Update app.py

app.py

@@ -1,5 +1,5 @@
-# import gradio as gr
-# from transformers import pipeline
+import torch
+import gradio as gr
 import pandas as pd
 from datasets import Dataset
 from transformers import (
@@ -8,8 +8,7 @@ from transformers import (
     TrainingArguments,
     Trainer
 )
-import torch
-import gradio as gr
+
 
 # load dataset
 df = pd.read_csv("dataset.csv")
@@ -34,7 +33,6 @@ training_args = TrainingArguments(
     logging_steps=10,
     save_strategy="no",
     learning_rate=2e-5,
-    # evaluation_strategy="no",
 )
 
 # train
@@ -44,33 +42,20 @@ trainer = Trainer(
     train_dataset=tokenized_dataset,
     tokenizer=tokenizer,
 )
-
 trainer.train()
 
-
-# inference function for Gradio
+# inference function for gradio
 def classify(text):
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
     with torch.no_grad():
         outputs = model(**inputs)
     probs = torch.softmax(outputs.logits, dim=1).numpy()[0]
     return {
-        "ไม่ใช่การจ้างงานรถขนส่ง": float(probs[0]),
-        "เป็นการจ้างงานรถขนส่ง": float(probs[1]),
+        "No": float(probs[0]),
+        "Yes": float(probs[1]),
     }
 
-# classifier = pipeline(
-#     "zero-shot-classification",
-#     model="MoritzLaurer/mDeBERTa-v3-base-mnli-xnli"
-# )
-
-# def classify(text, labels):
-#     labels = [label.strip() for label in labels.split(",")]
-#     result = classifier(text, candidate_labels=labels)
-#     return {label: round(score, 4) for label, score in zip(result["labels"], result["scores"])}
-
-
-# Gradio interface
+# gradio interface
 demo = gr.Interface(
     fn=classify,
     inputs=gr.Textbox(lines=3, label="ข้อความ"),