |
| pdf.cell(90, 10, 'PALABRA', 1, fill=True) | |
| pdf.cell(0, 10, 'ORACIÓN DE EJEMPLO', 1, fill=True) | |
| pdf.ln() | |
| pdf.set_font('Arial', '', 12) | |
| pdf.set_line_width(0.1) | |
| for word in high_freq_words: | |
| example_sent = get_example_sentences(word.lower()) | |
| if example_sent: | |
| example_sentence = example_sent[0].replace(word.lower(), f'**{word}**').replace(word, f'**{word}**') | |
| pdf.cell(90, 10, word, 1) | |
| pdf.set_font('Arial', '', 10) | |
| pdf.multi_cell(0, 10, example_sentence, 1) | |
| pdf.set_font('Arial', 'I', 8) | |
| pdf.cell(90, 10, '', 0) | |
| pdf.cell(0, 10, 'Fuente: NLTK', 0) | |
| pdf.set_font('Arial', '', 12) | |
| else: | |
| continue | |
| pdf.ln() | |
| # Función principal para la interfaz de Gradio | |
| def create_pdf_from_text(text, title): | |
| pdf = PDF() | |
| add_text_to_pdf(pdf, text, title) | |
| pdf_filename = f'{title}.pdf' | |
| pdf.output(pdf_filename) | |
| return pdf_filename | |
| # Interfaz de Gradio | |
| iface = gr.Interface( | |
| fn=create_pdf_from_text, | |
| inputs=[gr.inputs.Textbox(lines=10, label="Texto en Español"), gr.inputs.Textbox(label="Título")], | |
| outputs=gr.outputs.File(label="Descargar PDF"), | |
| title="Generador de PDFs con Nubes de Palabras" | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |