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| from tamilatis.predict import TamilATISPredictor | |
| from tamilatis.model import JointATISModel | |
| import numpy as np | |
| from sklearn.preprocessing import LabelEncoder | |
| import gradio as gr | |
| model_name = "microsoft/xlm-align-base" | |
| tokenizer_name = "microsoft/xlm-align-base" | |
| num_labels = 78 | |
| num_intents = 23 | |
| checkpoint_path = "tamilatis/models/xlm_align_base.bin" | |
| intent_encoder_path = "tamilatis/models/intent_classes.npy" | |
| ner_encoder_path = "tamilatis/models/ner_classes.npy" | |
| def predict_function(text): | |
| label_encoder = LabelEncoder() | |
| label_encoder.classes_ = np.load(ner_encoder_path) | |
| intent_encoder = LabelEncoder() | |
| intent_encoder.classes_ = np.load(intent_encoder_path) | |
| model = JointATISModel(model_name,num_labels,num_intents) | |
| predictor = TamilATISPredictor(model,checkpoint_path,tokenizer_name, | |
| label_encoder,intent_encoder,num_labels) | |
| slot_prediction, intent_preds = predictor.get_predictions(text) | |
| return slot_prediction, intent_preds | |
| title = "Tamil ATIS" | |
| iface = gr.Interface(fn=predict_function, inputs="text", title=title,theme="huggingface",outputs=["text","text"]) | |
| iface.launch(share=True) | |