Upload app.py

app.py

@@ -1,4 +1,8 @@
 import gradio as gr
+import torch
+import os
+from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer
+from transformers import AutoTokenizer
 
 README = """
     # Movie Review Score Discriminator
@@ -9,19 +13,63 @@ README = """
 """
 
 
+model = "roberta-base"
+learning_rate = 5e-5
+batch_size_train = 64
+step = 1900
+
+file_name = "model-{}.pt".format(step)
+state_dict = torch.load(os.path.join(file_name))
+
+id2label = {0: "NEGATIVE", 1: "POSITIVE"}
+label2id = {"NEGATIVE": 0, "POSITIVE": 1}
+
+
 title = "Movie Review Score Discriminator"
 description = "It is a program that classifies whether it is positive or negative by entering movie reviews. You can choose between the Korean version and the English version."
 examples = ["the greatest musicians ", "cold movie "]
 
 
-def greet(name):
-    return "Hello " + name + "!"
 
-demo = gr.Interface.load("models/cardiffnlp/twitter-roberta-base-sentiment", inputs="text", outputs="text", 
+def tokenized_data(tokenizer, inputs):
+    return tokenizer.batch_encode_plus(
+            inputs,
+            return_tensors="pt",
+            padding="max_length",
+            max_length=64,
+            truncation=True)
+
+
+def greet(text):
+    tokenizer = AutoTokenizer.from_pretrained(model)
+    model = AutoModelForSequenceClassification.from_pretrained(
+        model, num_labels=2, id2label=id2label, label2id=label2id,
+        state_dict=state_dict
+    )
+    inputs = tokenized_data(tokenizer, text)
+
+
+    # 모델의 매개변수 Tensor를 mps Tensor로 변환
+    # model.to(device)
+    # evaluation mode or training mode
+    model.eval()
+
+    with torch.no_grad():
+        # logits.shape = torch.Size([ batch_size, 2 ])
+        logits = model(input_ids=inputs[0], attention_mask=inputs[1]).logits
+
+    return logits
+
+demo1 = gr.Interface.load("models/cardiffnlp/twitter-roberta-base-sentiment", inputs="text", outputs="text", 
                          title=title, theme="peach",
                          allow_flagging="auto",
                          description=description, examples=examples)
 # demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+
+demo2 = gr.Interface(fn=greet, inputs="text", outputs="text", 
+                         title=title, theme="peach",
+                         allow_flagging="auto",
+                         description=description, examples=examples)
     
 if __name__ == "__main__":
-    demo.launch()   
+    demo2.launch()