Update app.py

app.py

@@ -2,6 +2,8 @@ import gradio as gr
 
 from transformers import AutoModelForTokenClassification,AutoModelForSequenceClassification, AutoTokenizer, pipeline
 
+from sentence_transformers import SentenceTransformer, util
+
 
 title = "Modelo Jurídico Mexicano"
 description = """
@@ -100,22 +102,39 @@ def clasifica_conv_americana(example):
   
   return {i["label"]: float(i["score"]) for i in results}
   
+def similitud(example,example2):
+    #Compute embedding for both lists
+    embeddings1 = model.encode(sentences1, convert_to_tensor=True)
+    embeddings2 = model.encode(sentences2, convert_to_tensor=True)
+    
+    #Compute cosine-similarits
+    cosine_scores = util.cos_sim(embeddings1, embeddings2)
+    
+    return float(cosine_scores[0])*100
+
+  
 def process(example):
     entidades = get_entities(example)
 
     class_sistema_universal = clasifica_sistema_universal(example)
 
     class_conv_americana = clasifica_conv_americana(example)
+    
+    score_similitud = similitud(example,example2)
 
-    return entidades,class_sistema_universal, class_conv_americana
+    return entidades,class_sistema_universal, class_conv_americana, score_similitud 
 
 input_sen = gr.inputs.Textbox(lines=10, label="Proporcione el texto a analizar:")
 
+input_sen2 = gr.inputs.Textbox(lines=10, label="Proporcione el texto a comparar:")
+
 output_lbl1= gr.outputs.Label(label="Clasificación modelo convención americana:")
 output_lbl2= gr.outputs.Label(label="Clasificación modelo sistema universal:")
 
+output_txt= gr.outputs.Textbox(label="Porcentaje de similitud:")
+
 #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, outputs=["highlight",output_lbl2,output_lbl2], examples=examples, title=title, description = description)
+iface = gr.Interface(fn=process, inputs=[input_sen, input_sen2], outputs=["highlight",output_lbl2,output_lbl2,output_txt], examples=examples, title=title, description = description)
 
 iface.launch(debug=True)