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app 2.py

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+import gradio as gr
+import transformers
+from transformers import BertTokenizer, DataCollatorWithPadding
+from transformers import AutoModelForSequenceClassification
+tokenizer = BertTokenizer.from_pretrained('huolongguo10/check_sec')
+model = AutoModelForSequenceClassification.from_pretrained('huolongguo10/check_sec', num_labels=2)
+import torch
+def check(text):
+    inputs = tokenizer(text, return_tensors="pt")
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    predicted_class_id = logits.argmax().item()
+    print(f'{logits.argmax().item()}:{text}')
+    return 'secure' if predicted_class_id==0 else 'insecure'
+with gr.Blocks() as demo:
+    text = gr.Textbox(label="Text")
+    output = gr.Textbox(label="Output Box")
+    # org = gr.Textbox(label="By normal check")
+    greet_btn = gr.Button("Check!")
+    greet_btn.click(fn=check, inputs=text, outputs=output, api_name="check")
+    gr.Markdown('''# check_sec
+    检查web参数安全性，目前只支持xss
+    ## 类型
+    ```
+    LABEL_0: secure
+    LABEL_1: insecure(可能包含xss payload)
+    ```
+    ''')
+# gr.Interface.load("models/huolongguo10/check_sec").launch()
+demo.launch()

requirements 2.txt

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+torch
+transformers