| import gradio as gr |
| from transformers import pipeline |
|
|
| bert_clf = pipeline("text-classification", |
| model="magomerob/clasificador-emotion", |
| top_k=None) |
|
|
| label_map = { |
| "LABEL_0": "sadness", |
| "LABEL_1": "joy", |
| "LABEL_2": "love", |
| "LABEL_3": "anger", |
| "LABEL_4": "fear", |
| "LABEL_5": "surprise" |
| } |
|
|
| def classify(text, model_choice): |
| clf = bert_clf |
| results = clf(text)[0] |
| return {label_map[r["label"]]: round(r["score"], 4) for r in results} |
|
|
| demo = gr.Interface( |
| fn=classify, |
| inputs=[ |
| gr.Textbox(label="Introduce un texto en inglés", lines=3, |
| placeholder="I feel so happy today!"), |
| gr.Radio(choices=["BERT"], value="BERT", label="Modelo") |
| ], |
| outputs=gr.Label(num_top_classes=6, label="Emoción detectada"), |
| title="Emotion Classifier", |
| description="Clasifica el sentimiento de un texto en inglés en 6 emociones: " |
| "sadness, joy, love, anger, fear, surprise.", |
| examples=[ |
| ["I am so happy and excited about this!", "BERT"], |
| ["I feel devastated and heartbroken.", "BERT"], |
| ["This made me so angry I could scream.", "BERT"], |
| ["I am terrified of what might happen.", "BERT"], |
| ] |
| ) |
|
|
| demo.launch() |
