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| from transformers import AutoModelForSequenceClassification | |
| from transformers import AutoTokenizer, AutoConfig | |
| import numpy as np | |
| from scipy.special import softmax | |
| import gradio as gr | |
| # Preprocess text (username and link placeholders) | |
| def preprocess(text): | |
| new_text = [] | |
| for t in text.split(" "): | |
| t = '@user' if t.startswith('@') and len(t) > 1 else t | |
| t = 'http' if t.startswith('http') else t | |
| new_text.append(t) | |
| return " ".join(new_text) | |
| # load model | |
| MODEL = f"cardiffnlp/twitter-roberta-base-sentiment-latest" | |
| model = AutoModelForSequenceClassification.from_pretrained(MODEL) | |
| #model.save_pretrained(MODEL) | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL) | |
| config = AutoConfig.from_pretrained(MODEL) | |
| # create classifier function | |
| def classify_sentiments(text): | |
| text = preprocess(text) | |
| encoded_input = tokenizer(text, return_tensors='pt') | |
| output = model(**encoded_input) | |
| scores = output[0][0].detach().numpy() | |
| scores = softmax(scores) | |
| # Print labels and scores | |
| probs = {} | |
| ranking = np.argsort(scores) | |
| ranking = ranking[::-1] | |
| for i in range(len(scores)): | |
| l = config.id2label[ranking[i]] | |
| s = scores[ranking[i]] | |
| probs[l] = np.round(float(s), 4) | |
| return probs | |
| #build the Gradio app | |
| #Instructuction = "Write an imaginary review about a product or service you might be interested in." | |
| title="Text Sentiment Analysis" | |
| description = """Write a Good or Bad review about an imaginary product or service,\ | |
| see how the machine learning model is able to predict your sentiments""" | |
| article = """ | |
| - Click submit button to test sentiment analysis prediction | |
| - Click clear button to refresh text | |
| """ | |
| gr.Interface(classify_sentiments, | |
| 'text', | |
| 'label', | |
| title = title, | |
| description = description, | |
| #Instruction = Instructuction, | |
| article = article, | |
| allow_flagging = "never", | |
| live = False, | |
| examples=["This has to be the best Introductory course in machine learning", | |
| "I consider this training an absolute waste of time."] | |
| ).launch() | |