updateing app.py with sts lora

app.py

@@ -1,15 +1,21 @@
 import gradio as gr
-from transformers import pipeline, AutoTokenizer
+from transformers import pipeline, AutoTokenizer, AutoModel
 from peft.auto import AutoPeftModelForSequenceClassification
 from tensorboard.backend.event_processing import event_accumulator
+from peft import PeftModel
 import plotly.express as px
 import pandas as pd
 
-tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
 tokenizer1 = AutoTokenizer.from_pretrained("albert-base-v2")
-tokenizer2 = AutoTokenizer.from_pretrained("microsoft/deberta-v3-xsmall")
+
 loraModel = AutoPeftModelForSequenceClassification.from_pretrained("Intradiction/text_classification_WithLORA")
-#loraModel1 = AutoPeftModelForSequenceClassification.from_pretrained("rajevan123/STS-Lora-Fine-Tuning-Capstone-Deberta-small")
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+
+tokenizer2 = AutoTokenizer.from_pretrained("microsoft/deberta-v3-xsmall")
+base_model = AutoModel.from_pretrained("microsoft/deberta-v3-xsmall")
+peft_model_id = "rajevan123/STS-Lora-Fine-Tuning-Capstone-Deberta-small"
+model = PeftModel.from_pretrained(base_model, peft_model_id)
+#merged_model = model.merge_and_unload()
 
 
 # Handle calls to DistilBERT------------------------------------------
@@ -46,16 +52,16 @@ def AlbertUntrained_fn(text1, text2):
 
 # Handle calls to Deberta--------------------------------------------
 DebertaUntrained_pipe = pipeline("text-classification", model="microsoft/deberta-v3-xsmall")
-DebertanoLORA_pipe = pipeline(model="rajevan123/STS-Conventional-Fine-Tuning")
-#DebertawithLORA_pipe = pipeline("text-classification", model=loraModel1, tokenizer=tokenizer2)
+DebertanoLORA_pipe = pipeline("text-classification", model="rajevan123/STS-Conventional-Fine-Tuning")
+DebertawithLORA_pipe = pipeline( model=model, tokenizer=tokenizer2)
 
 #STS models
 def DebertanoLORA_fn(text1, text2):
     return DebertanoLORA_pipe({'text': text1, 'text_pair': text2})
 
 def DebertawithLORA_fn(text1, text2):
-    #return DebertawithLORA_pipe({'text': text1, 'text_pair': text2})
-    return ("working2")
+    return DebertawithLORA_pipe({'text': text1, 'text_pair': text2})
+    #return ("working2")
 
 def DebertaUntrained_fn(text1, text2):
     return DebertaUntrained_pipe({'text': text1, 'text_pair': text2})