require

app.py

@@ -1,25 +1,11 @@
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM
-
+from transformers import pipeline, set_seed
 def vicuna(input):
-    tokenizer = AutoTokenizer.from_pretrained("gpt2")
-    model = AutoModelForCausalLM.from_pretrained("gpt2")
-    model.half().cuda()
 
-    # prompt = """\
-    # ### Human: Write a Python script for text classification using Transformers and PyTorch
-    # ### Assistant:\
-    # """
-    prompt=input
-    inputs = tokenizer(prompt, return_tensors='pt').to('cuda')
-    tokens = model.generate(
-    **inputs,
-    max_new_tokens=256,
-    do_sample=True,
-    temperature=1.0,
-    top_p=1.0,
-    )
-    print(tokenizer.decode(tokens[0], skip_special_tokens=True))
+    generator = pipeline('text-generation', model='gpt2')
+    set_seed(42)
+    generator(input, max_length=30, num_return_sequences=5)