Update app.py

app.py

@@ -2,28 +2,15 @@ import streamlit as st
 import layer
 from transformers import AutoModelWithLMHead, AutoTokenizer
 
-tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
-model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
-
-def get_sql(query):
-  input_text = "translate English to SQL: %s </s>" % query
-  features = tokenizer([input_text], return_tensors='pt')
-
-  output = model.generate(input_ids=features['input_ids'], 
-               attention_mask=features['attention_mask'])
-  
-  return tokenizer.decode(output[0])
-
-# model = layer.get_model('layer/t5-fine-tuning-with-layer/models/t5-english-to-sql').get_train()
-# tokenizer = layer.get_model('layer/t5-fine-tuning-with-layer/models/t5-tokenizer').get_train()
-
-# def convert(query):
-#     inputs = tokenizer.encode(f"translate English to SQL: {query}", return_tensors="pt")
-#     outputs = model.generate(inputs, max_length=1024)
-#     sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
-#     return sql
+model = layer.get_model('layer/t5-fine-tuning-with-layer/models/t5-english-to-sql').get_train()
+tokenizer = layer.get_model('layer/t5-fine-tuning-with-layer/models/t5-tokenizer').get_train()
 
+def convert(query):
+   inputs = tokenizer.encode(f"translate English to SQL: {query}", return_tensors="pt")
+   outputs = model.generate(inputs, max_length=1024)
+   sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
+   return sql
 
 query = st.text_input("Enter Text here", value="")
-output = get_sql(query)
+output = convert(query)
 st.text_area(label="Output Sql Query:", value=output, height=100)