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| import gradio as gr | |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, GenerationConfig | |
| # 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, min_new_tokens, num_beams, temperature, top_p): | |
| inputs = tokenizer.encode("grammar: " + text, return_tensors="pt") | |
| if max_new_tokens > 0 or min_new_tokens > 0: | |
| if max_new_tokens > 0 and min_new_tokens > 0: | |
| outputs = model.generate( | |
| inputs, | |
| max_new_tokens=max_new_tokens, | |
| min_new_tokens=min_new_tokens, | |
| num_beams=num_beams, | |
| temperature=temperature, | |
| top_p=top_p, | |
| early_stopping=True, | |
| do_sample=True | |
| ) | |
| elif max_new_tokens > 0: | |
| outputs = model.generate(inputs, max_new_tokens=max_new_tokens, min_length=min_length, num_beams=num_beams, temperature=temperature, top_p=top_p, early_stopping=True, do_sample=True) | |
| else: | |
| outputs = model.generate(inputs, max_length=max_length, min_new_tokens=min_new_tokens, num_beams=num_beams, temperature=temperature, top_p=top_p, early_stopping=True, do_sample=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, | |
| do_sample=True | |
| ) | |
| corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| yield corrected_text | |
| def correct_text2(text, genConfig): | |
| inputs = tokenizer.encode("grammar: " + text, return_tensors="pt") | |
| outputs = model.generate(inputs, **genConfig.to_dict()) | |
| corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| yield corrected_text | |
| def respond(text, max_length, min_length, max_new_tokens, min_new_tokens, num_beams, temperature, top_p): | |
| config = GenerationConfig( | |
| max_length=max_length, | |
| min_length=min_length, | |
| num_beams=num_beams, | |
| temperature=temperature, | |
| top_p=top_p, | |
| early_stopping=True, | |
| do_sample=True | |
| ) | |
| # Add max/min new tokens if they are there | |
| if max_new_tokens > 0: | |
| config.max_new_tokens = max_new_tokens | |
| if min_new_tokens > 0: | |
| config.min_new_tokens = min_new_tokens | |
| corrected = correct_text2(text, config) | |
| yield corrected | |
| def update_prompt(prompt): | |
| return prompt | |
| # Create the Gradio interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("""# Grammar Correction App""") | |
| prompt_box = gr.Textbox(placeholder="Enter your prompt here...") | |
| output_box = gr.Textbox() | |
| # 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) | |
| 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") | |
| min_tokens = gr.Slider(minimum=0, maximum=256, value=0, step=1, label="Min 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(respond, [prompt_box, max_length, min_length, max_tokens, min_tokens, num_beams, temperature, top_p], output_box) | |
| demo.launch() |