| from transformers import pipeline | |
| import gradio as gr | |
| import warnings | |
| warnings.simplefilter('ignore') | |
| model_path = './khaanaGPT' | |
| contrastive_search_config = dict( | |
| penalty_alpha = 0.5, | |
| top_k = 5, | |
| max_new_tokens = 512, | |
| pad_token_id = 50259 | |
| ) | |
| model = pipeline('text-generation',model=model_path) | |
| def create_prompt(ingredients): | |
| ingredients = ','.join([x.strip() for x in ingredients.split(',')]) | |
| ingredients = ingredients.strip().replace(',','\n').lower() | |
| s = f"<|startoftext|>Ingredients:\n{ingredients}\n\nInstructions:\n" | |
| return s | |
| def generate(prompt): | |
| recipe = model(prompt,**contrastive_search_config)[0]['generated_text'] | |
| recipe = recipe.replace('<|startoftext|>','') | |
| return recipe | |
| def wrapper(ingredients): | |
| prompt = create_prompt(ingredients) | |
| recipe = generate(prompt) | |
| return recipe | |
| intro_html = """ | |
| <center><h1>खानाGPT</h1></center> | |
| <center> | |
| <p>it's not perfect, may ± ingredients. The recipes are coherent, | |
| but the main purpose of this project was to understand fine-tuning a causalLM like GPT-2. | |
| This model was fine-tuned on GPT-2 Small.</p> | |
| </center> | |
| """ | |
| with gr.Blocks() as demo: | |
| gr.HTML(intro_html) | |
| ingredients = gr.Textbox(label="ingredients", | |
| placeholder='separate the ingredients with a comma.') | |
| output = gr.Textbox(label="recipe",lines=15,) | |
| greet_btn = gr.Button("Create a recipe!") | |
| gr.Examples(['yellow dal, turmeric, green peas, tomatoes', | |
| 'chicken, soy sauce, tomato sauce, vinegar'], | |
| inputs=ingredients | |
| ) | |
| greet_btn.click(fn=wrapper, inputs=ingredients, outputs=output) | |
| demo.launch() |