app.py

import gradio as gr

#def greet(name):
 #   return "Hello " + name + "!"

#iface = gr.Interface(fn=greet, inputs="text", outputs="text")
#iface.launch()

# Use a pipeline as a high-level helper 
from transformers import pipeline

pipe = pipeline("text-generation", model="gpt2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForCausalLM.from_pretrained("gpt2")

#get the text
text_input = "One upon a time there was a tree"
max_length = 100
temperature = 0.8
top_k = 100

input_ids = tokenizer.encode(text_input,return_tensors='pt')

output = model.generate(input_ids, max_length=max_length, temperature=temperature, top_k=top_k, do_sample = True)


response = tokenizer.decode(output[0], skip_special_token=True)
print(response)