Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+from peft import PeftModel, PeftConfig
+
+base_model = "cardiffnlp/twitter-roberta-base-sentiment-latest"
+adapter_model = 'saideep-arikontham/twitter-roberta-base-sentiment-latest-biden-stance'
+
+# define label maps
+id2label = {0: "Anti-Biden", 1 : "Pro-Biden"}
+label2id = {"Anti-Biden" : 0, "Pro-Biden" : 1}
+
+# generate classification model from model_checkpoint
+model = AutoModelForSequenceClassification.from_pretrained(base_model, num_labels=2, id2label = id2label, label2id = label2id, ignore_mismatched_sizes=True)
+
+model = PeftModel.from_pretrained(model, adapter_model)
+tokenizer = AutoTokenizer.from_pretrained(adapter_model)
+
+def greet(text):
+    
+    model.to('cpu')
+    inputs = tokenizer.encode(text, return_tensors="pt").to("cpu")
+    # compute logits
+    logits = model(inputs).logits
+    # convert logits to label
+    predictions = torch.argmax(logits)
+
+    return "This text is " + id2label[predictions.tolist()] + "!!"