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| import gradio as gr | |
| from transformers import pipeline | |
| from transformers_interpret import SequenceClassificationExplainer | |
| from bs4 import BeautifulSoup | |
| # Setup model | |
| classifier = pipeline("text-classification", model="simonschoe/TransformationTransformer") | |
| explainer = SequenceClassificationExplainer(classifier.model, classifier.tokenizer) | |
| legend = """ | |
| <div style="text-align: center; display: block; margin-left: auto; margin-right: auto; border-top: 1px solid; margin-top: 5px; padding-top: 5px;"><b>Legend: </b><span style="display: inline-block; width: 10px; height: 10px; border: 1px solid; background-color: hsl(0, 75%, 60%)"></span> Generic <span style="display: inline-block; width: 10px; height: 10px; border: 1px solid; background-color: hsl(120, 75%, 50%)"></span> Transformation </div> | |
| """ | |
| def classify(_input): | |
| """ | |
| wrapper method to compute label 1 probability and explanation for given input | |
| """ | |
| result = classifier(_input)[0] | |
| score = result['score'] | |
| if result['label'] == 'LABEL_0': | |
| score = 1-score | |
| # getting visualization | |
| attributions = explainer(_input) | |
| html = explainer.visualize().__html__() | |
| soup = BeautifulSoup(html, 'html.parser') | |
| explanation = soup.find_all('td')[-1].__str__().replace('td', 'div') | |
| # adding legend to word importance explanation | |
| result_html = explanation + legend | |
| return score, result_html | |
| app = gr.Blocks() | |
| with app: | |
| gr.Markdown("# Transformation Intensity Classifier") | |
| gr.Markdown("## Detect Transformation Sentences in Quarterly Earnings Conference Calls") | |
| with gr.Row(): | |
| with gr.Column(): | |
| text_in = gr.Textbox(lines=1, placeholder="Insert text", label="Input Sentence") | |
| with gr.Row(): | |
| compute_bt = gr.Button("Classify") | |
| score_out = gr.Number(label="Score", interactive=False) | |
| html_out = gr.HTML(label="Explanation") | |
| with gr.Column(): | |
| gr.Markdown( | |
| """ | |
| #### Project Description | |
| Placeholder | |
| """ | |
| ) | |
| gr.Markdown( | |
| """ | |
| #### App usage | |
| The model is intented to be used for **sequence classification**: It encodes the input sentence (entered in the textbox on the left) in a dense vector space and runs it through a deep neural network classifier (*Distill-RoBERTa*). | |
| It returns a confidence score that indicates the probability of the sentence containing a discussion on transformation activities. A value of 1 (0) signals a high confidence of the sentence being transformation-related (generic). A score in the range of [0.25; 0.75] implies that the model is rather undecided about the correct label. | |
| In addition, the app returns the tokenized version of the sentence, alongside word importances that are indicated by color codes. Those visuals illustrates the ability of the context-aware classifier to simultaneously pay attention to various parts in the input sentence to derive a final label. | |
| """ | |
| ) | |
| gr.Examples( | |
| examples=[ | |
| ["If we look at the plans for 2018, it is to introduce 650 new products, which is an absolute all- time high."], | |
| ["We have been doing kind of an integrated campaign, so it's TV, online, we do the Google Ad Words - all those different elements together."], | |
| ["So that turned out to be beneficial for us, and I think, we'll just see how the market and interest rates move over the course of the year,"] | |
| ], | |
| inputs=[text_in], | |
| outputs=[score_out, html_out], | |
| fn=classify, | |
| cache_examples=True | |
| ) | |
| gr.Markdown( | |
| """ | |
| <p style="text-align: center;"> | |
| TIClassifier by X and Y | |
| <br> | |
| <img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.glitch.me/badge?page_id=simonschoe.TIC&left_color=green&right_color=blue" style="display: block; margin-left: auto; margin-right: auto;"/> | |
| </p> | |
| """ | |
| ) | |
| compute_bt.click(classify, inputs=[text_in], outputs=[score_out, html_out]) | |
| app.launch() | |