Delete app.py

app.py

@@ -1,129 +0,0 @@
-import gradio as gr
-
-from app_data import examples
-
-from app_details import title, description, article
-
-from transformers import AutoModelForTokenClassification,AutoModelForSequenceClassification, AutoTokenizer, pipeline
-
-from sentence_transformers import SentenceTransformer, util
-
-def get_entities(example):
-    model_name = "hackathon-pln-es/jurisbert-finetuning-ner"
-    tokenizer = AutoTokenizer.from_pretrained(model_name,       add_prefix_space=True)
-  
-    model = AutoModelForTokenClassification.from_pretrained(model_name)
-    token_classifier = pipeline("token-classification", aggregation_strategy="simple", model=model, tokenizer=tokenizer)
-    results = token_classifier(example.lower())
-    
-    output = []
-
-    i=0
-    item = None
-    prev_item = None
-    next_item = None
-    while i < (len(results)):
-        item = results[i]
-        p=i-1
-        n=i+1
-        
-        if p > 0:
-            prev_item = results[p]
-        
-        
-        if n<(len(results)):
-            next_item = results[n]
-        
-        
-        if (i==0):
-            if item["start"]>0:
-                output.extend([(example[0:item["start"]], None)])
-        output.extend([(example[item["start"]:item["end"]], item["entity_group"])])
-        if (next_item!=None):
-            ##verificar el tramo entre actual y siguiente
-            if(item["end"]!=next_item["start"]):
-                output.extend([(example[item["end"]:next_item["start"]], None)])
-        i=i+1
-
-    if (item!=None): 
-      if (item["end"] < len(example)):
-        output.extend([(example[item["end"]:len(example)], None)])
-    
-    return output
-
-def clasifica_sistema_universal(example):
-    tokenizer = AutoTokenizer.from_pretrained("hackathon-pln-es/jurisbert-class-tratados-internacionales-sistema-universal")
-
-    model = AutoModelForSequenceClassification.from_pretrained("hackathon-pln-es/jurisbert-class-tratados-internacionales-sistema-universal")
-    text_classifier = pipeline("text-classification",  model=model, tokenizer=tokenizer)
-    results= text_classifier (example)
-
-    salida=[]
-    for i in results:
-      salida.append({i["label"]:i["score"]})
-    
-    #return results[0]["label"], round(results[0]["score"], 5)
-  
-
-    return {i["label"]: float(i["score"]) for i in results}
-
-def clasifica_conv_americana(example):
-  tokenizer = AutoTokenizer.from_pretrained("hackathon-pln-es/jurisbert-clas-art-convencion-americana-dh")
-
-  model = AutoModelForSequenceClassification.from_pretrained("hackathon-pln-es/jurisbert-clas-art-convencion-americana-dh")
-  
-  text_classifier = pipeline("text-classification",  model=model, tokenizer=tokenizer)
-  results= text_classifier (example)
-  
-  return {i["label"]: float(i["score"]) for i in results}
-  
-def similitud(example,example2):
-    model = SentenceTransformer("hackathon-pln-es/jurisbert-tsdae-sentence-transformer")
-    #Compute embedding for both lists
-    embeddings1 = model.encode(example, convert_to_tensor=True)
-    embeddings2 = model.encode(example2, convert_to_tensor=True)
-    
-    #Compute cosine-similarits
-    cosine_scores = util.cos_sim(embeddings1, embeddings2)
-    
-    return float(cosine_scores[0])*100
-
-  
-def process(example,example2):
-    entidades = get_entities(example)
-
-    class_sistema_universal = clasifica_sistema_universal(example)
-
-    class_conv_americana = clasifica_conv_americana(example)
-    
-    score_similitud = similitud(example,example2)
-    
-    entidades2 = get_entities(example2)
-
-    class_sistema_universal2 = clasifica_sistema_universal(example2)
-
-    class_conv_americana2 = clasifica_conv_americana(example2)
-    return entidades,class_sistema_universal, class_conv_americana, score_similitud , entidades2 ,class_sistema_universal2, class_conv_americana2
-
-input_sen = gr.inputs.Textbox(lines=10, label="Texto a analizar:")
-
-input_sen2 = gr.inputs.Textbox(lines=10, label="Texto a comparar:")
-
-#### Resultados texto analizar:
-output_hgtxt= gr.outputs.HighlightedText(label="Reconocimiento de entidades:")
-output_lbl1= gr.outputs.Label(label="Clasificación modelo sistema universal:")
-output_lbl2= gr.outputs.Label(label="Clasificación modelo convención americana:")
-
-#### Resultados de la similitud
-output_txt= gr.outputs.Textbox(label="Porcentaje de similitud entre los textos proporcionados:")
-
-#### Resultados texto a comparar:
-output_hgtxt2= gr.outputs.HighlightedText(label="Reconocimiento de entidades:")
-output_lbl3= gr.outputs.Label(label="Clasificación modelo sistema universal:")
-output_lbl4= gr.outputs.Label(label="Clasificación modelo convención americana:")
-
-#iface = gr.Interface(fn=process, inputs=input_sen, outputs=["highlight","label","label"], examples=examples, title=title, description = description)
-
-iface = gr.Interface(fn=process, inputs=[input_sen, input_sen2], outputs=[output_hgtxt,output_lbl2,output_lbl2,output_txt,output_hgtxt2,output_lbl3,output_lbl4], examples=examples, title=title, description = description, article=article)
-
-iface.launch()