Create app.py

app.py

@@ -0,0 +1,49 @@
+import pip
+pip.main(['install', 'torch'])
+pip.main(['install', 'transformers'])
+
+import torch
+import torch.nn as nn
+import gradio as gr
+import transformers
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+def load_model(model_name):
+    # model_name = "Unggi/hate_speech_bert"
+    # model
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    # tokenizer..
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+    return model, tokenizer
+
+
+def inference(prompt):
+    model_name = "Unggi/ko_hate_speech_KcELECTRA" #"Unggi/hate_speech_bert"
+    
+    model, tokenizer = load_model(
+        model_name = model_name
+    )
+
+    inputs = tokenizer(
+        prompt, 
+        return_tensors="pt"
+        )
+
+    with torch.no_grad():
+        logits = model(**inputs).logits
+
+    # for binary classification
+    sigmoid = nn.Sigmoid()
+    bi_prob = sigmoid(logits)
+
+    predicted_class_id = bi_prob.argmax().item()
+    class_id = model.config.id2label[predicted_class_id]
+
+    return "class_id: " + str(class_id) + "\n" + "clean_prob: " + str(bi_prob[0][0].item()) + "\n" + "unclean_prob: " + str(bi_prob[0][1].item())
+
+demo = gr.Interface(
+    fn=inference, 
+    inputs="text", 
+    outputs="text", #return 값
+    ).launch()