Add app and requirements

app.py

@@ -0,0 +1,266 @@
+import gradio as gr
+import torch
+import json
+import pandas as pd
+import numpy as np
+from transformers import AutoTokenizer, RobertaForTokenClassification
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from json import JSONEncoder
+from faker import Faker
+
+class out_json():
+    def __init__(self, w,l):
+        self.word = w
+        self.label = l
+class MyEncoder(JSONEncoder):
+        def default(self, o):
+            return o.__dict__
+class Model:
+   def __init__(self):
+        self.texto=""
+        self.idioma=""
+        self.modelo_ner=""
+        self.categoria_texto=""
+        
+   def identificacion_idioma(self,text): 
+        self.texto=text
+        tokenizer = AutoTokenizer.from_pretrained("papluca/xlm-roberta-base-language-detection")
+        model = AutoModelForSequenceClassification.from_pretrained("papluca/xlm-roberta-base-language-detection")
+        
+        inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
+        
+        with torch.no_grad():
+            logits = model(**inputs).logits
+        
+        preds = torch.softmax(logits, dim=-1)
+        
+       
+        id2lang = model.config.id2label
+        vals, idxs = torch.max(preds, dim=1)
+        
+       
+        
+        #retorna el idioma con mayor porcentaje
+        maximo=vals.max()
+        idioma=''
+        porcentaje=0
+        for k, v in zip(idxs, vals):
+           if v.item()==maximo:
+             idioma,porcentaje=id2lang[k.item()],v.item()
+             
+           
+        if idioma=='es':
+             self.idioma="es"
+             self.modelo_ner='BSC-LT/roberta_model_for_anonimization'
+             self.faker_ = Faker('es_MX')
+             self.model = RobertaForTokenClassification.from_pretrained(self.modelo_ner)     
+        else:
+             self.idioma="en"
+             self.faker_ = Faker('en_US')
+             self.modelo_ner="FacebookAI/xlm-roberta-large-finetuned-conll03-english"     
+             self.model = AutoModelForTokenClassification.from_pretrained(self.modelo_ner)   
+        self.categorizar_texto(self.texto)
+   def reordenacion_tokens(self,tokens):
+        
+        i=0
+        new_tokens=[]
+        ig_tokens=[] #ignorar estos indices del array de indentificadores
+        for token in tokens:
+            ind=len(new_tokens)
+            if i<len(tokens):
+                 if  token.startswith("▁"):
+                    
+                    new_tokens.append(token)
+                    
+                    i=i+1
+                 else:
+                    new_tokens[ind-1] = (new_tokens[ind-1] + token)
+                    ig_tokens.append(i)
+                    
+                    i=i+1
+        return (
+                new_tokens,
+                ig_tokens
+                )  
+        
+   def reordenacion_identificadores(self,ig_tokens,predicted_tokens_classes):
+        x=0
+        new_identificadores=[]
+        for token in predicted_tokens_classes:
+            
+            if x not in ig_tokens:
+                new_identificadores.append(token)
+                x=x+1
+            else:
+                 x=x+1
+        return new_identificadores
+   def salida_json(self,tokens,pre_tokens):
+       list=[]
+       i=0
+       for t in tokens:
+           if pre_tokens[i]!='O':
+              a = out_json(t.replace('▁','').replace('Ġ',''),pre_tokens[i].replace('▁',''))
+              list.append(a)
+           i=i+1
+       return MyEncoder().encode(list)   
+   def salida_texto( self,tokens,pre_tokens):
+        new_labels = []
+        current_word = None
+        i=0
+        for token in tokens:
+               
+            if pre_tokens[i]=='O' or 'MISC' in pre_tokens[i]:
+              new_labels.append(' ' +token.replace('▁',''))
+            else:
+               new_labels.append(' ' + pre_tokens[i])
+            i=i+1 
+        a=''    
+        for i in new_labels:
+            a = a+i
+        return a    
+        #return new_labels  
+   def salida_texto_anonimizado(self, ids,pre_tokens):
+    new_labels = []
+    current_word = None
+    i=0
+    for identificador in pre_tokens:
+       
+        if identificador=='O' or 'OTH' in identificador:
+            new_labels.append(self.tokenizer.decode(ids[i]))
+        else:
+            new_labels.append(' ' + identificador)
+        i=i+1
+    a=''
+    for i in new_labels:
+        a = a+i
+    return a    
+   def formato_salida(self,out):
+       a=""
+       for i in out:
+          a = a + i.replace('▁','').replace(' ','') + ' '
+       return  a  
+   def fake_pers(self):
+       return self.faker_.name(self)
+   def fake_word(self):
+       return self.faker_.word()
+   def fake_first_name(self):
+       return self.faker_.first_name()       
+   def fake_last_name(self):
+       return self.faker_.last_name()    
+   def fake_address(self):
+       return self.faker_.address()
+   def fake_sentence(self,n):
+       return self.faker_.sentence(nb_words=n)
+   def fake_text(self):
+       return self.faker_.text()
+   def fake_company(self):
+       return self.faker_.company() 
+   def fake_city(self):
+       return self.faker_.city()     
+   def reemplazo_fake(self,identificadores):
+       new_iden=[]
+       for id in identificadores:
+           
+           if 'PER' in id:
+               new_iden.append(self.fake_first_name())
+           
+           elif 'ORG' in id:
+               new_iden.append(self.fake_company())
+           
+           elif 'LOC' in id:
+               new_iden.append(self.fake_city())
+           else:
+               new_iden.append(id)
+       return new_iden
+   def categorizar_texto(self,texto):
+        name="elozano/bert-base-cased-news-category"       
+        tokenizer = AutoTokenizer.from_pretrained(name)
+        model_ = AutoModelForSequenceClassification.from_pretrained(name)
+        
+        inputs_ = tokenizer(texto, padding=True, truncation=True, return_tensors="pt")
+        
+        with torch.no_grad():
+            logits = model_(**inputs_).logits
+        
+        preds = torch.softmax(logits, dim=-1)
+        
+       
+        id2lang = model_.config.id2label
+        vals, idxs = torch.max(preds, dim=1)
+        
+        #retorna el idioma con mayor porcentaje
+        maximo=vals.max()
+        cat=''
+        self.categoria_texto=''
+        porcentaje=0
+        for k, v in zip(idxs, vals):
+           if v.item()==maximo:
+             cat,porcentaje=id2lang[k.item()],v.item()
+             self.categoria_texto=cat  
+             
+        
+        return  cat, porcentaje      
+   def predict(self):
+        
+        categoria, porcentaje = self.categorizar_texto(self.texto)
+        print(categoria, porcentaje)
+       
+        self.tokenizer  = AutoTokenizer.from_pretrained(self.modelo_ner)
+        tokens = self.tokenizer.tokenize(self.texto)
+       
+        ids = self.tokenizer.convert_tokens_to_ids(tokens)
+        
+        input_ids = torch.tensor([ids])
+        with torch.no_grad():
+           logits = self.model(input_ids).logits
+        
+        predicted_token_class_ids = logits.argmax(-1)
+        
+        predicted_tokens_classes = [self.model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]
+        
+        labels = predicted_token_class_ids
+        loss = self.model(input_ids, labels=labels).loss
+      
+        if (self.idioma=='es'):
+            
+           
+            out1 = self.salida_json(tokens,predicted_tokens_classes) #spanish solo palabras sensibles
+                        
+            out2 = self.salida_texto_anonimizado(ids,self.reemplazo_fake(predicted_tokens_classes)) #español texto completo    
+            
+        else:
+            
+            new_tokens,ig_tokens=self.reordenacion_tokens(tokens)
+            new_identificadores = self.reordenacion_identificadores(ig_tokens,predicted_tokens_classes)
+           
+            out1 = self.salida_json(new_tokens,new_identificadores),
+           
+            
+            
+            out2 = self.salida_texto(new_tokens,self.reemplazo_fake(new_identificadores))
+            
+           
+        return (
+          
+            self.texto,
+            out1,
+            str(out2)
+           
+            
+        )  
+model = Model()
+def get_model():
+        return model
+
+def procesar(texto,archivo):
+    print(texto)
+    print(archivo.name)
+    df=pd.read_csv(archivo.name,delimiter=",")
+    print(df["nombre"])
+    model.identificacion_idioma(texto)
+    
+    return model.predict()
+
+demo = gr.Interface(fn=procesar,inputs=[gr.inputs.Textbox(),gr.inputs.File()] , outputs="text")
+demo.launch(share=True)

requirements.txt

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+transformers
+torch
+Faker