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| import numpy as np | |
| import uvicorn | |
| from fastapi import File | |
| from fastapi import FastAPI | |
| from fastapi import UploadFile | |
| import spacy | |
| import pickle | |
| import sentence_transformers | |
| from sentence_transformers import SentenceTransformer, util | |
| from PIL import Image | |
| import torch | |
| import spacy | |
| import spacy_dbpedia_spotlight | |
| import os | |
| import sys | |
| import glob | |
| import random | |
| import pysos | |
| from random import shuffle | |
| app = FastAPI() | |
| print('Loading Models...') | |
| os.system("python -m spacy download en_core_web_sm") | |
| nlp = spacy.load('en_core_web_sm') | |
| nlp.add_pipe('dbpedia_spotlight') | |
| stop_words = set(['chopped', 'freshly ground', 'skinless', 'freshly squeezed', 'dash', 'powder', 'rice', 'ice', 'noodles', 'pepper', 'milk', 'ced', 'cheese', 'sugar', 'salt', 'pkt', 'minced', 'onion', 'onions', 'garlic', 'butter', 'slices', 'ounce', 'sauce', 'freshly', 'grated', 'teaspoon', 'cup', 'oz', '⁄', 'to', 'or', 'diced', 'into', 'pound', 'dried', 'water', 'about', 'whole', 'small', 'vegetable', 'inch', 'tbsp', 'cooked', 'large', 'sliced', 'dry', 'optional', 'package', 'ounces', 'unsalted', 'lbs', 'green', 'flour', 'for', 'wine', 'crushed', 'drained', 'lb', 'frozen', 'tsp', 'finely', 'medium', 'tablespoon', 'tablespoons', 'juice', 'shredded', 'can', 'minced', 'fresh', 'cut', 'pieces', 'in', 'thinly', 'of', 'extract', 'teaspoons', 'ground', 'and', 'cups', 'peeled', 'taste', 'ml', 'lengths']) | |
| model = SentenceTransformer('clip-ViT-B-32') | |
| with open("./Pretrained/labels.pkl", 'rb') as fIn: | |
| labels = pickle.load(fIn) | |
| emb_filename = './Pretrained/food_embeddings.pkl' | |
| text_emb = torch.load(emb_filename, map_location=torch.device('cpu')) | |
| food2id = pysos.Dict("./Pretrained/food2id") | |
| id2recipe = pysos.Dict("./Pretrained/id2recipe") | |
| def read_root(): | |
| return {"WhatTheFood !"} | |
| def get_food(food_input): | |
| results = detect_food(food_input, 3) | |
| food_recognised, score = results[0] | |
| id = food2id[food_recognised] | |
| recipe_name = food_recognised.title() | |
| ingredients_list =id2recipe[id]['ingredients'] | |
| highlighted_ingredients= get_spacy_dbpedia_highlights(ingredients_list) | |
| recipe= id2recipe[id]['instructions'] | |
| dataset = " " + id2recipe[id]['dataset'] | |
| nutritional_facts = id2recipe[id]['nutrition_facts'] | |
| source= id2recipe[id]['recipesource'] | |
| recipe_obj = {} | |
| recipe_obj['recipe_name'] = recipe_name | |
| recipe_obj['highlighted_ingredients'] = highlighted_ingredients | |
| recipe_obj['recipe'] = recipe | |
| recipe_obj['nutritional_facts'] = nutritional_facts | |
| recipe_obj['source'] = source | |
| return {"top3": results, "recipe": recipe_obj} | |
| def get_spacy_dbpedia_highlights(ingredients): | |
| raw_ingredients = ingredients | |
| import re | |
| ingredients = re.sub("[0-9,()\/\-\.]", "", ingredients) | |
| doc = nlp(ingredients) | |
| for ent in doc.ents: | |
| if ent.text.lower() not in stop_words and ent.text in raw_ingredients: | |
| replace_str = '<mark style="color: green; background-color:yellow"> <a href="' + ent.kb_id_ + '" target="_blank"> ' + ent.text + '</a> </mark>' | |
| raw_ingredients = raw_ingredients.replace(ent.text, replace_str) | |
| return raw_ingredients | |
| def detect_food(query, k=1): | |
| print(os.system("pwd")) | |
| query_emb = model.encode(Image.open(query), convert_to_tensor=True, show_progress_bar=False) | |
| hits = util.semantic_search(query_emb, text_emb, top_k=k)[0] | |
| results = [] | |
| for i, hit in enumerate(hits): | |
| results.append((labels[hit['corpus_id']], hit['score'])) | |
| if i > 2: | |
| break | |
| return results | |