Update app.py

app.py

@@ -7,7 +7,6 @@ import pandas as pd
 
 model = SentenceTransformer('gart-labor/eng-distilBERT-se-eclass')
 
-
 corpus = pd.read_json('corpus.jsonl', lines = True, encoding = 'utf-8')
 
 def predict(name, description):
@@ -23,34 +22,12 @@ def predict(name, description):
     IRDI1 = corpus.iloc[output[0][0].get('corpus_id'),4]
     score1 = output[0][0].get('score')
 
-    preferedName2 = corpus.iloc[output[0][1].get('corpus_id'),2]
-    definition2 = corpus.iloc[output[0][1].get('corpus_id'),1]
-    IRDI2 = corpus.iloc[output[0][1].get('corpus_id'),4]
-    score2 = output[0][1].get('score')
-
-    preferedName3 = corpus.iloc[output[0][2].get('corpus_id'),2]
-    definition3 = corpus.iloc[output[0][2].get('corpus_id'),1]
-    IRDI3 = corpus.iloc[output[0][2].get('corpus_id'),4]
-    score3 = output[0][2].get('score')
-
-    preferedName4 = corpus.iloc[output[0][3].get('corpus_id'),2]
-    definition4 = corpus.iloc[output[0][3].get('corpus_id'),1]
-    IRDI4 = corpus.iloc[output[0][3].get('corpus_id'),4]
-    score4 = output[0][3].get('score')
-        
-    preferedName5 = corpus.iloc[output[0][4].get('corpus_id'),2]
-    definition5 = corpus.iloc[output[0][4].get('corpus_id'),1]
-    IRDI5 = corpus.iloc[output[0][4].get('corpus_id'),4]
-    score5 = output[0][4].get('score')
-
-    df = [[preferedName1, IRDI1, score1], [preferedName2, IRDI2, score2],[preferedName3, IRDI3, score3],[preferedName4, IRDI4, score4], [preferedName5, IRDI5, score5]]
-
-    return pd.DataFrame(df)
+    return preferedName1, definition1, IRDI1, score1
 
 interface = gr.Interface(fn = predict, 
             inputs = [gr.Textbox(label="Name:", placeholder="Name of the Pump Property", lines=1), gr.Textbox(label="Description:", placeholder="Description of the Pump Property", lines=1)], 
-            #outputs = [gr.Textbox(label = 'preferedName'),gr.Textbox(label = 'definition'), gr.Textbox(label = 'IDRI'),gr.Textbox(label = 'score')],
-            outputs = [gr.Dataframe(row_count = (5, "fixed"), col_count=(3, "fixed"), label="Predictions", headers=['ECLASS preferedName', 'ECLASS IRDI', 'simularity score'])],
+            outputs = [gr.Textbox(label = 'preferedName'),gr.Textbox(label = 'definition'), gr.Textbox(label = 'IDRI'),gr.Textbox(label = 'score')],
+            #outputs = [gr.Dataframe(row_count = (5, "fixed"), col_count=(3, "fixed"), label="Predictions", headers=['ECLASS preferedName', 'ECLASS IRDI', 'simularity score'])],
             examples = [['Device type', 'describing a set of common specific characteristics in products or goods'], ['Item type','the type of product, an item can be assigned to'], 
                         ['Nominal power','power being consumed by or dissipated within an electric component as a variable'], ['Power consumption', 'power that is typically taken from the auxiliary power supply when the device is operating normally']],
                          #theme = 'huggingface',