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| from transformers import * | |
| import gradio as gr | |
| import torch | |
| model_category = AutoModelForCausalLM.from_pretrained("dquisi/story_spanish_gpt2_by_category") | |
| tokenizer_category = AutoTokenizer.from_pretrained("dquisi/story_spanish_gpt2_by_category") | |
| story_gen = pipeline("text-generation", "pranavpsv/gpt2-genre-story-generator") | |
| tokenizer = AutoTokenizer.from_pretrained("dquisi/story_spanish_gpt2_v2") | |
| model = AutoModelForCausalLM.from_pretrained("dquisi/story_spanish_gpt2_v2") | |
| task_name_en_es = f"translation_en_to_es" | |
| model_name_en_es = f"Helsinki-NLP/opus-mt-en-es" | |
| task_name_es_en = f"translation_es_to_en" | |
| model_name_es_en = f"Helsinki-NLP/opus-mt-es-en" | |
| translator_es_en = pipeline(task_name_en_es, model=model_name_en_es, tokenizer=model_name_en_es) | |
| translator_en_es = pipeline(task_name_es_en, model=model_name_es_en, tokenizer=model_name_es_en) | |
| def generate_story_translate(texto,longitud=250,categoria='superheroe'): | |
| translate_en = translator_es_en(texto)[0]["translation_text"] | |
| translate_cat_en = translator_es_en(texto)[0]["translation_text"] | |
| query = "<BOS> <{0}> {1}".format(translate_cat_en, translate_en) | |
| generated_text = story_gen(query, max_length=longitud,do_sample=True,repetition_penalty=1.1, temperature=1.2, top_p=0.95, top_k=50) | |
| generated_text = generated_text[0]['generated_text'] | |
| generated_text = generated_text.split('> ')[2] | |
| return translator_en_es(generated_text)[0]["translation_text"] | |
| def generate_story_custom(texto,longitud=250): | |
| query = "<BOS> <{0}>".format(texto) | |
| input_ids = tokenizer(query, return_tensors="pt")["input_ids"] | |
| output = model.generate(input_ids, max_length=longitud,do_sample=True,repetition_penalty=1.1, temperature=1.2, top_p=0.95, top_k=50) | |
| return tokenizer.decode(output[0]) | |
| def generate_story_custom_category(texto,longitud=250,categoria='superheroe'): | |
| query = "<BOS> <{0}> {1}".format(categoria, texto) | |
| input_ids = tokenizer_category(query, return_tensors="pt")["input_ids"] | |
| output = model_category.generate(input_ids, max_length=longitud,do_sample=True,repetition_penalty=1.1, temperature=1.2, top_p=0.95, top_k=50) | |
| return tokenizer_category.decode(output[0]) | |
| contexto = gr.inputs.Textbox(lines=10, placeholder="Ingresar palabras claves para generar un cuento") | |
| categoria = gr.inputs.Textbox(lines=1, placeholder="Ingresar Categoria") | |
| longitud = gr.inputs.Slider(50, 500) | |
| opciones = gr.inputs.CheckboxGroup(["Generar", "Generar por Categoria", "Generar 2"]) | |
| resultado = gr.outputs.HTML(label="Resultado") | |
| def generate_storie(contexto,categoria,longitud,opciones): | |
| resultado="" | |
| cuentos=[] | |
| if "Generar" in opciones: | |
| cuentos.append(generate_story_custom(contexto,longitud)) | |
| if "Generar por Categoria" in opciones: | |
| cuentos.append(generate_story_custom_category(contexto,longitud,categoria)) | |
| if "Generar 2" in opciones: | |
| cuentos.append(generate_story_translate(contexto,longitud,categoria)) | |
| resultado += "<p><b>Generados:</b> "+'<b>Cuento: <b/>'.join(cuentos)+"</p>" | |
| return resultado | |
| iface = gr.Interface( | |
| fn=generate_storie, | |
| inputs=[contexto,categoria,longitud,opciones], | |
| outputs=resultado) | |
| iface.launch(debug=True) |