Update app.py

app.py

@@ -1,48 +1,48 @@
-import string
-import gradio as gr
-import requests
-import torch
-from transformers import (
-    AutoConfig,
-    AutoModelForSequenceClassification,
-    AutoTokenizer,
-)
-
-custom_labels = {0: "neg", 1: "pos"}
-model_dir = r'model\sst-2-english'
-# model = pipeline("sentiment-analysis",model=model_dir,device=0)
-# print(model("you are bad boy."))
-config = AutoConfig.from_pretrained(model_dir, num_labels=2, finetuning_task="text-classification")
-tokenizer = AutoTokenizer.from_pretrained(model_dir)
-model = AutoModelForSequenceClassification.from_pretrained(model_dir, config=config)
-model.config.id2label = custom_labels
-model.config.label2id = {v: k for k, v in custom_labels.items()}
-def inference(input_text):
-    inputs = tokenizer.batch_encode_plus(
-                [input_text],
-                max_length=512,
-                pad_to_max_length=True,
-                truncation=True,
-                padding="max_length",
-                return_tensors="pt",
-            )
-    
-    with torch.no_grad():
-        logits = model(**inputs).logits
-    
-    predicted_class_id = logits.argmax().item()
-    output = model.config.id2label[predicted_class_id]
-    return output
-
-demo = gr.Interface(
-    fn=inference,
-    inputs=gr.Textbox(label="Input Text", scale=2, container=False),
-    outputs=gr.Textbox(label="Output Label"),
-    examples = [
-        ["My last two weather pics from the storm on August 2nd. People packed up real fast after the temp dropped and winds picked up.", 1],
-        ["Lying Clinton sinking! Donald Trump singing: Let's Make America Great Again!", 0],
-        ],
-    title="Tutorial: BERT-based Text Classificatioin",
-    )
-
+import string
+import gradio as gr
+import requests
+import torch
+from transformers import (
+    AutoConfig,
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+)
+
+custom_labels = {0: "neg", 1: "pos"}
+model_dir = r'model/sst-2-english'
+# model = pipeline("sentiment-analysis",model=model_dir,device=0)
+# print(model("you are bad boy."))
+config = AutoConfig.from_pretrained(model_dir, num_labels=2, finetuning_task="text-classification")
+tokenizer = AutoTokenizer.from_pretrained(model_dir)
+model = AutoModelForSequenceClassification.from_pretrained(model_dir, config=config)
+model.config.id2label = custom_labels
+model.config.label2id = {v: k for k, v in custom_labels.items()}
+def inference(input_text):
+    inputs = tokenizer.batch_encode_plus(
+                [input_text],
+                max_length=512,
+                pad_to_max_length=True,
+                truncation=True,
+                padding="max_length",
+                return_tensors="pt",
+            )
+    
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    
+    predicted_class_id = logits.argmax().item()
+    output = model.config.id2label[predicted_class_id]
+    return output
+
+demo = gr.Interface(
+    fn=inference,
+    inputs=gr.Textbox(label="Input Text", scale=2, container=False),
+    outputs=gr.Textbox(label="Output Label"),
+    examples = [
+        ["My last two weather pics from the storm on August 2nd. People packed up real fast after the temp dropped and winds picked up.", 1],
+        ["Lying Clinton sinking! Donald Trump singing: Let's Make America Great Again!", 0],
+        ],
+    title="Tutorial: BERT-based Text Classificatioin",
+    )
+
 demo.launch(debug=True)