#!/usr/bin/env python # coding: utf-8 # ### Keywords to Title Generator # - https://huggingface.co/EnglishVoice/t5-base-keywords-to-headline?text=diabetic+diet+plan # - Apache 2.0 # In[7]: import torch from transformers import T5ForConditionalGeneration,T5Tokenizer 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) # In[37]: 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 = "" for i in range(len(beam_outputs)): result = tokenizer.decode(beam_outputs[i], skip_special_tokens=True) titles += f"

{result}

" #Create string with titles and
tag for html reading in gradio html return titles # In[8]: import gradio as gr # In[40]: 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="

AI Creative Title Generator

  • With just keywords, generate a list of creative titles.
  • Click on Submit to generate more title options.
  • Tweak slider for less or more creative titles
  • Check 'diversity' to turn on diversity beam search
  • AI Model:

  • T5 Model trained on a dataset of titles and related keywords
  • Original model id: EnglishVoice/t5-base-keywords-to-headline by English Voice AI Labs
  • Default parameter details:

  • Temperature = 1.2, no_repeat_ngram_size=3, top_k = 15, penalty_alpha = 0.8, max_new_tokens = 30
  • Diversity beam search params:

  • num_beams=20, diversity_penalty=0.8, num_beam_groups=20
  • ", flagging_mode='never' ) iface.launch() # In[ ]: ''' #Create a four button panel for changing parameters with one click def fn(text): return ("Hello gradio!") with gr.Blocks () as demo: with gr.Row(variant='compact') as PanelRow1: #first row: top with gr.Column(scale=0, min_width=180) as PanelCol5: gr.HTML("") with gr.Column(scale=0) as PanelCol4: submit = gr.Button("Temp++", scale=0) with gr.Column(scale=1) as PanelCol5: gr.HTML("") with gr.Row(variant='compact') as PanelRow2: #2nd row: left, right, middle with gr.Column(min_width=100) as PanelCol1: submit = gr.Button("Contrastive") with gr.Column(min_width=100) as PanelCol2: submit = gr.Button("Re-generate") with gr.Column(min_width=100) as PanelCol3: submit = gr.Button("Diversity Beam") with gr.Column(min_width=100) as PanelCol5: gr.HTML("") with gr.Column(min_width=100) as PanelCol5: gr.HTML("") with gr.Column(scale=0) as PanelCol5: gr.HTML("") with gr.Row(variant='compact') as PanelRow3: #last row: down with gr.Column(scale=0, min_width=180) as PanelCol7: gr.HTML("") with gr.Column(scale=1) as PanelCol6: submit = gr.Button("Temp--", scale=0) with gr.Column(scale=0) as PanelCol5: gr.HTML("") demo.launch() '''