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| from transformers import pipeline, set_seed | |
| import gradio as gr | |
| classifier = pipeline('text-generation', model='gpt2') | |
| set_seed(42) | |
| def generate_text(text, gen_length): | |
| gen_text = classifier(text, max_length=gen_length)[0]['generated_text'] | |
| return gen_text | |
| Instructuction = "Browse the internet to download any unique image" | |
| title="Text generation playground" | |
| description = "Start writing a peice of text in the input box\ | |
| and see how well the text generation language model\ | |
| is able to generate new text that uniquely completes your sentences." | |
| article = """ | |
| - Write a text in the input box and specify the length of text. | |
| - Also you can select a quick example to continue. | |
| - Click submit button to generate new text. | |
| - Click clear button to try new text generation. | |
| """ | |
| # Gradio app design | |
| interface = gr.Interface( | |
| generate_text, | |
| inputs = ['text', gr.Slider(20, 120, value=80, step=1)], | |
| outputs='text', | |
| title = title, | |
| description = description, | |
| article = article, | |
| allow_flagging = "never", | |
| #theme = "peach", | |
| #live = False, | |
| examples=[["Agriculture is very fundamental to", | |
| 50], ["I will tell a story about", | |
| 100]] | |
| ) | |
| interface.launch() | |