Spaces:
Sleeping
Sleeping
| import numpy as np | |
| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline, T5Tokenizer, T5Model, BertTokenizer, BertModel, T5ForConditionalGeneration, AutoTokenizer, AutoModelForSeq2SeqLM | |
| # 1. GENERATE SUMMARY | |
| tokenizer = AutoTokenizer.from_pretrained("suriya7/bart-finetuned-text-summarization") | |
| model = AutoModelForSeq2SeqLM.from_pretrained("suriya7/bart-finetuned-text-summarization") | |
| def generate_summary(text): | |
| print(text) | |
| inputs = tokenizer([text], max_length=1024, return_tensors='pt', truncation=True) | |
| summary_ids = model.generate(inputs['input_ids'], max_new_tokens=100, do_sample=False) | |
| summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) | |
| return summary | |
| # 2. TRANSLATE FUNCTION | |
| t5_tokenizer = T5Tokenizer.from_pretrained('t5-small') | |
| t5_model = T5ForConditionalGeneration.from_pretrained('t5-small') | |
| def translate_text(text_to_translate, original_language, destination_language): | |
| input_text = "translate "+original_language+" to "+destination_language+": "+text_to_translate | |
| input_ids = t5_tokenizer.encode(input_text, return_tensors='pt') | |
| outputs = t5_model.generate(input_ids) | |
| output_text = t5_tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return(output_text) | |
| # 4. QUESTION ANSWERING FUNCTION | |
| def question_answering(question,context): | |
| qa_model = pipeline("question-answering", "timpal0l/mdeberta-v3-base-squad2") | |
| question = question | |
| context = context | |
| solution = qa_model(question = question, context = context) | |
| return solution['answer'] | |
| # 5. PARAPHRASING FUNCTION | |
| paraphrasing_tokenizer = AutoTokenizer.from_pretrained("vngrs-ai/VBART-Large-Paraphrasing", model_input_names=['input_ids', 'attention_mask']) | |
| paraphrasing_model = AutoModelForSeq2SeqLM.from_pretrained("vngrs-ai/VBART-Large-Paraphrasing") | |
| def paraphrasing(text): | |
| input_text= text | |
| token_input = tokenizer(input_text, return_tensors="pt")#.to('cuda') | |
| outputs = model.generate(**token_input) | |
| return(tokenizer.decode(outputs[0])) | |
| with gr.Blocks() as demo: | |
| gr.Markdown("My AI interface") | |
| with gr.Tab("Single models"): | |
| # 1. GENERATE SUMMARY | |
| with gr.Accordion("Text summarization"): | |
| gr.Markdown("Single model summarization using BART model") | |
| text_to_summarize = gr.Textbox(label="Text to summarize") | |
| summary_output = gr.Textbox(label="Summary") | |
| summarize_btn = gr.Button("Summarize") | |
| # 2. TRANSLATE FUNCTION | |
| with gr.Accordion("Text translation", open=False): | |
| gr.Markdown("Single model translation using GOOGLE T5 Base model") | |
| text_to_translate = gr.Textbox(label="Text to translate") | |
| original_language = gr.Textbox(label="Original language (Write in full form e.g. english)") | |
| destination_language = gr.Textbox(label="Destination language (Write in full form e.g. deutsch)") | |
| translate_output = gr.Textbox(label="Translation") | |
| translate_btn = gr.Button("Translate") | |
| # 3. .. | |
| with gr.Accordion("Scentence fill mask", open=False): | |
| gr.Markdown("Single model translation using GOOGLE T5 Base model") | |
| scentence_To_fill = gr.Textbox(label="Text to translate") | |
| filled_scentence = gr.Textbox(label="Translation") | |
| fill_button = gr.Button("Fill scentence") | |
| # 4. QUESTION ANSWERING | |
| with gr.Accordion("Question answering", open=False): | |
| gr.Markdown("Single model question answering using GOOGLE mdeberta model") | |
| question = gr.Textbox(label="Question") | |
| context = gr.Textbox(label="Context for question") | |
| answer = gr.Textbox(label="Answer to question") | |
| ask_question_button = gr.Button("Ask question") | |
| # 5. PARAPHRASING | |
| with gr.Accordion("Paraphrasing", open=False): | |
| gr.Markdown("Single model paraphrasing using the VBART model") | |
| scentence_to_rephrase = gr.Textbox(label="Text to rephrase") | |
| rephrased_scentence = gr.Textbox(label="Rephrased scentence") | |
| paraphrase_button = gr.Button("Rephrase scentence") | |
| with gr.Tab("Multi models"): | |
| with gr.Row(): | |
| print("No multi models yet..") | |
| # Button listeners | |
| summarize_btn.click(generate_summary, inputs=text_to_summarize, outputs=summary_output) # 1. GENERATE SUMMARY | |
| translate_btn.click(translate_text, inputs=[text_to_translate, original_language, destination_language], outputs=translate_output) # 2. TRANSLATE FUNCTION | |
| ask_question_button.click(question_answering, inputs=[question,context], outputs=answer) # 4. QUESTION ANSWERING | |
| paraphrase_button.click(paraphrasing, inputs=scentence_to_rephrase, outputs=rephrased_scentence) # 5. PARAPHRASING | |
| demo.launch() | |