Add app file

app.py

@@ -0,0 +1,29 @@
+import streamlit as st
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+model_checkpoint = 'distilbert-base-uncased'
+
+
+config = PeftConfig.from_pretrained("SavirD/distilbert-base-uncased-lora-text-classification")
+base_model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
+model = PeftModel.from_pretrained(base_model, "SavirD/distilbert-base-uncased-lora-text-classification")
+model.to('mps')
+
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, add_prefix_space=True)
+if tokenizer.pad_token is None:
+    tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+    model.resize_token_embeddings(len(tokenizer))
+
+id2label = {0: "Negative", 1: "Positive"}
+
+st.markdown("## Enter Some Text")
+review = st.text_area("", height=150)
+
+inputs = tokenizer.encode(review, return_tensors="pt").to("mps") # moving to mps for Mac (can alternatively do 'cpu')
+logits = model(inputs).logits
+predictions = torch.max(logits,1).indices
+with st.chat_message("assistant"):
+    response = "The Following Text " + f'"{review}" is ' + id2label[predictions.tolist()[0]]
+    st.markdown(response)