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| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| from transformers_interpret import SequenceClassificationExplainer | |
| import torch | |
| class EmotionDetection: | |
| """ | |
| Emotion Detection on text data. | |
| Attributes: | |
| tokenizer: An instance of Hugging Face Tokenizer | |
| model: An instance of Hugging Face Model | |
| explainer: An instance of SequenceClassificationExplainer from Transformers interpret | |
| """ | |
| def __init__(self): | |
| hub_location = 'cardiffnlp/twitter-roberta-base-emotion' | |
| self.tokenizer = AutoTokenizer.from_pretrained(hub_location) | |
| self.model = AutoModelForSequenceClassification.from_pretrained(hub_location) | |
| self.explainer = SequenceClassificationExplainer(self.model, self.tokenizer) | |
| # Friendly emoji mapping | |
| self.emoji_map = { | |
| "joy": "😊", | |
| "anger": "😠", | |
| "optimism": "😎", | |
| "sadness": "😢" | |
| } | |
| # Friendly explanation mapping | |
| self.explanation_map = { | |
| "joy": "The person is happy or excited.", | |
| "anger": "The person is upset or angry.", | |
| "optimism": "The person is feeling hopeful or positive.", | |
| "sadness": "The person is feeling low or unhappy." | |
| } | |
| def justify(self, text): | |
| """ | |
| Get html annotation for displaying emotion justification over text. | |
| Parameters: | |
| text (str): The user input string for emotion justification | |
| Returns: | |
| html (str): html string for plotting emotion prediction justification | |
| """ | |
| word_attributions = self.explainer(text) | |
| html = self.explainer.visualize(return_html=True) # Changed to return HTML string | |
| return html | |
| def classify(self, text): | |
| """ | |
| Recognize Emotion in text (simplified). | |
| Parameters: | |
| text (str): The user input string to perform emotion classification on | |
| Returns: | |
| result (str): User-friendly emotion label with emoji and explanation | |
| """ | |
| tokens = self.tokenizer.encode_plus(text, return_tensors='pt') | |
| outputs = self.model(**tokens) | |
| probs = torch.nn.functional.softmax(outputs[0], dim=-1) | |
| probs = probs.mean(dim=0).detach().numpy() | |
| labels = list(self.model.config.id2label.values()) | |
| max_index = probs.argmax() | |
| emotion = labels[max_index] | |
| confidence = probs[max_index] | |
| emoji = self.emoji_map.get(emotion, "") | |
| explanation = self.explanation_map.get(emotion, "") | |
| result = f"{emoji} **{emotion.capitalize()}** ({confidence:.1%})\n{explanation}" | |
| return result | |
| def run(self, text): | |
| """ | |
| Classify and Justify Emotion in text. | |
| Parameters: | |
| text (str): The user input string to perform emotion classification on | |
| Returns: | |
| result (str): Friendly emotion classification output | |
| html (str): HTML visualization string for model justification | |
| """ | |
| result = self.classify(text) | |
| html = self.justify(text) | |
| return result, html | |