Create app.py

app.py

@@ -0,0 +1,29 @@
+import numpy as np
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+labels = ['Not_Adult', 'Adult']
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+device
+
+model_name = 'valurank/finetuned-distilbert-adult-content-detection'
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+def get_adult_content(text):
+  input_tensor = tokenizer.encode(text, return_tensors='pt', truncation=True)
+  logits = model(input_tensor).logits
+
+  softmax = torch.nn.Softmax(dim=1)
+  probs = softmax(logits)[0]
+  probs = probs.cpu().detach().numpy()
+  max_index = np.argmax(probs)
+  adult_content = labels[max_index]
+  return adult_content
+  
+demo = gr.Interface(get_adult_content, inputs = gr.inputs.Textbox(label= "Input your text here"),
+                    outputs = gr.outputs.Textbox(label='Category'))
+               
+if __name__ == "__main__":
+demo.launch(debug=True)