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import gradio as gr |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import re |
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from resources import banner, error_html_response |
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import logging |
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logging.basicConfig(format='%(asctime)s: [%(levelname)s]: %(message)s', level=logging.INFO) |
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model_checkpoint = 'gastronomia-para-to2/gastronomia_para_to2' |
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tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) |
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model = AutoModelForCausalLM.from_pretrained(model_checkpoint) |
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special_tokens = [ |
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'<INPUT_START>', |
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'<NEXT_INPUT>', |
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'<INPUT_END>', |
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'<TITLE_START>', |
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'<TITLE_END>', |
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'<INGR_START>', |
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'<NEXT_INGR>', |
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'<INGR_END>', |
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'<INSTR_START>', |
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'<NEXT_INSTR>', |
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'<INSTR_END>'] |
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def frame_html_response(html_response): |
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return f"""<iframe style="width: 100%; height: 800px" name="result" allow="midi; geolocation; microphone; camera; |
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display-capture; encrypted-media;" sandbox="allow-modals allow-forms |
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allow-scripts allow-same-origin allow-popups |
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allow-top-navigation-by-user-activation allow-downloads" allowfullscreen="" |
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allowpaymentrequest="" frameborder="0" srcdoc='{html_response}'></iframe>""" |
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def check_special_tokens_order(pre_output): |
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return (pre_output.find('<INPUT_START>') < |
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pre_output.find('<NEXT_INPUT>') <= |
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pre_output.rfind('<NEXT_INPUT>') < |
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pre_output.find('<INPUT_END>') < |
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pre_output.find('<INGR_START>') < |
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pre_output.find('<NEXT_INGR>') <= |
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pre_output.rfind('<NEXT_INGR>') < |
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pre_output.find('<INGR_END>') < |
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pre_output.find('<INSTR_START>') < |
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pre_output.find('<NEXT_INSTR>') <= |
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pre_output.rfind('<NEXT_INSTR>') < |
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pre_output.find('<INSTR_END>') < |
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pre_output.find('<TITLE_START>') < |
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pre_output.find('<TITLE_END>')) |
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def make_html_response(title, ingredients, instructions): |
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ingredients_html_list = '<ul><li>' + '</li><li>'.join(ingredients) + '</li></ul>' |
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instructions_html_list = '<ol><li>' + '</li><li>'.join(instructions) + '</li></ol>' |
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html_response = f''' |
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<!DOCTYPE html> |
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<html> |
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<body> |
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<h1>{title}</h1> |
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<h2>Ingredientes</h2> |
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{ingredients_html_list} |
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<h2>Instrucciones</h2> |
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{instructions_html_list} |
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</body> |
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</html> |
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''' |
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return html_response |
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def rerun_model_output(pre_output): |
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if pre_output is None: |
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return True |
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elif not '<RECIPE_END>' in pre_output: |
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logging.info('<RECIPE_END> not in pre_output') |
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return True |
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pre_output_trimmed = pre_output[:pre_output.find('<RECIPE_END>')] |
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if not all(special_token in pre_output_trimmed for special_token in special_tokens): |
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logging.info('Not all special tokens are in preoutput') |
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return True |
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elif not check_special_tokens_order(pre_output_trimmed): |
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logging.info('Special tokens are unordered in preoutput') |
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return True |
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elif len(pre_output_trimmed.split())<75: |
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logging.info('Length of the recipe is <75') |
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return True |
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else: |
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return False |
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def check_wrong_ingredients(ingredients): |
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new_ingredients = [] |
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for ingredient in ingredients: |
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if ingredient.startswith('De '): |
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new_ingredients.append(ingredient.strip('De ').capitalize()) |
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else: |
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new_ingredients.append(ingredient) |
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return new_ingredients |
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def make_recipe(input_ingredients): |
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logging.info(f'Received inputs: {input_ingredients}') |
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input_ingredients = re.sub(' y ', ', ', input_ingredients) |
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input = '<RECIPE_START> ' |
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input += '<INPUT_START> ' + ' <NEXT_INPUT> '.join(input_ingredients.split(', ')) + ' <INPUT_END> ' |
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input += '<INGR_START> ' |
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tokenized_input = tokenizer(input, return_tensors='pt') |
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pre_output = None |
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i = 0 |
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while rerun_model_output(pre_output): |
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if i == 3: |
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return frame_html_response(error_html_response) |
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output = model.generate(**tokenized_input, |
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max_length=600, |
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do_sample=True, |
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top_p=0.92, |
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top_k=50, |
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num_return_sequences=3) |
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pre_output = tokenizer.decode(output[0], skip_special_tokens=False) |
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i += 1 |
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pre_output_trimmed = pre_output[:pre_output.find('<RECIPE_END>')] |
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output_ingredients = re.search('<INGR_START> (.*) <INGR_END>', pre_output_trimmed).group(1) |
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output_ingredients = output_ingredients.split(' <NEXT_INGR> ') |
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output_ingredients = list(set([output_ingredient.strip() for output_ingredient in output_ingredients])) |
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output_ingredients = [output_ing.capitalize() for output_ing in output_ingredients] |
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output_ingredients = check_wrong_ingredients(output_ingredients) |
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output_title = re.search('<TITLE_START> (.*) <TITLE_END>', pre_output_trimmed).group(1).strip().capitalize() |
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output_instructions = re.search('<INSTR_START> (.*) <INSTR_END>', pre_output_trimmed).group(1) |
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output_instructions = output_instructions.split(' <NEXT_INSTR> ') |
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html_response = make_html_response(output_title, output_ingredients, output_instructions) |
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return frame_html_response(html_response) |
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iface = gr.Interface( |
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fn=make_recipe, |
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inputs= |
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[ |
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gr.inputs.Textbox(lines=1, placeholder='ingrediente_1, ingrediente_2, ..., ingrediente_n', |
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label='Dime con qué ingredientes quieres que cocinemos hoy y te sugeriremos una receta tan pronto como nuestros fogones estén libres'), |
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], |
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outputs= |
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[ |
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gr.outputs.HTML(label="¡Esta es mi propuesta para ti! ¡Buen provecho!") |
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], |
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examples= |
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[ |
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['salmón, zumo de naranja, aceite de oliva, sal, pimienta'], |
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['harina, azúcar, huevos, chocolate, levadura Royal'] |
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], |
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description=banner) |
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iface.launch(enable_queue=True) |
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