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| from huggingface_hub import from_pretrained_fastai | |
| import gradio as gr | |
| from fastai.vision.all import * | |
| # repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME" | |
| repo_id = "rodrigomiranda98/tweet-eval-emotion" | |
| learner = from_pretrained_fastai(repo_id) | |
| labels = ['anger', 'joy', 'optimism', 'sadness'] | |
| # Definimos una función que se encarga de llevar a cabo las predicciones | |
| def predict(text): | |
| pred, pred_idx, probs = learner.predict(text) | |
| print(f'pred: {pred}') | |
| print(f'probs: {probs}') | |
| return {labels[i]: float(probs[i]) for i in range(len(labels))} | |
| # Creamos la interfaz y la lanzamos. | |
| gr.Interface( | |
| fn=predict, | |
| inputs=gr.inputs.Textbox(lines=5, placeholder="Inserta el texto aquí"), | |
| outputs=gr.outputs.Label(num_top_classes=4), | |
| examples=["Everything will be fine, you'll see!", "My favorite team did not qualify, I'm sad."] | |
| ).launch(share=False) |