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import gradio as gr
import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer
# Load the model and tokenizer
model = GPT2LMHeadModel.from_pretrained("samwell/SamGPT")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2") # Assuming you used the GPT-2 tokenizer
def generate_text(prompt, max_length):
input_ids = tokenizer.encode(prompt, return_tensors="pt")
output = model.generate(input_ids, max_length=max_length, num_return_sequences=1, no_repeat_ngram_size=2)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
return generated_text
iface = gr.Interface(
fn=generate_text,
inputs=[
gr.Textbox(label="Prompt"),
gr.Slider(minimum=10, maximum=200, value=50, step=1, label="Max Length")
],
outputs=gr.Textbox(label="Generated Text"),
title="SamGPT Text Generation",
description="Enter a prompt to generate text with SamGPT."
)
iface.launch() |