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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
# Define the function to handle text generation
def generate_text(model_name, text, num_beams, max_length, top_p, temperature, repetition_penalty, no_repeat_ngram_size):
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Initialize pipeline
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
# Generate text with the specified parameters
generated_text = pipe(text,
pad_token_id=tokenizer.eos_token_id,
num_beams=num_beams,
max_length=max_length,
top_p=top_p,
temperature=temperature,
repetition_penalty=repetition_penalty,
no_repeat_ngram_size=no_repeat_ngram_size)[0]['generated_text']
return generated_text
# Define model options
model_options = [
"riotu-lab/ArabianGPT-01B",
"riotu-lab/ArabianGPT-03B",
"riotu-lab/ArabianGPT-08B"
]
# Define Gradio interface components
inputs_component = [
gr.Dropdown(choices=model_options, label="Select Model"),
gr.Textbox(lines=2, placeholder="Enter your text here...", label="Input Text"),
gr.Slider(minimum=1, maximum=10, step=1, label="Num Beams"),
gr.Slider(minimum=50, maximum=300, step=10, label="Max Length"),
gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label="Top p"),
gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label="Temperature"),
gr.Slider(minimum=1.0, maximum=5.0, step=0.5, label="Repetition Penalty"),
gr.Slider(minimum=2, maximum=5, step=1, label="No Repeat Ngram Size")
]
# Setup the interface
iface = gr.Interface(
fn=generate_text,
inputs=inputs_component,
outputs="text",
title="ArabianGPT Playground",
description="Explore the capabilities of ArabianGPT models. Adjust the hyperparameters to see how they affect text generation.",
live=True
)
# Launch the app
iface.launch()
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