# ### Keywords to Title Generator # - https://huggingface.co/EnglishVoice/t5-base-keywords-to-headline?text=diabetic+diet+plan # - Apache 2.0 import torch from transformers import T5ForConditionalGeneration,T5Tokenizer import gradio as gr device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = T5ForConditionalGeneration.from_pretrained("EnglishVoice/t5-base-keywords-to-headline") tokenizer = T5Tokenizer.from_pretrained("EnglishVoice/t5-base-keywords-to-headline", clean_up_tokenization_spaces=True, legacy=False) model = model.to(device) def title_gen(keywords, diversity, temp): if keywords!= "": text = "headline: " + keywords encoding = tokenizer.encode_plus(text, return_tensors = "pt") input_ids = encoding["input_ids"].to(device) attention_masks = encoding["attention_mask"].to(device) if diversity: num_beams = 20, num_beam_groups = 20, diversity_penalty=0.8, early_stopping = True, else: penalty_alpha = 0.8, beam_outputs = model.generate( input_ids = input_ids, attention_mask = attention_masks, max_new_tokens = 30, do_sample = True, num_return_sequences = 5, temperature = temp, top_k = 15, no_repeat_ngram_size = 3, #top_p = 0.60, ) titles = "<p align=center>Title Suggestions:</p>" for i in range(len(beam_outputs)): result = tokenizer.decode(beam_outputs[i], skip_special_tokens=True) titles += f"<p align=center><b>{result}</b></p></p>" #Create string with titles and <br> tag for html reading in gradio html return titles iface = gr.Interface(fn=title_gen, inputs=[gr.Textbox(label="Paste one or more keywords searated by a comma and hit 'Submit'.", lines=1), "checkbox", gr.Slider(0.1, 1.9, 1.2)], outputs=[gr.HTML(label="Title suggestions:")], title="AI Keywords to Title Generator", #description="Turn keywords into creative suggestions", article="<div align=left><h1>AI Creative Title Generator</h1><li>With just keywords, generate a list of creative titles.</li><li>Click on Submit to generate more title options.</li><li>Tweak slider for less or more creative titles</li><li>Check 'diversity' to turn on diversity beam search</li><p>AI Model:<br><li>T5 Model trained on a dataset of titles and related keywords</li><li>Original model id: EnglishVoice/t5-base-keywords-to-headline by English Voice AI Labs</li></p><p>Default parameter details:<br><li><code>temperature = 1.2</code>, <code>no_repeat_ngram_size=3</code>, <code>top_k = 15</code>, <code>penalty_alpha = 0.8</code>, <code>max_new_tokens = 30</code></li><p>Diversity beam search params:<br><li><code>num_beams=20</code>, <code>diversity_penalty=0.8</code>, <code>num_beam_groups=20</code></li></div>", flagging_mode='never', examples=[ ["new, weight loss, lifestyle"], ["launch, free, dating, app"], ["AI, text to video, app"], ["new movie, watch, free streaming"], ], cache_examples=True, ) iface.launch()