import re
import torch
import gradio as gr

from presidio_anonymizer import AnonymizerEngine
from presidio_analyzer import AnalyzerEngine
from presidio_anonymizer.entities import RecognizerResult, OperatorConfig
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline


# Initialize the engine:
analyzer = AnalyzerEngine()
anonymizer = AnonymizerEngine()

# Create pipeline
tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER-uncased")
tokenizer.add_tokens('<person>')
model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER-uncased")
pipe = pipeline(model=model, tokenizer=tokenizer, task='ner')

# https://microsoft.github.io/presidio/supported_entities/
ENT_TYPES = [
#     'PERSON',
    'CREDIT_CARD',
    'EMAIL_ADDRESS',
    'IP_ADDRESS',
    'PHONE_NUMBER'
]

def mask_names_hf(text):
    # Tokenize inputs
    inputs = tokenizer(text, return_tensors='pt', truncation=True)
    tokens = inputs.tokens()
    
    # Make inferences
    outputs = model(**inputs).logits
    predictions = torch.argmax(outputs, dim=2)
    
    # Replace tokens that are people with <PERSON>
    words = []
    for token, prediction in zip(tokens, predictions[0].numpy()):
        prediction = model.config.id2label[prediction]
        if prediction not in ('I-PER', 'B-PER'):
            words.append(token)
        elif prediction == 'B-PER':
            if words[-1] != '<PERSON>':
                words.append('<PERSON>')
        else:
            pass
    # Convert those tokens to a string
    return tokenizer.convert_tokens_to_string(words[1:-1])

def anonymize(text, min_len=3):
    
    # Find and replace other stuff (Presidio NER)
    ents = analyzer.analyze(text, language='en', entities=ENT_TYPES)
    results = anonymizer.anonymize(text, analyzer_results=ents)
    t = results.text
    
    # Find and replace names (HF NER)
    t = mask_names_hf(t)
    
    pats = re.findall('<.+?>', t)
    for p in pats:
        t = t.replace(p, p.upper().replace(' ', ''))
        
    
    t = t.replace('<PERSON><PERSON>', '<PERSON>')
    return t

title = "Personal Info Remover"
description = """Personal Info Remover"""

gr.Interface(
        anonymize, 
        inputs='text', 
        outputs='text',
        title=title,
        description=description,
        examples=["My name is Yuriy, contacts info: 0-800-123-456, test@i.ua, IP address is 1.0.0.1"]
).launch(debug=True)