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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load pre-trained model and tokenizer
model_name = "gpt2" # You can also try "distilgpt2" for a smaller model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Function to generate text
def generate_text(prompt, max_length=100, num_return_sequences=1):
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
inputs["input_ids"],
max_length=max_length,
num_return_sequences=num_return_sequences,
no_repeat_ngram_size=2,
top_p=0.95,
top_k=60,
temperature=0.7,
)
return [tokenizer.decode(output, skip_special_tokens=True) for output in outputs]
# Create Gradio interface
iface = gr.Interface(
fn=generate_text,
inputs=[
gr.Textbox(label="Enter your prompt", placeholder="Type here..."),
gr.Slider(minimum=50, maximum=200, value=100, label="Max Length"),
gr.Slider(minimum=1, maximum=5, value=1, label="Number of Outputs")
],
outputs=gr.Textbox(label="Generated Text"),
title="GPT-2 Text Generator",
description="Generate human-like text based on your prompt using GPT-2.",
theme="compact",
examples=[["Once upon a time in a land far, far away..."]]
)
iface.launch()
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