Upload app.py

app.py

@@ -0,0 +1,129 @@
+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()