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| from transformers import TFAutoModelForCausalLM, AutoTokenizer | |
| import tensorflow as tf | |
| import gradio as gr | |
| import spacy | |
| from spacy import displacy | |
| from transformers import TFAutoModelForSequenceClassification | |
| from transformers import AutoTokenizer | |
| from scipy.special import softmax | |
| import plotly.express as px | |
| import plotly.io as pio | |
| # configuration params | |
| pio.templates.default = "plotly_dark" | |
| # setting up the text in the page | |
| TITLE = "<center><h1>Talk with an AI</h1></center>" | |
| DESCRIPTION = r"""<center>This application allows you to talk with a machine/robot with state-of-the-art technology!!<br> | |
| In the back-end is using the GPT2 model from OpenAI. One of the best models in text generation and comprehension.<br> | |
| Language processing is done using RoBERTa for sentiment-analysis and spaCy for named-entity recognition and dependency plotting.<br> | |
| The AI thinks he is a human, so please treat him as such, else he migh get angry!<br> | |
| """ | |
| EXAMPLES = [ | |
| ["What is your favorite videogame?"], | |
| ["What gets you really sad?"], | |
| ["How can I make you really angry? "], | |
| ["What do you do for work?"], | |
| ["What are your hobbies?"], | |
| ["What is your favorite food?"], | |
| ] | |
| ARTICLE = r"""<center> | |
| Done by dr. Gabriel Lopez<br> | |
| For more please visit: <a href='https://sites.google.com/view/dr-gabriel-lopez/home'>My Page</a><br> | |
| For info about the chat-bot model can also see the <a href="https://arxiv.org/abs/1911.00536">ArXiv paper</a><br> | |
| </center>""" | |
| # Loading necessary NLP models | |
| # dialog | |
| checkpoint = "microsoft/DialoGPT-medium" # tf | |
| model_gtp2 = TFAutoModelForCausalLM.from_pretrained(checkpoint) | |
| tokenizer_gtp2 = AutoTokenizer.from_pretrained(checkpoint) | |
| # sentiment | |
| checkpoint = f"cardiffnlp/twitter-roberta-base-emotion" | |
| model_roberta = TFAutoModelForSequenceClassification.from_pretrained(checkpoint) | |
| tokenizer_roberta = AutoTokenizer.from_pretrained(checkpoint) | |
| # NER & Dependency | |
| nlp = spacy.load("en_core_web_sm") | |
| # test-to-test : chatting function -- GPT2 | |
| def chat_with_bot(user_input, chat_history_and_input=[]): | |
| """Text generation using GPT2""" | |
| emb_user_input = tokenizer_gtp2.encode( | |
| user_input + tokenizer_gtp2.eos_token, return_tensors="tf" | |
| ) | |
| if chat_history_and_input == []: | |
| bot_input_ids = emb_user_input # first iteration | |
| else: | |
| bot_input_ids = tf.concat( | |
| [chat_history_and_input, emb_user_input], axis=-1 | |
| ) # other iterations | |
| chat_history_and_input = model_gtp2.generate( | |
| bot_input_ids, max_length=1000, pad_token_id=tokenizer_gtp2.eos_token_id | |
| ).numpy() | |
| bot_response = tokenizer_gtp2.decode( | |
| chat_history_and_input[:, bot_input_ids.shape[-1] :][0], | |
| skip_special_tokens=True, | |
| ) | |
| return bot_response, chat_history_and_input | |
| # text-to-sentiment | |
| def text_to_sentiment(text_input): | |
| """Sentiment analysis using RoBERTa""" | |
| labels = ["anger", "joy", "optimism", "sadness"] | |
| encoded_input = tokenizer_roberta(text_input, return_tensors="tf") | |
| output = model_roberta(encoded_input) | |
| scores = output[0][0].numpy() | |
| scores = softmax(scores) | |
| return px.histogram(x=labels, y=scores, height=200) | |
| # text_to_semantics | |
| def text_to_semantics(text_input): | |
| """NER and Dependency plot using Spacy""" | |
| processed_text = nlp(text_input) | |
| # Dependency | |
| html_dep = displacy.render( | |
| processed_text, | |
| style="dep", | |
| options={"compact": True, "color": "white", "bg": "light-black"}, | |
| page=False, | |
| ) | |
| html_dep = "" + html_dep + "" | |
| # NER | |
| pos_tokens = [] | |
| for token in processed_text: | |
| pos_tokens.extend([(token.text, token.pos_), (" ", None)]) | |
| # html_ner = ("" + html_ner + "")s | |
| return pos_tokens, html_dep | |
| # gradio interface | |
| blocks = gr.Blocks() | |
| with blocks: | |
| # physical elements | |
| session_state = gr.State([]) | |
| gr.Markdown(TITLE) | |
| gr.Markdown(DESCRIPTION) | |
| with gr.Row(): | |
| with gr.Column(): | |
| in_text = gr.Textbox(value="How was the class?", label="Start chatting!") | |
| submit_button = gr.Button("Submit") | |
| gr.Examples(inputs=in_text, examples=EXAMPLES) | |
| with gr.Column(): | |
| response_text = gr.Textbox(value="", label="GPT2 response:") | |
| sentiment_plot = gr.Plot( | |
| label="How is GPT2 feeling about your conversation?:", visible=True | |
| ) | |
| ner_response = gr.Highlight( | |
| label="Named Entity Recognition (NER) over response" | |
| ) | |
| dependency_plot = gr.HTML(label="Dependency plot of response") | |
| gr.Markdown(ARTICLE) | |
| # event listeners | |
| submit_button.click( | |
| inputs=[in_text, session_state], | |
| outputs=[response_text, session_state], | |
| fn=chat_with_bot, | |
| ) | |
| response_text.change( | |
| inputs=response_text, outputs=sentiment_plot, fn=text_to_sentiment | |
| ) | |
| response_text.change( | |
| inputs=response_text, | |
| outputs=[ner_response, dependency_plot], | |
| fn=text_to_semantics, | |
| ) | |
| blocks.launch() | |