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| import gradio as gr | |
| import pandas as pd | |
| from huggingface_hub import hf_hub_url, cached_download | |
| from gensim.models import KeyedVectors | |
| # Setup model | |
| url = hf_hub_url(repo_id="map222/recipe-spice-model", filename="recipe_w2v.gensim") | |
| cached_download(url) | |
| recipe_w2v = KeyedVectors.load(cached_download(url) ) | |
| ''' | |
| url = hf_hub_url(repo_id="map222/recipe-spice-model", filename="ingredient_count.json") | |
| cached_download(url) | |
| ingredient_count = json.load(cached_download(url) ) | |
| url = hf_hub_url(repo_id="map222/recipe-spice-model", filename="recipe_NER.tsv") | |
| cached_download(url) | |
| recipe_NER = pd.read_csv(cached_download(url), sep='\t' ) | |
| ''' | |
| def get_fusion_ingredients(ingredient: str, | |
| recipe_model, #gensim model | |
| recipes, #iterable of recipes | |
| ingredient_count: dict, | |
| max_candidates = 20, | |
| min_occurence_factor = 100 # minimum number of recipes an ingredient has to be in | |
| ): | |
| ingredient_recipes = recipes.loc[recipes.apply(lambda row: ingredient in row)] | |
| ingredient_candidates = recipe_model.most_similar(ingredient, topn=50) # get top similar ingredients | |
| candidate_names = list(zip(*ingredient_candidates))[0] | |
| pruned_candidates = [candidate for candidate in candidate_names if ingredient not in candidate][:max_candidates] # clean up candidates to remove duplicates (e.g. "gala apple") | |
| cooccurrence_counts = calc_cooccurrence(ingredient, candidate_names, ingredient_recipes) # get counts for normalization | |
| # final score for sorting: similarity / how often co-occur / total occurences | |
| min_occurences = max(cooccurrence_counts.values()) / min_occurence_factor | |
| print(min_occurences) | |
| freq_norm_candidates = {candidate[0]: candidate[1] / (cooccurrence_counts[candidate[0]]+1) / ingredient_count[candidate[0]] for candidate in ingredient_candidates if candidate[0] in pruned_candidates and cooccurrence_counts[candidate[0]] > min_occurences} | |
| top_candidates = sorted([(k,v) for k,v in freq_norm_candidates.items()], key=lambda x: x[1])[-5:] | |
| return top_candidates, cooccurrence_counts, ingredient_candidates # return multiple for debugging | |
| def helper_func(text): | |
| #a,b,c = get_fusion_ingredients(x, recipe_w2v, ingredient_count, recipe_NER) | |
| nearest = recipe_w2v.wv.most_similar(text) | |
| return pd.DataFrame(nearest) | |
| app = gr.Blocks() | |
| with app: | |
| gr.Markdown("# Recipe Spice") | |
| with gr.Row(): | |
| text_in = gr.Textbox(lines=1, placeholder="Ingredient", label="Search Query") | |
| with gr.Row(): | |
| df_out = gr.Dataframe(interactive=False) | |
| with gr.Row(): | |
| gr.Markdown( | |
| """ | |
| #### Project Description | |
| This finds cool new recipe stuff | |
| """ | |
| ) | |
| text_in.submit(helper_func, inputs=[text_in], outputs=[df_out]) | |
| app.launch() |