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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) | |
| return 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 | |
| # 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") | |
| 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)") | |
| show_top_p = gr.Checkbox(value=True, label="Show Top-p Slider") | |
| top_p_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)", visible=True) | |
| show_top_p.change(lambda show: gr.update(visible=show), show_top_p, top_p_slider) | |
| submitBtn.click(correct_text, [prompt_box, max_length, min_length, max_tokens, num_beams, temperature, top_p], output_box) | |
| demo.launch() | |