Upload app.py

app.py

@@ -0,0 +1,45 @@
+import string
+import gradio as gr
+import requests
+import torch
+from transformers import (
+    AutoConfig,
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+)
+
+model_dir = "my-bert-model"
+
+config = AutoConfig.from_pretrained(model_dir, num_labels=3, finetuning_task="text-classification")
+tokenizer = AutoTokenizer.from_pretrained(model_dir)
+model = AutoModelForSequenceClassification.from_pretrained(model_dir, config=config)
+
+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)