Create app.py

app.py

@@ -0,0 +1,33 @@
+import gradio as grad
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+def load_prompter():
+  prompter_model = AutoModelForCausalLM.from_pretrained("microsoft/Promptist")
+  tokenizer = AutoTokenizer.from_pretrained("gpt2")
+  tokenizer.pad_token = tokenizer.eos_token
+  tokenizer.padding_side = "left"
+  return prompter_model, tokenizer
+
+prompter_model, prompter_tokenizer = load_prompter()
+
+def generate(plain_text):
+    input_ids = prompter_tokenizer(plain_text.strip()+" Rephrase:", return_tensors="pt").input_ids
+    eos_id = prompter_tokenizer.eos_token_id
+    outputs = prompter_model.generate(input_ids, do_sample=False, max_new_tokens=75, num_beams=8, num_return_sequences=8, eos_token_id=eos_id, pad_token_id=eos_id, length_penalty=-1.0)
+    output_texts = prompter_tokenizer.batch_decode(outputs, skip_special_tokens=True)
+    res = output_texts[0].replace(plain_text+" Rephrase:", "").strip()
+    print("[I] Prompter input: %s" % plain_text)
+    print("[I] Prompter output: %s \n------------\n" % res)
+    return res
+
+txt = grad.Textbox(lines=1, label="Initial Text", placeholder="Input Prompt")
+out = grad.Textbox(lines=1, label="Optimized Prompt")
+
+grad.Interface(fn=generate,
+               inputs=txt,
+               outputs=out,
+               title="Promptist",
+               allow_flagging='never',
+               cache_examples=False,
+               theme="default").launch(enable_queue=True, share=True, debug=True)