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import gradio as gr
import numpy as np
from huggingface_hub import hf_hub_download
import os
def predict_label(text):
ip = text.split()
ip_len = [len(ip)]
span_scores = extract_spannet_scores(span_model,ip,ip_len, pos_col=1, task_col=2)
span_pooled_scores = pool_span_scores(span_scores, ip_len)
msa_span_scores = extract_spannet_scores(msa_span_model,ip,ip_len, pos_col=1, task_col=2, pos='not none')
msa_pooled_scores = pool_span_scores(msa_span_scores, ip_len)
ensemble_span_scores = [score for scores in [span_scores, msa_span_scores] for score in scores]
ensemble_pooled_scores = pool_span_scores(ensemble_span_scores, ip_len)
ent_scores = extract_ent_scores(entity_model,ip,ensemble_pooled_scores, pos_col=1, task_col=2)
combined_sequences, ent_pred_tags = pool_ent_scores(ent_scores, ip_len)
return combined_sequences
if __name__ == '__main__':
space_key = os.environ.get('key')
filenames = ['network.py', 'layers.py', 'utils.py',
'representation.py', 'predict.py', 'validate.py']
for file in filenames:
hf_hub_download('nehalelkaref/stagedNER',
filename=file,
local_dir='src',
token=space_key)
from src.predict import extract_spannet_scores,extract_ent_scores,pool_span_scores,pool_ent_scores
from src.network import SpanNet, EntNet
from src.validate import entities_from_token_classes
from camel_tools.disambig.mle import MLEDisambiguator
from camel_tools.tagger.default import DefaultTagger
mled = MLEDisambiguator.pretrained()
tagger = DefaultTagger(mled, 'pos')
print(tagger.tag('ذهبت الى المدرسة'.split()))
span_path = 'models/span.model'
msa_span_path = 'models/msa.best.model'
entity_path= 'models/entity.msa.model'
span_model = SpanNet.load_model(span_path)
msa_span_model = SpanNet.load_model(msa_span_path)
entity_model = EntNet.load_model(entity_path)
# iface= gr.Base(primary_hue="green")
iface = gr.Interface(fn=predict_label, inputs="text", outputs="text")
iface.launch(show_api=False)
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