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| title: Chef Transformer | |
| emoji: 🍲 | |
| colorFrom: blue | |
| colorTo: red | |
| sdk: streamlit | |
| app_file: app.py | |
| pinned: false | |
| # Chef Transformer (T5) | |
| > This is part of the [Flax/Jax Community Week](https://discuss.huggingface.co/t/recipe-generation-model/7475), organized by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google. | |
| Want to give it a try? Then what's the wait, head over to the demo [here](https://share.streamlit.io/chef-transformer/chef-transformer/main/app.py). | |
| ## Team Members | |
| - Mehrdad Farahani ([m3hrdadfi](https://huggingface.co/m3hrdadfi)) | |
| - Kartik Godawat ([dk-crazydiv](https://huggingface.co/dk-crazydiv)) | |
| - Haswanth Aekula ([hassiahk](https://huggingface.co/hassiahk)) | |
| - Deepak Pandian ([rays2pix](https://huggingface.co/rays2pix)) | |
| - Nicholas Broad ([nbroad](https://huggingface.co/nbroad)) | |
| ## Dataset | |
| [RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation](https://recipenlg.cs.put.poznan.pl/). This dataset contains **2,231,142** cooking recipes (>2 millions) with size of **2.14 GB**. It's processed in more careful way. | |
| ### Example | |
| ```json | |
| { | |
| "NER": [ | |
| "oyster crackers", | |
| "salad dressing", | |
| "lemon pepper", | |
| "dill weed", | |
| "garlic powder", | |
| "salad oil" | |
| ], | |
| "directions": [ | |
| "Combine salad dressing mix and oil.", | |
| "Add dill weed, garlic powder and lemon pepper.", | |
| "Pour over crackers; stir to coat.", | |
| "Place in warm oven.", | |
| "Use very low temperature for 15 to 20 minutes." | |
| ], | |
| "ingredients": [ | |
| "12 to 16 oz. plain oyster crackers", | |
| "1 pkg. Hidden Valley Ranch salad dressing mix", | |
| "1/4 tsp. lemon pepper", | |
| "1/2 to 1 tsp. dill weed", | |
| "1/4 tsp. garlic powder", | |
| "3/4 to 1 c. salad oil" | |
| ], | |
| "link": "www.cookbooks.com/Recipe-Details.aspx?id=648947", | |
| "source": "Gathered", | |
| "title": "Hidden Valley Ranch Oyster Crackers" | |
| } | |
| ``` | |
| ## How To Use | |
| ```bash | |
| # Installing requirements | |
| pip install transformers | |
| ``` | |
| ```python | |
| from transformers import FlaxAutoModelForSeq2SeqLM | |
| from transformers import AutoTokenizer | |
| MODEL_NAME_OR_PATH = "flax-community/t5-recipe-generation" | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME_OR_PATH, use_fast=True) | |
| model = FlaxAutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME_OR_PATH) | |
| prefix = "items: " | |
| # generation_kwargs = { | |
| # "max_length": 1024, | |
| # "min_length": 128, | |
| # "no_repeat_ngram_size": 3, | |
| # "do_sample": True, | |
| # "top_k": 60, | |
| # "top_p": 0.95 | |
| # } | |
| generation_kwargs = { | |
| "max_length": 512, | |
| "min_length": 64, | |
| "no_repeat_ngram_size": 3, | |
| "early_stopping": True, | |
| "num_beams": 5, | |
| "length_penalty": 1.5, | |
| } | |
| special_tokens = tokenizer.all_special_tokens | |
| tokens_map = { | |
| "<sep>": "--", | |
| "<section>": "\n" | |
| } | |
| def skip_special_tokens(text, special_tokens): | |
| for token in special_tokens: | |
| text = text.replace(token, "") | |
| return text | |
| def target_postprocessing(texts, special_tokens): | |
| if not isinstance(texts, list): | |
| texts = [texts] | |
| new_texts = [] | |
| for text in texts: | |
| text = skip_special_tokens(text, special_tokens) | |
| for k, v in tokens_map.items(): | |
| text = text.replace(k, v) | |
| new_texts.append(text) | |
| return new_texts | |
| def generation_function(texts): | |
| _inputs = texts if isinstance(texts, list) else [texts] | |
| inputs = [prefix + inp for inp in _inputs] | |
| inputs = tokenizer( | |
| inputs, | |
| max_length=256, | |
| padding="max_length", | |
| truncation=True, | |
| return_tensors="jax" | |
| ) | |
| input_ids = inputs.input_ids | |
| attention_mask = inputs.attention_mask | |
| output_ids = model.generate( | |
| input_ids=input_ids, | |
| attention_mask=attention_mask, | |
| **generation_kwargs | |
| ) | |
| generated = output_ids.sequences | |
| generated_recipe = target_postprocessing( | |
| tokenizer.batch_decode(generated, skip_special_tokens=False), | |
| special_tokens | |
| ) | |
| return generated_recipe | |
| ``` | |
| ```python | |
| items = [ | |
| "macaroni, butter, salt, bacon, milk, flour, pepper, cream corn", | |
| "provolone cheese, bacon, bread, ginger" | |
| ] | |
| generated = generation_function(items) | |
| for text in generated: | |
| sections = text.split("\n") | |
| for section in sections: | |
| section = section.strip() | |
| if section.startswith("title:"): | |
| section = section.replace("title:", "") | |
| headline = "TITLE" | |
| elif section.startswith("ingredients:"): | |
| section = section.replace("ingredients:", "") | |
| headline = "INGREDIENTS" | |
| elif section.startswith("directions:"): | |
| section = section.replace("directions:", "") | |
| headline = "DIRECTIONS" | |
| if headline == "TITLE": | |
| print(f"[{headline}]: {section.strip().capitalize()}") | |
| else: | |
| section_info = [f" - {i+1}: {info.strip().capitalize()}" for i, info in enumerate(section.split("--"))] | |
| print(f"[{headline}]:") | |
| print("\n".join(section_info)) | |
| print("-" * 130) | |
| ``` | |
| Output: | |
| ```text | |
| [TITLE]: Macaroni and corn | |
| [INGREDIENTS]: | |
| - 1: 2 c. macaroni | |
| - 2: 2 tbsp. butter | |
| - 3: 1 tsp. salt | |
| - 4: 4 slices bacon | |
| - 5: 2 c. milk | |
| - 6: 2 tbsp. flour | |
| - 7: 1/4 tsp. pepper | |
| - 8: 1 can cream corn | |
| [DIRECTIONS]: | |
| - 1: Cook macaroni in boiling salted water until tender. | |
| - 2: Drain. | |
| - 3: Melt butter in saucepan. | |
| - 4: Blend in flour, salt and pepper. | |
| - 5: Add milk all at once. | |
| - 6: Cook and stir until thickened and bubbly. | |
| - 7: Stir in corn and bacon. | |
| - 8: Pour over macaroni and mix well. | |
| ---------------------------------------------------------------------------------------------------------------------------------- | |
| [TITLE]: Grilled provolone and bacon sandwich | |
| [INGREDIENTS]: | |
| - 1: 2 slices provolone cheese | |
| - 2: 2 slices bacon | |
| - 3: 2 slices sourdough bread | |
| - 4: 2 slices pickled ginger | |
| [DIRECTIONS]: | |
| - 1: Place a slice of provolone cheese on one slice of bread. | |
| - 2: Top with a slice of bacon. | |
| - 3: Top with a slice of pickled ginger. | |
| - 4: Top with the other slice of bread. | |
| - 5: Heat a skillet over medium heat. | |
| - 6: Place the sandwich in the skillet and cook until the cheese is melted and the bread is golden brown. | |
| ---------------------------------------------------------------------------------------------------------------------------------- | |
| ``` | |
| ## Evaluation | |
| The following table summarizes the scores obtained by the **Chef Transformer**. Those marked as (*) are the baseline models. | |
| | Model | WER | COSIM | ROUGE-2 | | |
| | :-------------: | :---: | :---: | :-----: | | |
| | Recipe1M+ * | 0.786 | 0.589 | - | | |
| | RecipeNLG * | 0.751 | 0.666 | - | | |
| | ChefTransformer | 0.709 | 0.714 | 0.290 | | |
| ## Streamlit demo | |
| ```bash | |
| streamlit run app.py | |
| ``` | |
| ## Looking to contribute? | |
| Then follow the steps mentioned in this [contributing guide](CONTRIBUTING.md) and you are good to go. | |
| ## Copyright | |
| Special thanks to those who provided these fantastic materials. | |
| - [Anatomy](https://www.flaticon.com/free-icon) | |
| - [Chef Hat](https://www.vecteezy.com/members/jellyfishwater) | |
| - [Moira Nazzari](https://pixabay.com/photos/food-dessert-cake-eggs-butter-3048440/) | |
| - [Instagram Post](https://www.freepik.com/free-psd/recipes-ad-social-media-post-template_11520617.htm) | |