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| import transformers | |
| from transformers import EncoderDecoderModel,BertTokenizer | |
| import gradio as gr | |
| import pandas as pd | |
| import torch | |
| #loading tokenizer and model | |
| tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') | |
| Model = EncoderDecoderModel.from_pretrained('damilojohn/Bert2BertForTextDescrambling') | |
| def descramble(prompt): | |
| input = tokenizer(prompt,return_tensors='pt') | |
| input_id = input.input_ids | |
| attention_mask = input.attention_mask | |
| max_length = len(prompt.split(' ')) | |
| output = Model.generate(input_ids=input_id,attention_mask=attention_mask,) | |
| output = tokenizer.decode(output[0],skip_special_tokens=True) | |
| return gr.Textbox.update(value=output) | |
| examples = [['layer Neurons receptive of input visual develop cortex primates in edge-like primary in the fields.'], | |
| ['of role unknown. still is in largely the representations homeostasis sparse such learning specific However,'], | |
| ['coding sparse is fair. optimized it when is Competition in'], | |
| ['sparse excitatory neurons. of inhibitory connections populations and and separate'], | |
| ['E. in proteins to oscillation Ongoing is Min required minicelling coli. block of sub-cellular'], | |
| ['Experimentally, newly and divided are Min minicells produced. cells no are in seen oscillations'], | |
| ['this behavior been role of sedentary has determined. The in not defect'], | |
| ['connections models These have for and important consequences of dynamics protein thermodynamics.'], | |
| ['plays role metric classification. The an important (NN) in nearest neighbor distance'], | |
| ['physiologically monostability. likely more That within ranges for multistability plausible becomes parameters, is, than']] | |
| def set_example(example): | |
| return gr.TextArea.update(value=example[0]) | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown( | |
| ''' | |
| # A Text Descrambler 😎😎 | |
| Turn your Incoherent Sentences to Grammatically correct Sentences. | |
| This was built using transformers and Gradio | |
| ''') | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown( | |
| ''' | |
| Enter a meaningless sentence here | |
| ''') | |
| prompt = gr.TextArea( | |
| value = examples[0][0], | |
| placeholder = "Enter A Text to see it's correct form " | |
| ) | |
| example_prompts = gr.Dataset( | |
| components = [prompt], | |
| samples = examples) | |
| with gr.Column(): | |
| find_answer = gr.Button('Click here to generate your sentence 👀🤺').style(full_width=False) | |
| with gr.Column(): | |
| answer = gr.Textbox(label='Answer',placeholder = "Correct Form") | |
| with gr.Column(): | |
| gr.Markdown( | |
| ''' | |
| ## Under Construction ⏳, | |
| ''') | |
| find_answer.click( | |
| fn=descramble, | |
| inputs=[prompt], | |
| outputs=[answer] | |
| ) | |
| example_prompts.click( | |
| fn=set_example, | |
| inputs=[example_prompts], | |
| outputs=example_prompts.components, | |
| ) | |
| demo.launch() |