app.py

import gradio as gr
import numpy as np
import os
from huggingface_hub import hf_hub_download
from camel_tools.data import CATALOGUE
from camel_tools.tagger.default import DefaultTagger
from camel_tools.disambig.bert import BERTUnfactoredDisambiguator

def predict_label(text):
    
    ip = text.split()
    ip_len = [len(ip)]
    
    span_scores = extract_spannet_scores(span_model,ip,ip_len)
    span_pooled_scores = pool_span_scores(span_scores, ip_len)

    pos_tags = tagger.tag(ip)
    msa_span_scores = extract_spannet_scores(msa_span_model,ip,ip_len,pos=pos_tags)
    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)
    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)
        
    # CATALOGUE.download_package("all",
    #                        recursive=True,
    #                        force=True,
    #                        print_status=True)
    
    # 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


    # diasmbig = BERTUnfactoredDisambiguator.pretrained('msa')
    # tagger = DefaultTagger(diasmbig, 'pos')
    
    # span_path = 'models/span.model'
    # msa_span_path = 'new_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")

    
    with gr.Blocks(theme='finlaymacklon/smooth_slate') as iface:
        example_input=gr.Textbox(label="Input Example", lines=1)
        
        gr.Interface(fn=predict_label, inputs="text", outputs="text",
                         theme="smooth_slate")
        gr.Examples(
      examples=["النشرة الإخبارية الصادرة عن الأونروا رقم 113 (1986/1/8)."],
      inputs= example_input)
    iface.launch(show_api=False)