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0f856dc b209ccf 0f856dc b209ccf 0f856dc a3c6f61 0f856dc b209ccf a3c6f61 b209ccf 0f856dc b209ccf 0f856dc b209ccf a3c6f61 b209ccf a3c6f61 b209ccf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | import gradio as gr
from transformers import GPT2LMHeadModel, GPT2Tokenizer
MODEL_NAME = "gpt2"
tokenizer = GPT2Tokenizer.from_pretrained(MODEL_NAME)
model = GPT2LMHeadModel.from_pretrained(MODEL_NAME)
def generate_text(prompt, max_length, temperature):
if not prompt.strip():
return "Please enter a prompt."
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(
inputs,
max_length=max_length,
do_sample=True,
temperature=temperature,
top_k=50,
top_p=0.95,
num_return_sequences=1,
pad_token_id=tokenizer.eos_token_id
)
generated_text = tokenizer.decode(
outputs[0],
skip_special_tokens=True
)
return generated_text
demo = gr.Interface(
fn=generate_text,
inputs=[
gr.Textbox(
lines=4,
placeholder="Enter a prompt...",
label="Prompt"
),
gr.Slider(
minimum=50,
maximum=300,
value=100,
step=10,
label="Maximum Length"
),
gr.Slider(
minimum=0.1,
maximum=2.0,
value=0.8,
step=0.1,
label="Temperature"
)
],
outputs=gr.Textbox(
label="Generated Text",
lines=10
),
title="GPT-2 Text Generation",
description="Generate text using the pre-trained GPT-2 model with adjustable temperature."
)
if __name__ == "__main__":
demo.launch() |