| from transformers import pipeline | |
| import gradio as gr | |
| model_checkpoint = "MuntasirHossain/RoBERTa-base-finetuned-emotion" | |
| model = pipeline("text-classification", model=model_checkpoint) | |
| def classify(text): | |
| label = model(text)[0]["label"] | |
| return label | |
| description = "This AI model is trained to classify texts expressing human emotion into different categories." | |
| title = "Xoxo's Texts Expressing Emotion" | |
| examples = [["He is very happy Today", | |
| "NOTE Free Palestien"]] | |
| theme = { | |
| "container": { | |
| "background-color": "#007bff", | |
| "color": "#fff", | |
| "padding": "20px", | |
| }, | |
| "textbox": { | |
| "background-color": "#fff", | |
| "border-radius": "5px", | |
| "padding": "10px", | |
| "margin-bottom": "10px", | |
| }, | |
| "button": { | |
| "background-color": "#007bff", | |
| "color": "#fff", | |
| "padding": "10px", | |
| "border-radius": "5px", | |
| "cursor": "pointer", | |
| }, | |
| "label": { | |
| "color": "#fff", | |
| }, | |
| } | |
| gr.Interface(fn=classify, | |
| inputs="textbox", | |
| outputs="text", | |
| title=title, | |
| theme=theme, | |
| description=description, | |
| examples=examples, | |
| ).launch() | |