Update app.py

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 import sentence_transformers
 from sentence_transformers import SentenceTransformer
+from sentence_transformers.util import semantic_search
 
 import pandas as pd
 
@@ -8,12 +9,24 @@ model = SentenceTransformer('JoBeer/all-mpnet-base-v2-eclass')
 
 corpus = pd.read_excel('corpus.xlsx')
 
-def predict(i):
-    name = corpus[i][5]
-    return name
+def predict(name, description):
+    text = 'Description: '+ description + '; Name: ' + name 
+    query_embedding = model.encode(text, convert_to_tensor=True)
+    
+    corpus_embeddings = torch.FloatTensor(corpus["embeddings"])
+
+    output = sentence_transformers.util.semantic_search(query_embedding, corpus_embeddings, top_k = 5)
+
+    preferedName1 = corpus.iloc[output[0][0].get('corpus_id'),2]
+    definition1 = corpus.iloc[output[0][0].get('corpus_id'),1]
+    IRDI1 = corpus.iloc[output[0][0].get('corpus_id'),4]
+    score1 = output[0][0].get('score')
+    
+
+    return preferedName1, definition1, IRDI1, score1
 
-interface = gr.Interface(fn = predict, inputs = gr.Textbox(label="did:", placeholder="z.B. query", lines=1), 
-            outputs = 'text',
+interface = gr.Interface(fn = predict, inputs = [gr.Textbox(label="Description:", placeholder="z.B. Globel Trade Item Number", lines=1), gr.Textbox(label="Name:", placeholder="z.B. GTIN", lines=1)], 
+            outputs = [gr.Textbox(label = 'preferedName'),gr.Textbox(label = 'definition'), gr.Textbox(label = 'IDRI'),gr.Textbox(label = 'score')]
             title = 'ECLASS-Property-Search')
     
 interface.launch()