initial commit

app.py

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+import gradio as gr
+import torch.cuda
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+model = AutoModelForCausalLM.from_pretrained("salt/RandomPrompt-v1")
+
+model.to(device)
+
+tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M")
+tokenizer.pad_token = tokenizer.eos_token
+
+
+def detect(text_in, max_length):
+    if not text_in:
+        inputs = tokenizer.pad_token
+    else:
+        inputs = text_in
+    text = tokenizer.batch_decode(model.generate(tokenizer.encode(inputs,
+                                                                  return_tensors='pt').to(device),
+                                                 do_sample=True,
+                                                 temperature=0.9,
+                                                 max_length=max_length))[0]
+    text = text.replace(tokenizer.pad_token, '')
+    return text
+
+
+iface = gr.Interface(fn=detect, inputs=[gr.Textbox(), gr.Slider(50, 200, default=120)], outputs=gr.TextArea())
+iface.launch()

requirements.txt

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+--extra-index-url https://download.pytorch.org/whl/cu113
+torch
+transformers