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Browse files- app.py +99 -18
- requirements.txt +2 -1
app.py
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import numpy as np
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import gradio as gr
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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def flip_text(x):
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return x[::-1]
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def flip_image(x):
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return np.fliplr(x)
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tokenizer = AutoTokenizer.from_pretrained("suriya7/bart-finetuned-text-summarization")
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model = AutoModelForSeq2SeqLM.from_pretrained("suriya7/bart-finetuned-text-summarization")
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summary_ids = model.generate(inputs['input_ids'], max_new_tokens=100, do_sample=False)
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summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
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return summary
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with gr.Blocks() as
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gr.Markdown("My AI interface")
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with gr.Tab("Single models"):
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with gr.Tab("Multi models"):
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with gr.Row():
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image_output = gr.Image()
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image_button = gr.Button("Flip")
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summarize_btn.click(generate_summary, inputs=text_to_summarize, outputs=summary_output)
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import numpy as np
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import gradio as gr
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline, T5Tokenizer, T5Model, BertTokenizer, BertModel, T5ForConditionalGeneration, AutoTokenizer, AutoModelForSeq2SeqLM
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# 1. GENERATE SUMMARY
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tokenizer = AutoTokenizer.from_pretrained("suriya7/bart-finetuned-text-summarization")
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model = AutoModelForSeq2SeqLM.from_pretrained("suriya7/bart-finetuned-text-summarization")
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summary_ids = model.generate(inputs['input_ids'], max_new_tokens=100, do_sample=False)
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summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
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return summary
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# 2. TRANSLATE FUNCTION
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t5_tokenizer = T5Tokenizer.from_pretrained('t5-small')
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t5_model = T5ForConditionalGeneration.from_pretrained('t5-small')
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def translate_text(text_to_translate, original_language, destination_language):
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input_text = "translate "+original_language+" to "+destination_language+": "+text_to_translate
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input_ids = t5_tokenizer.encode(input_text, return_tensors='pt')
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outputs = t5_model.generate(input_ids)
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output_text = t5_tokenizer.decode(outputs[0], skip_special_tokens=True)
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return(output_text)
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# 4. QUESTION ANSWERING FUNCTION
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def question_answering(question,context):
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qa_model = pipeline("question-answering", "timpal0l/mdeberta-v3-base-squad2")
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question = question
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context = context
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solution = qa_model(question = question, context = context)
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return solution['answer']
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# 5. PARAPHRASING FUNCTION
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paraphrasing_tokenizer = AutoTokenizer.from_pretrained("vngrs-ai/VBART-Large-Paraphrasing", model_input_names=['input_ids', 'attention_mask'])
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paraphrasing_model = AutoModelForSeq2SeqLM.from_pretrained("vngrs-ai/VBART-Large-Paraphrasing")
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def paraphrasing(text):
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input_text= text
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token_input = tokenizer(input_text, return_tensors="pt")#.to('cuda')
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outputs = model.generate(**token_input)
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return(tokenizer.decode(outputs[0]))
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with gr.Blocks() as demo:
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gr.Markdown("My AI interface")
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with gr.Tab("Single models"):
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# 1. GENERATE SUMMARY
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with gr.Accordion("Text summarization"):
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gr.Markdown("Single model summarization using BART model")
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text_to_summarize = gr.Textbox(label="Text to summarize")
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summary_output = gr.Textbox(label="Summary")
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summarize_btn = gr.Button("Summarize")
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# 2. TRANSLATE FUNCTION
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with gr.Accordion("Text translation", open=False):
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gr.Markdown("Single model translation using GOOGLE T5 Base model")
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text_to_translate = gr.Textbox(label="Text to translate")
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original_language = gr.Textbox(label="Original language (Write in full form e.g. english)")
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destination_language = gr.Textbox(label="Destination language (Write in full form e.g. deutsch)")
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translate_output = gr.Textbox(label="Translation")
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translate_btn = gr.Button("Translate")
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# 3. ..
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with gr.Accordion("Scentence fill mask", open=False):
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gr.Markdown("Single model translation using GOOGLE T5 Base model")
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scentence_To_fill = gr.Textbox(label="Text to translate")
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filled_scentence = gr.Textbox(label="Translation")
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fill_button = gr.Button("Fill scentence")
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# 4. QUESTION ANSWERING
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with gr.Accordion("Question answering", open=False):
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gr.Markdown("Single model question answering using GOOGLE mdeberta model")
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question = gr.Textbox(label="Question")
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context = gr.Textbox(label="Context for question")
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answer = gr.Textbox(label="Answer to question")
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ask_question_button = gr.Button("Ask question")
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# 5. PARAPHRASING
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with gr.Accordion("Paraphrasing", open=False):
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gr.Markdown("Single model paraphrasing using the VBART model")
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scentence_to_rephrase = gr.Textbox(label="Text to rephrase")
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rephrased_scentence = gr.Textbox(label="Rephrased scentence")
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paraphrase_button = gr.Button("Rephrase scentence")
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with gr.Tab("Multi models"):
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with gr.Row():
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print("No multi models yet..")
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# Button listeners
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summarize_btn.click(generate_summary, inputs=text_to_summarize, outputs=summary_output) # 1. GENERATE SUMMARY
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translate_btn.click(translate_text, inputs=[text_to_translate, original_language, destination_language], outputs=translate_output) # 2. TRANSLATE FUNCTION
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ask_question_button.click(question_answering, inputs=[question,context], outputs=answer) # 4. QUESTION ANSWERING
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paraphrase_button.click(paraphrasing, inputs=scentence_to_rephrase, outputs=rephrased_scentence) # 5. PARAPHRASING
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demo.launch()
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requirements.txt
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torchvision==0.17.2
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transformers==4.40.0
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torchvision==0.17.2
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transformers==4.40.0
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sentencepiece==0.2.0
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