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| import gradio as gr | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer, TextClassificationPipeline | |
| tokenizer = AutoTokenizer.from_pretrained("daspartho/text-emotion") | |
| model = AutoModelForSequenceClassification.from_pretrained("daspartho/text-emotion") # i've uploaded the model on HuggingFace :) | |
| pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, top_k=6) | |
| label_map={ | |
| 'LABEL_0':'🙁', | |
| 'LABEL_1':'😃', | |
| 'LABEL_2':'🥰', | |
| 'LABEL_3':'😠', | |
| 'LABEL_4':'😬', | |
| 'LABEL_5':'😳' | |
| } | |
| def classify_text(text): | |
| predictions = pipe(text)[0] | |
| return {label_map[pred['label']]: float(pred['score']) for pred in predictions} | |
| iface = gr.Interface( | |
| title='Text Emotion', | |
| description = "enter a text and the model will attempt to predict the emotion.", | |
| article = "<p style='text-align: center'><a href='https://github.com/daspartho/text-emotion' target='_blank'>Github</a></p>", | |
| fn=classify_text, | |
| inputs=gr.inputs.Textbox(label="type the text here"), | |
| outputs=gr.outputs.Label(label='what the model thinks'), | |
| ) | |
| iface.launch() |