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import gradio as gr
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
# Load the model and tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("vennify/t5-base-grammar-correction")
tokenizer = AutoTokenizer.from_pretrained("vennify/t5-base-grammar-correction")
def correct_text(text, max_length, min_length, max_new_tokens, num_beams, temperature, top_p):
inputs = tokenizer.encode("grammar: " + text, return_tensors="pt")
if max_new_tokens > 0:
outputs = model.generate(
inputs,
max_length=max_length,
max_new_tokens=max_new_tokens,
min_length=min_length,
num_beams=num_beams,
temperature=temperature,
top_p=top_p,
early_stopping=True
)
else:
outputs = model.generate(
inputs,
max_length=max_length,
min_length=min_length,
num_beams=num_beams,
temperature=temperature,
top_p=top_p,
early_stopping=True
)
corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
yield corrected_text
def respond(prompt, max_length, min_length, max_new_tokens, num_beams, temperature, top_p):
# Your response generation logic here
#response = correct_text(message, max_length, max_new_tokens, min_length, num_beams, temperature, top_p)
#yield response
#return f"System message: {system_message}, Max Length: {max_length}, Min Length: {min_length}, Max new tokens: {max_new_tokens}, Num Beams: {num_beams}, Temperature: {temperature}, Top-p: {top_p}"
#response = correct_text(prompt, max_length, max_new_tokens, min_length, num_beams, temperature, top_p)
return prompt
def update_prompt(promptBtn):
return promptBtn.value
# Create the Gradio interface
with gr.Blocks() as demo:
gr.Markdown(
"""
# Grammar Correction App
""")
prompt_box = gr.Textbox(lines=2, placeholder="Enter your prompt here...")
output_box = gr.Textbox()
submitBtn = gr.Button("Submit")
# Sample prompts
with gr.Row():
samp1 = gr.Button("we shood buy an car")
samp2 = gr.Button("she is more taller")
samp3 = gr.Button("John and i saw a sheep over their.")
samp1.click(update_prompt, samp1, prompt_box)
samp2.click(update_prompt, samp2, prompt_box)
samp3.click(update_prompt, samp3, prompt_box)
with gr.Accordion("Generation Parameters:", open=False):
max_length = gr.Slider(minimum=1, maximum=256, value=80, step=1, label="Max Length")
min_length = gr.Slider(minimum=1, maximum=256, value=0, step=1, label="Min Length")
max_tokens = gr.Slider(minimum=0, maximum=256, value=0, step=1, label="Max new tokens")
num_beams = gr.Slider(minimum=1, maximum=10, value=5, step=1, label="Num Beams")
temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
submitBtn.click(correct_text, [prompt_box, max_length, min_length, max_tokens, num_beams, temperature, top_p], output_box)
demo.launch()
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