Upload 8 files
Browse files- Datasets/Final used Datasets/disease_food_nutrition_mapping.csv +0 -0
- Datasets/Final used Datasets/food_dataset_with_nutriition.csv +0 -0
- Dockerfile +30 -0
- app.py +621 -0
- docs/common_ingredients.json +68 -0
- docs/common_misspellings.json +454 -0
- docs/common_nutrition.json +0 -0
- requirements.txt +12 -0
Datasets/Final used Datasets/disease_food_nutrition_mapping.csv
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Datasets/Final used Datasets/food_dataset_with_nutriition.csv
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Dockerfile
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# Use an official lightweight Python image.
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FROM python:3.10-slim
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# Install system dependencies required by some Python packages.
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RUN apt-get update && apt-get install -y \
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build-essential \
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git \
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wget \
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&& rm -rf /var/lib/apt/lists/*
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# Set the working directory inside the container.
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WORKDIR /app
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# Copy the requirements file and install the Python dependencies.
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# This helps leverage Docker's layer caching when dependencies don’t change.
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COPY requirements.txt .
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RUN pip install --upgrade pip && pip install -r requirements.txt
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# Copy your entire project into the container.
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COPY . .
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# Set an environment variable for the port; your code defaults to port 7860.
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ENV PORT=7860
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# Expose the port (Hugging Face Spaces will map this externally).
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EXPOSE 7860
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# Define the command to run your Flask app.
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# Make sure the file name below matches the name of your main Python file.
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CMD ["python", "app.py"]
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app.py
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1 |
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from flask import Flask, request, jsonify
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2 |
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import pandas as pd
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from transformers import pipeline
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import os
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import re
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import json
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7 |
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import requests
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import random
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from difflib import get_close_matches
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from textblob import TextBlob
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from nltk.tokenize import word_tokenize, sent_tokenize
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import nltk
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import ast
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from urllib.parse import quote
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def force_download_nltk():
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needed_packages = ['punkt']
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for package in needed_packages:
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try:
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nltk.data.find(f'tokenizers/{package}')
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except LookupError:
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print(f"Downloading NLTK package: {package}")
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nltk.download(package)
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force_download_nltk()
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domain_words = {
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"carb", "carbs", "carbo", "carbohydrate", "carbohydrates",
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"fat", "fats", "protein", "proteins", "fiber", "cholesterol",
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"calcium", "iron", "magnesium", "potassium", "sodium", "vitamin", "vitamin c",
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"calories", "calorie"
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}
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+
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33 |
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def smart_correct_spelling(text, domain_set):
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34 |
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tokens = word_tokenize(text)
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35 |
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corrected_tokens = []
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36 |
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for token in tokens:
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37 |
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if token.isalpha() and token.lower() not in domain_set:
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38 |
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corrected_word = str(TextBlob(token).correct())
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39 |
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corrected_tokens.append(corrected_word)
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40 |
+
else:
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41 |
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corrected_tokens.append(token)
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42 |
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return " ".join(corrected_tokens)
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43 |
+
|
44 |
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qa_pipeline = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")
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45 |
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classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
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46 |
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summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
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47 |
+
|
48 |
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def summarize_input(text):
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49 |
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summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
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return summary[0]['summary_text']
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51 |
+
|
52 |
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df = pd.read_csv(r"Datasets\Final used Datasets\food_dataset_with_nutriition.csv")
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print(f"Starting with {len(df)} recipes in dataset")
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nutrition_columns = ["calories", "Total fats", "Carbohydrate", "Fiber", "Protein",
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"Cholesterol", "Calcium", "Iron", "Magnesium", "Potassium", "Sodium", "Vitamin C"]
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for col in nutrition_columns:
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df[col] = pd.to_numeric(df[col], errors='coerce')
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58 |
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disease_df = pd.read_csv(r"Datasets\Final used Datasets\disease_food_nutrition_mapping.csv")
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disease_df["Disease"] = disease_df["Disease"].str.lower()
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61 |
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try:
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63 |
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with open(r"docs\common_misspellings.json", "r") as file:
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common_misspellings = json.load(file)
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except FileNotFoundError:
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common_misspellings = {"suger": "sugar", "milc": "milk"}
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with open(r"docs\common_misspellings.json", "w") as file:
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json.dump(common_misspellings, file, indent=2)
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69 |
+
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70 |
+
try:
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71 |
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with open(r"docs\common_ingredients.json", "r") as file:
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common_ingredients = json.load(file)
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73 |
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except FileNotFoundError:
|
74 |
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common_ingredients = ["sugar", "salt", "flour", "milk", "eggs", "butter", "oil", "water"]
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75 |
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with open(r"docs\common_ingredients.json", "w") as file:
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76 |
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json.dump(common_ingredients, file, indent=2)
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77 |
+
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78 |
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def create_ingredient_dictionary(dataframe, common_ingredients_list):
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79 |
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all_ingredients = []
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80 |
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all_ingredients.extend(common_ingredients_list)
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81 |
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all_ingredients.extend(set(common_misspellings.values()))
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82 |
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for ingredients_list in dataframe['ingredients']:
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83 |
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parts = re.split(r',|\sand\s|\sor\s|;', str(ingredients_list))
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84 |
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for part in parts:
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85 |
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clean_part = re.sub(
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86 |
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r'\d+[\s/]*(oz|ounce|cup|tbsp|tsp|tablespoon|teaspoon|pound|lb|g|ml|l|pinch|dash)\b\.?',
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87 |
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'', part)
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88 |
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clean_part = re.sub(
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r'\b(fresh|freshly|chopped|minced|diced|sliced|grated|ground|powdered|crushed|toasted|roasted)\b',
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90 |
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'', clean_part)
|
91 |
+
clean_part = re.sub(r'\(.*?\)', '', clean_part)
|
92 |
+
clean_part = clean_part.strip()
|
93 |
+
subparts = re.split(r'\sand\s|\sor\s', clean_part)
|
94 |
+
for subpart in subparts:
|
95 |
+
cleaned_subpart = subpart.strip().lower()
|
96 |
+
if cleaned_subpart and len(cleaned_subpart) > 2:
|
97 |
+
all_ingredients.append(cleaned_subpart)
|
98 |
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unique_ingredients = list(set(all_ingredients))
|
99 |
+
unique_ingredients.sort(key=len, reverse=True)
|
100 |
+
return unique_ingredients
|
101 |
+
food_dictionary = create_ingredient_dictionary(df, common_ingredients)
|
102 |
+
|
103 |
+
def identify_food_ingredient(text, ingredient_dict, misspellings_dict):
|
104 |
+
cleaned = re.sub(
|
105 |
+
r'\d+[\s/]*(oz|ounce|cup|tbsp|tsp|tablespoon|teaspoon|pound|lb|g|ml|l|pinch|dash)\b\.?',
|
106 |
+
'', text)
|
107 |
+
cleaned = re.sub(
|
108 |
+
r'\b(fresh|freshly|chopped|minced|diced|sliced|grated|ground|powdered|crushed|toasted|roasted)\b',
|
109 |
+
'', cleaned)
|
110 |
+
cleaned = re.sub(r'\(.*?\)', '', cleaned)
|
111 |
+
cleaned = cleaned.strip().lower()
|
112 |
+
if cleaned in misspellings_dict:
|
113 |
+
return misspellings_dict[cleaned]
|
114 |
+
if cleaned in ingredient_dict:
|
115 |
+
return cleaned
|
116 |
+
words = cleaned.split()
|
117 |
+
for word in words:
|
118 |
+
if word in ingredient_dict:
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119 |
+
return word
|
120 |
+
if word in misspellings_dict:
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121 |
+
return misspellings_dict[word]
|
122 |
+
close_matches = get_close_matches(cleaned, ingredient_dict, n=3, cutoff=0.8)
|
123 |
+
if close_matches:
|
124 |
+
return close_matches[0]
|
125 |
+
for dict_ingredient in ingredient_dict:
|
126 |
+
if dict_ingredient in cleaned:
|
127 |
+
return dict_ingredient
|
128 |
+
close_matches = get_close_matches(cleaned, ingredient_dict, n=3, cutoff=0.6)
|
129 |
+
if close_matches:
|
130 |
+
return close_matches[0]
|
131 |
+
return None
|
132 |
+
|
133 |
+
def correct_food_ingredient(ingredient, ingredient_dict, misspellings_dict):
|
134 |
+
cleaned = re.sub(
|
135 |
+
r'\d+[\s/]*(oz|ounce|cup|tbsp|tsp|tablespoon|teaspoon|pound|lb|g|ml|l|pinch|dash)\b\.?',
|
136 |
+
'', ingredient)
|
137 |
+
cleaned = re.sub(
|
138 |
+
r'\b(fresh|freshly|chopped|minced|diced|sliced|grated|ground|powdered|crushed|toasted|roasted)\b',
|
139 |
+
'', cleaned)
|
140 |
+
cleaned = re.sub(r'\(.*?\)', '', cleaned)
|
141 |
+
cleaned = cleaned.strip().lower()
|
142 |
+
if cleaned in misspellings_dict:
|
143 |
+
return misspellings_dict[cleaned]
|
144 |
+
if cleaned in ingredient_dict:
|
145 |
+
return cleaned
|
146 |
+
close_matches = get_close_matches(cleaned, ingredient_dict, n=3, cutoff=0.8)
|
147 |
+
if close_matches:
|
148 |
+
return close_matches[0]
|
149 |
+
close_matches = get_close_matches(cleaned, ingredient_dict, n=3, cutoff=0.6)
|
150 |
+
if close_matches:
|
151 |
+
return close_matches[0]
|
152 |
+
for dict_ingredient in ingredient_dict:
|
153 |
+
if cleaned in dict_ingredient or dict_ingredient in cleaned:
|
154 |
+
return dict_ingredient
|
155 |
+
return cleaned
|
156 |
+
|
157 |
+
def add_misspelling(misspelled, correct):
|
158 |
+
try:
|
159 |
+
with open(r"docs\common_misspellings.json", "r") as file:
|
160 |
+
misspellings = json.load(file)
|
161 |
+
misspellings[misspelled.lower()] = correct.lower()
|
162 |
+
with open(r"docs\common_misspellings.json", "w") as file:
|
163 |
+
json.dump(misspellings, file, indent=2, sort_keys=True)
|
164 |
+
return True
|
165 |
+
except Exception:
|
166 |
+
return False
|
167 |
+
|
168 |
+
def extract_unwanted_ingredients(input_text):
|
169 |
+
question = "What ingredients should be excluded?"
|
170 |
+
result = qa_pipeline(question=question, context=input_text)
|
171 |
+
raw_answer = result['answer']
|
172 |
+
potential_ingredients = []
|
173 |
+
for part in raw_answer.split(','):
|
174 |
+
for subpart in part.split(' and '):
|
175 |
+
for item in subpart.split(' or '):
|
176 |
+
clean_item = item.strip()
|
177 |
+
if clean_item:
|
178 |
+
potential_ingredients.append(clean_item)
|
179 |
+
valid_ingredients = []
|
180 |
+
for item in potential_ingredients:
|
181 |
+
corrected = identify_food_ingredient(item, food_dictionary, common_misspellings)
|
182 |
+
if corrected:
|
183 |
+
valid_ingredients.append(corrected)
|
184 |
+
return valid_ingredients if valid_ingredients else [raw_answer]
|
185 |
+
|
186 |
+
def classify_clause(clause):
|
187 |
+
candidate_labels = ["include", "exclude"]
|
188 |
+
result = classifier(clause, candidate_labels, hypothesis_template="This clause means the ingredient should be {}.")
|
189 |
+
return result["labels"][0].lower()
|
190 |
+
|
191 |
+
def extract_ingredients_from_clause(clause, ingredient_dict, misspellings_dict):
|
192 |
+
found = []
|
193 |
+
for ingredient in ingredient_dict:
|
194 |
+
if ingredient.lower() in clause.lower():
|
195 |
+
normalized = identify_food_ingredient(ingredient, ingredient_dict, misspellings_dict)
|
196 |
+
if normalized:
|
197 |
+
found.append(normalized)
|
198 |
+
return list(set(found))
|
199 |
+
|
200 |
+
def classify_ingredients_in_query(query, ingredient_dict, misspellings_dict):
|
201 |
+
include_ingredients = []
|
202 |
+
exclude_ingredients = []
|
203 |
+
|
204 |
+
nutrition_terms = ['calories', 'calorie', 'fat', 'fats', 'carb', 'carbs', 'protein',
|
205 |
+
'fiber', 'cholesterol', 'calcium', 'iron', 'magnesium',
|
206 |
+
'potassium', 'sodium', 'vitamin']
|
207 |
+
modified_query = query
|
208 |
+
for term in nutrition_terms:
|
209 |
+
pattern = re.compile(r'(low|high)\s+' + term, re.IGNORECASE)
|
210 |
+
modified_query = pattern.sub('', modified_query)
|
211 |
+
clauses = re.split(r'\bbut\b|,', modified_query, flags=re.IGNORECASE)
|
212 |
+
for clause in clauses:
|
213 |
+
clause = clause.strip()
|
214 |
+
if not clause:
|
215 |
+
continue
|
216 |
+
intent = classify_clause(clause)
|
217 |
+
ingredients_found = extract_ingredients_from_clause(clause, ingredient_dict, misspellings_dict)
|
218 |
+
if intent == "include":
|
219 |
+
include_ingredients.extend(ingredients_found)
|
220 |
+
elif intent == "exclude":
|
221 |
+
exclude_ingredients.extend(ingredients_found)
|
222 |
+
return list(set(include_ingredients)), list(set(exclude_ingredients))
|
223 |
+
|
224 |
+
def extract_nutrition_from_clause(clause, nutrition_dict, misspellings_dict):
|
225 |
+
found = []
|
226 |
+
clause_lower = clause.lower()
|
227 |
+
sorted_terms = sorted(nutrition_dict, key=lambda x: -len(x))
|
228 |
+
for term in sorted_terms:
|
229 |
+
pattern = r'\b' + re.escape(term.lower()) + r'\b'
|
230 |
+
if re.search(pattern, clause_lower):
|
231 |
+
found.append(term.lower())
|
232 |
+
return list(set(found))
|
233 |
+
|
234 |
+
def classify_nutrition_in_query(query, nutrition_dict, misspellings_dict):
|
235 |
+
include_nutrition = []
|
236 |
+
exclude_nutrition = []
|
237 |
+
clauses = re.split(r'\band\b|,|but', query, flags=re.IGNORECASE)
|
238 |
+
overall_intent = "exclude" if re.search(r'sensitivity|allergy|exclude', query, flags=re.IGNORECASE) else "include"
|
239 |
+
for clause in clauses:
|
240 |
+
clause = clause.strip()
|
241 |
+
if not clause:
|
242 |
+
continue
|
243 |
+
intent = "include" if "i want" in clause.lower() else overall_intent
|
244 |
+
numbers = re.findall(r'\d+(?:\.\d+)?', clause)
|
245 |
+
threshold = float(numbers[0]) if numbers else None
|
246 |
+
if re.search(r'\b(high|over|above|more than|exceeding)\b', clause, flags=re.IGNORECASE):
|
247 |
+
modifier = "high"
|
248 |
+
elif re.search(r'\b(low|under|less than|below)\b', clause, flags=re.IGNORECASE):
|
249 |
+
modifier = "low"
|
250 |
+
else:
|
251 |
+
modifier = "high" if intent == "exclude" else "low"
|
252 |
+
terms_found = extract_nutrition_from_clause(clause, nutrition_dict, misspellings_dict)
|
253 |
+
for term in terms_found:
|
254 |
+
norm_term = nutrition_terms_dictionary.get(term, term)
|
255 |
+
condition = (modifier, norm_term, threshold) if threshold is not None else (modifier, norm_term)
|
256 |
+
if intent == "include":
|
257 |
+
include_nutrition.append(condition)
|
258 |
+
elif intent == "exclude":
|
259 |
+
exclude_nutrition.append(condition)
|
260 |
+
return list(set(include_nutrition)), list(set(exclude_nutrition))
|
261 |
+
|
262 |
+
nutrition_terms_dictionary = {
|
263 |
+
"calorie": "calories",
|
264 |
+
"calories": "calories",
|
265 |
+
"fat": "Total fats",
|
266 |
+
"fats": "Total fats",
|
267 |
+
"total fat": "Total fats",
|
268 |
+
"total fats": "Total fats",
|
269 |
+
"carb": "Carbohydrate",
|
270 |
+
"carbs": "Carbohydrate",
|
271 |
+
"carbo": "Carbohydrate",
|
272 |
+
"carbohydrate": "Carbohydrate",
|
273 |
+
"carbohydrates": "Carbohydrate",
|
274 |
+
"fiber": "Fiber",
|
275 |
+
"protein": "Protein",
|
276 |
+
"proteins": "Protein",
|
277 |
+
"cholesterol": "Cholesterol",
|
278 |
+
"calcium": "Calcium",
|
279 |
+
"iron": "Iron",
|
280 |
+
"magnesium": "Magnesium",
|
281 |
+
"potassium": "Potassium",
|
282 |
+
"sodium": "Sodium",
|
283 |
+
"vitamin c": "Vitamin C"
|
284 |
+
}
|
285 |
+
|
286 |
+
fixed_thresholds = {
|
287 |
+
"calories": 700,
|
288 |
+
"Total fats": 60,
|
289 |
+
"Carbohydrate": 120,
|
290 |
+
"Fiber": 10,
|
291 |
+
"Protein": 30,
|
292 |
+
"Cholesterol": 100,
|
293 |
+
"Calcium": 300,
|
294 |
+
"Iron": 5,
|
295 |
+
"Magnesium": 100,
|
296 |
+
"Potassium": 300,
|
297 |
+
"Sodium": 400,
|
298 |
+
"Vitamin C": 50
|
299 |
+
}
|
300 |
+
|
301 |
+
def filter_by_nutrition_condition(df, condition):
|
302 |
+
if isinstance(condition, tuple):
|
303 |
+
if len(condition) == 3:
|
304 |
+
direction, nutrition_term, threshold = condition
|
305 |
+
elif len(condition) == 2:
|
306 |
+
direction, nutrition_term = condition
|
307 |
+
threshold = fixed_thresholds.get(nutrition_term)
|
308 |
+
else:
|
309 |
+
return df
|
310 |
+
column = nutrition_term
|
311 |
+
if column is None or threshold is None:
|
312 |
+
return df
|
313 |
+
if direction == "low":
|
314 |
+
return df[df[column] < threshold]
|
315 |
+
elif direction == "high":
|
316 |
+
return df[df[column] >= threshold]
|
317 |
+
return df
|
318 |
+
|
319 |
+
def score_recipe_ingredients(recipe_ingredients, include_list):
|
320 |
+
recipe_lower = recipe_ingredients.lower()
|
321 |
+
match_count = sum(
|
322 |
+
1 for ingredient in include_list
|
323 |
+
if ingredient.lower() in recipe_lower
|
324 |
+
)
|
325 |
+
return match_count
|
326 |
+
|
327 |
+
def filter_and_rank_recipes(df, include_list, exclude_list, include_nutrition, exclude_nutrition):
|
328 |
+
filtered_df = df.copy()
|
329 |
+
print(f"Starting with {len(filtered_df)} recipes for filtering")
|
330 |
+
if include_list:
|
331 |
+
filtered_df['ingredient_match_count'] = filtered_df['ingredients'].apply(
|
332 |
+
lambda x: score_recipe_ingredients(str(x), include_list)
|
333 |
+
)
|
334 |
+
filtered_df = filtered_df[filtered_df['ingredient_match_count'] >= 2]
|
335 |
+
print(f"After requiring at least 2 included ingredients: {len(filtered_df)} recipes remain")
|
336 |
+
for ingredient in exclude_list:
|
337 |
+
before_count = len(filtered_df)
|
338 |
+
filtered_df = filtered_df[
|
339 |
+
~filtered_df['ingredients']
|
340 |
+
.str.lower()
|
341 |
+
.fillna('')
|
342 |
+
.str.contains(re.escape(ingredient.lower()))
|
343 |
+
]
|
344 |
+
print(f"After excluding '{ingredient}': {len(filtered_df)} recipes remain (removed {before_count - len(filtered_df)})")
|
345 |
+
for i, cond in enumerate(include_nutrition):
|
346 |
+
before_count = len(filtered_df)
|
347 |
+
filtered_df = filter_by_nutrition_condition(filtered_df, cond)
|
348 |
+
after_count = len(filtered_df)
|
349 |
+
print(f"After applying nutrition condition {i+1} (include) '{cond}': {after_count} recipes remain (removed {before_count - after_count})")
|
350 |
+
for i, cond in enumerate(exclude_nutrition):
|
351 |
+
before_count = len(filtered_df)
|
352 |
+
temp_df = filter_by_nutrition_condition(df.copy(), cond)
|
353 |
+
filtered_df = filtered_df[~filtered_df.index.isin(temp_df.index)]
|
354 |
+
after_count = len(filtered_df)
|
355 |
+
print(f"After applying nutrition condition {i+1} (exclude) '{cond}': {after_count} recipes remain (removed {before_count - after_count})")
|
356 |
+
if filtered_df.empty:
|
357 |
+
print("\nNo recipes match all criteria. Implementing fallback approach...")
|
358 |
+
fallback_df = df.copy()
|
359 |
+
if include_list:
|
360 |
+
fallback_df['ingredient_match_count'] = fallback_df['ingredients'].apply(
|
361 |
+
lambda x: score_recipe_ingredients(str(x), include_list)
|
362 |
+
)
|
363 |
+
fallback_df = fallback_df[fallback_df['ingredient_match_count'] >= 1]
|
364 |
+
else:
|
365 |
+
fallback_df['ingredient_match_count'] = 1
|
366 |
+
for ingredient in exclude_list:
|
367 |
+
fallback_df = fallback_df[
|
368 |
+
~fallback_df['ingredients']
|
369 |
+
.str.lower()
|
370 |
+
.fillna('')
|
371 |
+
.str.contains(re.escape(ingredient.lower()))
|
372 |
+
]
|
373 |
+
if fallback_df.empty:
|
374 |
+
fallback_df = df.sample(min(5, len(df)))
|
375 |
+
fallback_df['ingredient_match_count'] = 0
|
376 |
+
print("No matches found. Showing random recipes as a fallback")
|
377 |
+
filtered_df = fallback_df
|
378 |
+
if 'ingredient_match_count' not in filtered_df.columns:
|
379 |
+
filtered_df['ingredient_match_count'] = 0
|
380 |
+
filtered_df = filtered_df.sort_values('ingredient_match_count', ascending=False)
|
381 |
+
return filtered_df
|
382 |
+
|
383 |
+
def get_disease_recommendations(user_text, disease_mapping_df):
|
384 |
+
user_text_lower = user_text.lower()
|
385 |
+
matches = disease_mapping_df[disease_mapping_df['Disease'].apply(lambda d: d in user_text_lower)]
|
386 |
+
if not matches.empty:
|
387 |
+
disease_info = matches.iloc[0]
|
388 |
+
def safe_parse_list(x):
|
389 |
+
if isinstance(x, str):
|
390 |
+
try:
|
391 |
+
return ast.literal_eval(x)
|
392 |
+
except:
|
393 |
+
return [item.strip() for item in x.split(',') if item.strip()]
|
394 |
+
return x
|
395 |
+
best_foods = safe_parse_list(disease_info.get("Best_Foods", "[]"))
|
396 |
+
worst_foods = safe_parse_list(disease_info.get("Worst_Foods", "[]"))
|
397 |
+
best_nutrition = safe_parse_list(disease_info.get("Best_Nutrition", "[]"))
|
398 |
+
worst_nutrition = safe_parse_list(disease_info.get("Worst_Nutrition", "[]"))
|
399 |
+
recommendations = {
|
400 |
+
"Disease": disease_info['Disease'],
|
401 |
+
"Best_Foods": best_foods,
|
402 |
+
"Worst_Foods": worst_foods,
|
403 |
+
"Best_Nutrition": best_nutrition,
|
404 |
+
"Worst_Nutrition": worst_nutrition
|
405 |
+
}
|
406 |
+
return recommendations
|
407 |
+
return None
|
408 |
+
|
409 |
+
def get_recipe_output(recipe_row):
|
410 |
+
recipe_name = recipe_row['title']
|
411 |
+
ner_info = recipe_row.get('NER', '')
|
412 |
+
try:
|
413 |
+
ner_list = json.loads(ner_info)
|
414 |
+
ner_str = ", ".join(ner_list)
|
415 |
+
except Exception:
|
416 |
+
ner_str = ner_info
|
417 |
+
nutrition_details = {col: float(recipe_row[col]) for col in nutrition_columns}
|
418 |
+
result = {
|
419 |
+
"Meal name": recipe_name,
|
420 |
+
"NER": ner_str,
|
421 |
+
"Nutrition details": nutrition_details
|
422 |
+
}
|
423 |
+
print(f"Meal name: {recipe_name}")
|
424 |
+
print(f"NER: {ner_str}")
|
425 |
+
print(f"Nutrition details: {nutrition_details}")
|
426 |
+
return result
|
427 |
+
|
428 |
+
def process_long_query(query):
|
429 |
+
if len(query.split()) > 500:
|
430 |
+
print("Long input detected. Summarizing...")
|
431 |
+
query = summarize_input(query)
|
432 |
+
print(f"Processed Query: \"{query}\"")
|
433 |
+
corrected = smart_correct_spelling(query, domain_words)
|
434 |
+
sentences = sent_tokenize(corrected)
|
435 |
+
aggregated_include = []
|
436 |
+
aggregated_exclude = []
|
437 |
+
aggregated_include_nutrition = []
|
438 |
+
aggregated_exclude_nutrition = []
|
439 |
+
for sentence in sentences:
|
440 |
+
inc, exc = classify_ingredients_in_query(sentence, food_dictionary, common_misspellings)
|
441 |
+
aggregated_include.extend(inc)
|
442 |
+
aggregated_exclude.extend(exc)
|
443 |
+
inc_nut, exc_nut = classify_nutrition_in_query(sentence, list(nutrition_terms_dictionary.keys()), common_misspellings)
|
444 |
+
aggregated_include_nutrition.extend(inc_nut)
|
445 |
+
aggregated_exclude_nutrition.extend(exc_nut)
|
446 |
+
return corrected, list(set(aggregated_include)), list(set(aggregated_exclude)), \
|
447 |
+
list(set(aggregated_include_nutrition)), list(set(aggregated_exclude_nutrition))
|
448 |
+
|
449 |
+
def send_to_api(meal_data, parent_id):
|
450 |
+
try:
|
451 |
+
api_endpoint = "http://54.242.19.19:3000/api/ResturantMenu/add"
|
452 |
+
meal_id = random.randint(1000, 9999)
|
453 |
+
meal_name = meal_data.get("Meal name", "No meal name available")
|
454 |
+
ner_info = meal_data.get("NER", "")
|
455 |
+
images_public = "https://kero.beshoy.me/recipe_images/"
|
456 |
+
image_path = True
|
457 |
+
image_url = ""
|
458 |
+
if image_path:
|
459 |
+
try:
|
460 |
+
image_url = images_public + quote(meal_name, safe="") + ".jpg"
|
461 |
+
print(f"Successfully uploaded image to the server for {meal_name}: {image_url}")
|
462 |
+
except Exception as cl_err:
|
463 |
+
print(f"Error uploading to the server: {cl_err}")
|
464 |
+
if not image_url:
|
465 |
+
image_url = "https://picsum.photos/200"
|
466 |
+
payload = {
|
467 |
+
"id": str(meal_id),
|
468 |
+
"name": meal_name,
|
469 |
+
"description": ner_info,
|
470 |
+
"photo": image_url,
|
471 |
+
"parentId": parent_id
|
472 |
+
}
|
473 |
+
print(f"\nSending payload to API: {payload}")
|
474 |
+
response = requests.post(api_endpoint, json=payload)
|
475 |
+
print(f"API Response for meal {meal_name}: {response.status_code}")
|
476 |
+
|
477 |
+
try:
|
478 |
+
return response.json()
|
479 |
+
except Exception:
|
480 |
+
return {"error": response.text}
|
481 |
+
except Exception as e:
|
482 |
+
print(f"Error sending meal to API: {e}")
|
483 |
+
return {"error": str(e)}
|
484 |
+
|
485 |
+
app = Flask(__name__)
|
486 |
+
@app.route('/process', methods=['POST'])
|
487 |
+
def process():
|
488 |
+
try:
|
489 |
+
|
490 |
+
input_text = ""
|
491 |
+
parent_id = ""
|
492 |
+
|
493 |
+
if request.is_json:
|
494 |
+
|
495 |
+
data = request.json
|
496 |
+
input_text = data.get("description", "")
|
497 |
+
parent_id = data.get("parentId", "")
|
498 |
+
|
499 |
+
if not input_text:
|
500 |
+
return jsonify({"error": "Missing description in request"}), 400
|
501 |
+
if not parent_id:
|
502 |
+
return jsonify({"error": "Missing parentId in request"}), 400
|
503 |
+
|
504 |
+
else:
|
505 |
+
|
506 |
+
input_text_json = request.form
|
507 |
+
input_text = input_text_json.get("description", "")
|
508 |
+
parent_id = input_text_json.get("parentId", "")
|
509 |
+
|
510 |
+
if not input_text:
|
511 |
+
return jsonify({"error": "Missing description in request"}), 400
|
512 |
+
if not parent_id:
|
513 |
+
return jsonify({"error": "Missing parentId in request"}), 400
|
514 |
+
|
515 |
+
print("WARNING: Using raw data format. Please consider using JSON format.")
|
516 |
+
|
517 |
+
raw_input_text = input_text
|
518 |
+
processed_input, user_include, user_exclude, user_include_nutrition, user_exclude_nutrition = process_long_query(raw_input_text)
|
519 |
+
|
520 |
+
include_list, exclude_list = [], []
|
521 |
+
include_nutrition, exclude_nutrition = [], []
|
522 |
+
|
523 |
+
disease_recs = get_disease_recommendations(processed_input, disease_df)
|
524 |
+
|
525 |
+
if disease_recs:
|
526 |
+
print("\nDisease-related Recommendations Detected:")
|
527 |
+
print(f"Disease: {disease_recs['Disease']}")
|
528 |
+
print(f"Best Foods: {disease_recs['Best_Foods']}")
|
529 |
+
print(f"Worst Foods: {disease_recs['Worst_Foods']}")
|
530 |
+
print(f"Best Nutrition: {disease_recs['Best_Nutrition']}")
|
531 |
+
print(f"Worst Nutrition: {disease_recs['Worst_Nutrition']}")
|
532 |
+
|
533 |
+
include_list.extend(disease_recs["Best_Foods"])
|
534 |
+
exclude_list.extend(disease_recs["Worst_Foods"])
|
535 |
+
|
536 |
+
def parse_nutrition_condition(nutrition_phrase):
|
537 |
+
parts = nutrition_phrase.strip().split()
|
538 |
+
if len(parts) == 2:
|
539 |
+
direction = parts[0].lower()
|
540 |
+
nutrient = parts[1].lower()
|
541 |
+
mapped_nutrient = nutrition_terms_dictionary.get(nutrient, nutrient)
|
542 |
+
return (direction, mapped_nutrient)
|
543 |
+
return None
|
544 |
+
|
545 |
+
for bn in disease_recs["Best_Nutrition"]:
|
546 |
+
cond = parse_nutrition_condition(bn)
|
547 |
+
if cond:
|
548 |
+
include_nutrition.append(cond)
|
549 |
+
for wn in disease_recs["Worst_Nutrition"]:
|
550 |
+
cond = parse_nutrition_condition(wn)
|
551 |
+
if cond:
|
552 |
+
exclude_nutrition.append(cond)
|
553 |
+
|
554 |
+
include_list.extend(user_include)
|
555 |
+
exclude_list.extend(user_exclude)
|
556 |
+
include_nutrition.extend(user_include_nutrition)
|
557 |
+
exclude_nutrition.extend(user_exclude_nutrition)
|
558 |
+
|
559 |
+
include_list = list(set(include_list))
|
560 |
+
exclude_list = list(set(exclude_list))
|
561 |
+
include_nutrition = list(set(include_nutrition))
|
562 |
+
exclude_nutrition = list(set(exclude_nutrition))
|
563 |
+
|
564 |
+
print("\nFinal Lists After Combining Disease + User Query:")
|
565 |
+
print(f"Ingredients to include: {include_list}")
|
566 |
+
print(f"Ingredients to exclude: {exclude_list}")
|
567 |
+
print(f"Nutrition conditions to include: {include_nutrition}")
|
568 |
+
print(f"Nutrition conditions to exclude: {exclude_nutrition}")
|
569 |
+
|
570 |
+
corrected_include = [correct_food_ingredient(ingredient, food_dictionary, common_misspellings) for ingredient in include_list]
|
571 |
+
corrected_exclude = [correct_food_ingredient(ingredient, food_dictionary, common_misspellings) for ingredient in exclude_list]
|
572 |
+
|
573 |
+
include_list = list(set(corrected_include))
|
574 |
+
exclude_list = list(set(corrected_exclude))
|
575 |
+
filtered_df = filter_and_rank_recipes(
|
576 |
+
df,
|
577 |
+
include_list,
|
578 |
+
exclude_list,
|
579 |
+
include_nutrition,
|
580 |
+
exclude_nutrition
|
581 |
+
)
|
582 |
+
|
583 |
+
final_output = {}
|
584 |
+
api_responses = []
|
585 |
+
|
586 |
+
if not filtered_df.empty:
|
587 |
+
filtered_df = filtered_df.sample(frac=1)
|
588 |
+
meal_count = min(6, len(filtered_df))
|
589 |
+
|
590 |
+
for i in range(meal_count):
|
591 |
+
if i == 0:
|
592 |
+
print("\nRecommended Meal:")
|
593 |
+
meal_data = get_recipe_output(filtered_df.iloc[i])
|
594 |
+
final_output["Recommended Meal"] = meal_data
|
595 |
+
else:
|
596 |
+
print(f"\nOption {i}:")
|
597 |
+
meal_data = get_recipe_output(filtered_df.iloc[i])
|
598 |
+
final_output[f"Option {i}"] = meal_data
|
599 |
+
|
600 |
+
api_response = send_to_api(meal_data, parent_id)
|
601 |
+
api_responses.append(api_response)
|
602 |
+
else:
|
603 |
+
error_message = f"No recipes found that match your criteria.\nIngredients to include: {', '.join(include_list)}\nIngredients to exclude: {', '.join(exclude_list)}\nNutrition Include: {', '.join(str(cond) for cond in include_nutrition)}\nNutrition Exclude: {', '.join(str(cond) for cond in exclude_nutrition)}."
|
604 |
+
print(error_message)
|
605 |
+
final_output["Message"] = error_message
|
606 |
+
return jsonify({"error": error_message}), 404
|
607 |
+
|
608 |
+
return jsonify({
|
609 |
+
"original_response": final_output,
|
610 |
+
"api_responses": api_responses,
|
611 |
+
"message": f"Successfully processed {len(api_responses)} meals"
|
612 |
+
})
|
613 |
+
|
614 |
+
except Exception as e:
|
615 |
+
print(f"Error processing request: {str(e)}")
|
616 |
+
return jsonify({"error": f"Internal server error: {str(e)}"}), 500
|
617 |
+
|
618 |
+
|
619 |
+
if __name__ == '__main__':
|
620 |
+
port = int(os.environ.get("PORT", 7860))
|
621 |
+
app.run(host="0.0.0.0", port=port, debug=False)
|
docs/common_ingredients.json
ADDED
@@ -0,0 +1,68 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
[
|
2 |
+
"sugar", "salt", "flour", "milk", "eggs", "butter", "oil", "water", "garlic", "onion",
|
3 |
+
"tomato", "cheese", "chicken", "beef", "pork", "rice", "pasta", "potato", "carrot", "celery",
|
4 |
+
"pepper", "cinnamon", "vanilla", "chocolate", "nuts", "almonds", "peanuts", "wheat", "gluten", "soy",
|
5 |
+
"corn", "fish", "shellfish", "shrimp", "crab", "lobster", "coconut", "banana", "apple", "orange",
|
6 |
+
"lemon", "lime", "strawberry", "blueberry", "raspberry", "blackberry", "honey", "maple syrup", "mustard", "ketchup",
|
7 |
+
"mayonnaise", "vinegar", "wine", "beer", "basil", "oregano", "thyme", "rosemary", "parsley", "cilantro",
|
8 |
+
"mint", "cumin", "paprika", "ginger", "turmeric", "avocado", "broccoli", "cauliflower", "spinach", "kale",
|
9 |
+
"lettuce", "cucumber", "zucchini", "eggplant", "mushroom", "bacon", "ham", "sausage", "turkey", "lamb",
|
10 |
+
"tofu", "beans", "lentils", "chickpeas", "quinoa", "oats", "barley", "yogurt", "cream", "cream cheese",
|
11 |
+
"sour cream", "cheddar", "mozzarella", "parmesan", "olive oil", "vegetable oil", "canola oil", "coconut oil",
|
12 |
+
"sesame oil", "baking powder", "baking soda", "yeast", "brown sugar", "powdered sugar", "confectioners sugar",
|
13 |
+
"cane sugar", "granulated sugar", "molasses", "agave", "stevia", "honey", "corn syrup", "almond milk",
|
14 |
+
"soy milk", "oat milk", "coconut milk", "cashew milk", "heavy cream", "half and half", "buttermilk",
|
15 |
+
"condensed milk", "evaporated milk", "whole milk", "skim milk", "ricotta", "cottage cheese", "blue cheese",
|
16 |
+
"goat cheese", "feta", "brie", "camembert", "gruyere", "manchego", "gouda", "provolone", "swiss cheese",
|
17 |
+
"pecans", "walnuts", "pistachios", "cashews", "hazelnuts", "macadamia nuts", "peanut butter", "almond butter",
|
18 |
+
"cashew butter", "tahini", "sunflower seeds", "pumpkin seeds", "chia seeds", "flax seeds", "sesame seeds",
|
19 |
+
"poppy seeds", "hemp seeds", "pine nuts", "white rice", "brown rice", "jasmine rice", "basmati rice",
|
20 |
+
"arborio rice", "wild rice", "sushi rice", "wheat flour", "all-purpose flour", "bread flour", "cake flour",
|
21 |
+
"pastry flour", "whole wheat flour", "almond flour", "coconut flour", "rice flour", "semolina", "cornmeal",
|
22 |
+
"corn flour", "tapioca flour", "potato starch", "cornstarch", "arrowroot", "black pepper", "white pepper",
|
23 |
+
"cayenne pepper", "red pepper flakes", "chili powder", "curry powder", "cardamom", "cloves", "nutmeg",
|
24 |
+
"allspice", "star anise", "bay leaf", "saffron", "fennel", "coriander", "mustard seeds", "anise",
|
25 |
+
"tarragon", "dill", "chives", "sage", "marjoram", "lemongrass", "black beans", "kidney beans", "pinto beans",
|
26 |
+
"navy beans", "lima beans", "fava beans", "green beans", "chickpeas", "split peas", "black-eyed peas",
|
27 |
+
"edamame", "mung beans", "red lentils", "green lentils", "brown lentils", "yellow lentils", "white potatoes",
|
28 |
+
"red potatoes", "sweet potatoes", "russet potatoes", "fingerling potatoes", "yukon gold potatoes", "yams",
|
29 |
+
"cabbage", "red cabbage", "napa cabbage", "brussels sprouts", "bok choy", "asparagus", "artichoke",
|
30 |
+
"bell pepper", "red bell pepper", "green bell pepper", "yellow bell pepper", "jalapeno", "habanero",
|
31 |
+
"serrano", "poblano", "chipotle", "cherry tomatoes", "roma tomatoes", "grape tomatoes", "heirloom tomatoes",
|
32 |
+
"sun-dried tomatoes", "tomato paste", "tomato sauce", "tomato puree", "ketchup", "salsa", "red onion",
|
33 |
+
"white onion", "yellow onion", "green onion", "scallion", "shallot", "leek", "chive", "pumpkin",
|
34 |
+
"butternut squash", "acorn squash", "spaghetti squash", "delicata squash", "kabocha squash", "radish",
|
35 |
+
"daikon", "turnip", "rutabaga", "beet", "fennel bulb", "jicama", "okra", "corn", "baby corn", "hominy",
|
36 |
+
"apple cider vinegar", "balsamic vinegar", "red wine vinegar", "white wine vinegar", "rice vinegar",
|
37 |
+
"malt vinegar", "sherry vinegar", "champagne vinegar", "distilled vinegar", "white vinegar", "red wine",
|
38 |
+
"white wine", "rose wine", "champagne", "prosecco", "vodka", "rum", "whiskey", "bourbon", "tequila",
|
39 |
+
"brandy", "cognac", "gin", "vermouth", "sake", "ale", "lager", "stout", "porter", "pilsner",
|
40 |
+
"wheat beer", "cider", "coffee", "espresso", "instant coffee", "green tea", "black tea", "herbal tea",
|
41 |
+
"chai tea", "matcha", "cocoa powder", "dark chocolate", "milk chocolate", "white chocolate", "chocolate chips",
|
42 |
+
"candy", "caramel", "toffee", "marshmallow", "gelatin", "agar agar", "sprinkles", "food coloring",
|
43 |
+
"vanilla extract", "almond extract", "lemon extract", "mint extract", "orange extract", "rum extract",
|
44 |
+
"peppermint extract", "coconut extract", "maple extract", "rose water", "orange flower water", "miso",
|
45 |
+
"soy sauce", "tamari", "hoisin sauce", "oyster sauce", "fish sauce", "teriyaki sauce", "sriracha",
|
46 |
+
"tabasco", "worcestershire sauce", "hot sauce", "barbecue sauce", "pizza sauce", "pesto", "hummus",
|
47 |
+
"guacamole", "salsa", "relish", "pickle", "sauerkraut", "kimchi", "olives", "capers", "anchovies",
|
48 |
+
"peaches", "pears", "plums", "cherries", "apricots", "nectarines", "grapes", "watermelon", "cantaloupe",
|
49 |
+
"honeydew", "kiwi", "pomegranate", "mango", "papaya", "pineapple", "passion fruit", "dragon fruit",
|
50 |
+
"raisins", "dried cranberries", "dried apricots", "dried dates", "dried figs", "prunes", "grapefruit",
|
51 |
+
"cranberries", "gooseberries", "currants", "fig", "durian", "lychee", "star fruit", "persimmon",
|
52 |
+
"ground beef", "ground turkey", "ground chicken", "ground pork", "ground lamb", "steak", "filet mignon",
|
53 |
+
"sirloin", "ribeye", "flank steak", "skirt steak", "brisket", "short ribs", "pork chops", "pork tenderloin",
|
54 |
+
"pork belly", "ham hock", "chicken breast", "chicken thigh", "chicken wing", "chicken leg", "whole chicken",
|
55 |
+
"turkey breast", "turkey leg", "duck", "duck breast", "goose", "lamb chop", "leg of lamb", "rack of lamb",
|
56 |
+
"veal", "liver", "foie gras", "tuna", "salmon", "cod", "haddock", "tilapia", "sea bass", "trout",
|
57 |
+
"halibut", "mackerel", "sardines", "herring", "anchovy", "catfish", "mahi mahi", "swordfish", "squid",
|
58 |
+
"octopus", "clams", "mussels", "oysters", "scallops", "crayfish", "langoustine", "eel", "caviar",
|
59 |
+
"roe", "seaweed", "nori", "kombu", "wakame", "dulse", "hijiki", "macaroni", "spaghetti", "fettuccine",
|
60 |
+
"linguine", "penne", "rigatoni", "farfalle", "orzo", "lasagna", "tortellini", "ravioli", "gnocchi",
|
61 |
+
"couscous", "bulgur", "farro", "polenta", "grits", "croissant", "baguette", "brioche", "sourdough",
|
62 |
+
"rye bread", "pumpernickel", "naan", "pita", "focaccia", "ciabatta", "breadcrumbs", "panko", "crackers",
|
63 |
+
"matzo", "tortilla", "phyllo dough", "puff pastry", "pie crust", "graham cracker", "wonton wrapper",
|
64 |
+
"dumpling wrapper", "egg roll wrapper", "rice paper", "udon", "soba", "ramen", "rice noodles", "egg noodles",
|
65 |
+
"glass noodles", "vermicelli", "fusilli", "rotini", "risotto", "shortening", "lard", "suet", "ghee",
|
66 |
+
"margarine", "mayonnaise", "miracle whip", "gravy", "croutons", "stuffing", "nougat", "zwieback", "custard",
|
67 |
+
"pudding", "jam", "jelly", "marmalade", "preserves", "compote", "curd", "chutney"
|
68 |
+
]
|
docs/common_misspellings.json
ADDED
@@ -0,0 +1,454 @@
|
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|
1 |
+
{
|
2 |
+
"added sugar or corn syrup": "corn syrup",
|
3 |
+
"advocado": "avocado",
|
4 |
+
"advocados": "avocado",
|
5 |
+
"almond mylk": "almond milk",
|
6 |
+
"almonds": "almond",
|
7 |
+
"and": "candy",
|
8 |
+
"armour lower salt bacon": "\"3 slices armour lower salt bacon\"",
|
9 |
+
"aspargus": "asparagus",
|
10 |
+
"aubergine": "eggplant",
|
11 |
+
"avacado": "avocado",
|
12 |
+
"avacados": "avocado",
|
13 |
+
"avocados": "avocado",
|
14 |
+
"baby pearl tapioca": "\" baby pearl tapioca\"",
|
15 |
+
"baccon": "bacon",
|
16 |
+
"bacn": "bacon",
|
17 |
+
"baking potatoes": "5 baking potatoes\"",
|
18 |
+
"bakon": "bacon",
|
19 |
+
"bannana": "banana",
|
20 |
+
"bannanas": "banana",
|
21 |
+
"basil": "basil",
|
22 |
+
"bazil": "basil",
|
23 |
+
"beaf": "beef",
|
24 |
+
"because": "beans",
|
25 |
+
"beef": "beef",
|
26 |
+
"beens": "beans",
|
27 |
+
"beff": "beef",
|
28 |
+
"bewries": "berries",
|
29 |
+
"bewwies": "berries",
|
30 |
+
"bicarbonate of soda": "baking soda",
|
31 |
+
"blak pepper": "black pepper",
|
32 |
+
"blu berries": "blueberries",
|
33 |
+
"bluberries": "blueberries",
|
34 |
+
"blue beries": "blueberries",
|
35 |
+
"blueberreis": "blueberries",
|
36 |
+
"blueberrys": "blueberries",
|
37 |
+
"blueburys": "blueberries",
|
38 |
+
"boc choi": "bok choy",
|
39 |
+
"bokchoy": "bok choy",
|
40 |
+
"boneless rump": "[\"1 1/ boneless rump\"",
|
41 |
+
"bowtie macaroni": "\"bowtie macaroni\"]",
|
42 |
+
"bracoli": "broccoli",
|
43 |
+
"bread": "bread",
|
44 |
+
"bred": "bread",
|
45 |
+
"brockley": "broccoli",
|
46 |
+
"brocli": "broccoli",
|
47 |
+
"brocoli": "broccoli",
|
48 |
+
"brocolli": "broccoli",
|
49 |
+
"brok": "broccoli",
|
50 |
+
"bromato": "tomato",
|
51 |
+
"brown suger": "brown sugar",
|
52 |
+
"brownie mix": "\"1 pkg. brownie mix\"",
|
53 |
+
"brussel sprouts": "brussels sprouts",
|
54 |
+
"brussell sprouts": "brussels sprouts",
|
55 |
+
"brussels": "mussels",
|
56 |
+
"budr": "butter",
|
57 |
+
"bulgur wheat": "\"1 c. bulgur wheat\"",
|
58 |
+
"buter": "butter",
|
59 |
+
"butr": "butter",
|
60 |
+
"cabij": "cabbage",
|
61 |
+
"cacoa": "cocoa",
|
62 |
+
"calorie": "calories",
|
63 |
+
"camomile": "chamomile",
|
64 |
+
"canned soup": "canned tomato soup\"",
|
65 |
+
"capsicm": "capsicum",
|
66 |
+
"capsikcum": "capsicum",
|
67 |
+
"carb": "crab",
|
68 |
+
"carbs": "capers",
|
69 |
+
"carot": "carrot",
|
70 |
+
"carrats": "carrot",
|
71 |
+
"carrit": "carrot",
|
72 |
+
"carrott": "carrot",
|
73 |
+
"carrts": "carrot",
|
74 |
+
"carumom": "cardamom",
|
75 |
+
"caulflower": "cauliflower",
|
76 |
+
"cayane": "cayenne",
|
77 |
+
"cayanne": "cayenne",
|
78 |
+
"cayene": "cayenne",
|
79 |
+
"cellary": "celery",
|
80 |
+
"cellery": "celery",
|
81 |
+
"center": "\"centers",
|
82 |
+
"chalot": "shallot",
|
83 |
+
"chas": "cheese",
|
84 |
+
"chciken": "chicken",
|
85 |
+
"chease": "cheese",
|
86 |
+
"chedar": "cheddar",
|
87 |
+
"chedar cheese": "cheddar cheese",
|
88 |
+
"cheeze": "cheese",
|
89 |
+
"chekin": "chicken",
|
90 |
+
"cheries": "cherries",
|
91 |
+
"cherys": "cherries",
|
92 |
+
"ches": "cheese",
|
93 |
+
"chese": "cheese",
|
94 |
+
"chesee": "cheese",
|
95 |
+
"chestnuts": "chestnut",
|
96 |
+
"chi peas": "chickpeas",
|
97 |
+
"chic pea": "chickpea",
|
98 |
+
"chic peas": "chickpeas",
|
99 |
+
"chicken drummettes": "\"chicken drummettes\"",
|
100 |
+
"chicken livers": "[\"chicken livers\"",
|
101 |
+
"chiili": "chili",
|
102 |
+
"chiken": "chicken",
|
103 |
+
"chili peppers": "chili pepper",
|
104 |
+
"chilli": "chili",
|
105 |
+
"chilli flakes": "chili flakes",
|
106 |
+
"chilli pepper": "chili pepper",
|
107 |
+
"chipolte": "chipotle",
|
108 |
+
"chipotlay": "chipotle",
|
109 |
+
"chives": "chive",
|
110 |
+
"chli flakes": "chili flakes",
|
111 |
+
"choclate": "chocolate",
|
112 |
+
"choclit": "chocolate",
|
113 |
+
"chocolat": "chocolate",
|
114 |
+
"chocolote": "chocolate",
|
115 |
+
"choklit": "chocolate",
|
116 |
+
"chokolate": "chocolate",
|
117 |
+
"chpd": "chopped",
|
118 |
+
"cider vinegar": "\" cider vinegar\"",
|
119 |
+
"cinammon": "cinnamon",
|
120 |
+
"cinamon": "cinnamon",
|
121 |
+
"cinimin": "cinnamon",
|
122 |
+
"cinimon": "cinnamon",
|
123 |
+
"cinnamin": "cinnamon",
|
124 |
+
"cinquetfoil": "cinquefoil",
|
125 |
+
"clery": "celery",
|
126 |
+
"cocnut": "coconut",
|
127 |
+
"coconut milk": "coconut milk",
|
128 |
+
"coconut millk": "coconut milk",
|
129 |
+
"cocunut": "coconut",
|
130 |
+
"cofee": "coffee",
|
131 |
+
"coffe": "coffee",
|
132 |
+
"cokies": "cookies",
|
133 |
+
"coliflower": "cauliflower",
|
134 |
+
"collander": "colander",
|
135 |
+
"confectionary sugar": "confectioners sugar",
|
136 |
+
"cooking oil": "\"cooking oil",
|
137 |
+
"cornflaur": "cornflour",
|
138 |
+
"corriander": "coriander",
|
139 |
+
"cream of mushroom soup": "cream of mushroom soup\"",
|
140 |
+
"crem": "cream",
|
141 |
+
"crisp": "\"crisp",
|
142 |
+
"crutons": "croutons",
|
143 |
+
"cucomber": "cucumber",
|
144 |
+
"cucumba": "cucumber",
|
145 |
+
"cucumbr": "cucumber",
|
146 |
+
"cucumbra": "cucumber",
|
147 |
+
"cumcumber": "cucumber",
|
148 |
+
"cummin": "cumin",
|
149 |
+
"curied": "curry",
|
150 |
+
"currents": "currants",
|
151 |
+
"cyanne": "cayenne",
|
152 |
+
"dijon mustard": "\"dijon mustard\"",
|
153 |
+
"dill": "dill",
|
154 |
+
"doctor": "octopus",
|
155 |
+
"doritos chips": "\"doritos chips\"",
|
156 |
+
"dry mustard": "dry mustard\"",
|
157 |
+
"egg": "eggs",
|
158 |
+
"egges": "eggs",
|
159 |
+
"egggs": "eggs",
|
160 |
+
"eggplnt": "eggplant",
|
161 |
+
"egs": "eggs",
|
162 |
+
"egz": "eggs",
|
163 |
+
"exra-virgin olive oil": "extra virgin olive oil",
|
164 |
+
"exrtra virgin olive oil": "extra virgin olive oil",
|
165 |
+
"fat": "fat\"",
|
166 |
+
"fats": "oats",
|
167 |
+
"fenel": "fennel",
|
168 |
+
"fennle": "fennel",
|
169 |
+
"fettucine noodles": "[\"fettucine noodles \"",
|
170 |
+
"finna": "fennel",
|
171 |
+
"flor": "flour",
|
172 |
+
"flore": "flour",
|
173 |
+
"flounder": "\"flounder",
|
174 |
+
"flour": "flour",
|
175 |
+
"flower": "flour",
|
176 |
+
"flowers with": "\"flower",
|
177 |
+
"fresh ground black pepper": "black pepper",
|
178 |
+
"fresh mushrooms": "mushrooms",
|
179 |
+
"frozen italian style vegetables": "frozen oriental-style vegetables\"",
|
180 |
+
"galic": "garlic",
|
181 |
+
"garlick": "garlic",
|
182 |
+
"garlicy": "garlic",
|
183 |
+
"garlik": "garlic",
|
184 |
+
"garllic": "garlic",
|
185 |
+
"gelatine": "gelatin",
|
186 |
+
"geletin": "gelatin",
|
187 |
+
"gellatine": "gelatin",
|
188 |
+
"ginjer": "ginger",
|
189 |
+
"gnocci": "gnocchi",
|
190 |
+
"gralic": "garlic",
|
191 |
+
"green beens": "green beans",
|
192 |
+
"green chile enchilada sauce": "\"1 can green enchilada sauce\"",
|
193 |
+
"green chillies": "green chilies\"",
|
194 |
+
"green pepper": "green peppers",
|
195 |
+
"ground beef": "beef",
|
196 |
+
"ground meat": "meat\"",
|
197 |
+
"hareesa": "harissa",
|
198 |
+
"hazel nuts": "hazelnuts",
|
199 |
+
"hazlenuts": "hazelnuts",
|
200 |
+
"hebs": "herbs",
|
201 |
+
"herbes": "herbs",
|
202 |
+
"herbz": "herbs",
|
203 |
+
"high": "thighs",
|
204 |
+
"hony": "honey",
|
205 |
+
"hut": "halibut",
|
206 |
+
"jalepano": "jalapeno",
|
207 |
+
"jalepeno": "jalapeno",
|
208 |
+
"jalepino": "jalapeno",
|
209 |
+
"jalopeno": "jalapeno",
|
210 |
+
"japapeno": "jalapeno",
|
211 |
+
"kale": "kale",
|
212 |
+
"katsup": "ketchup",
|
213 |
+
"ketcup": "ketchup",
|
214 |
+
"ketshup": "ketchup",
|
215 |
+
"ketsup": "ketchup",
|
216 |
+
"ladyfingers": "\"ladyfingers\"]",
|
217 |
+
"large red": "\"1 large red",
|
218 |
+
"leafy greens": "collard greens \"",
|
219 |
+
"lemmon": "lemon",
|
220 |
+
"lemmonds": "lemon",
|
221 |
+
"lemmons": "lemon",
|
222 |
+
"lentills": "lentils",
|
223 |
+
"less than 700 calories per serving": "calories",
|
224 |
+
"lettuice": "lettuce",
|
225 |
+
"lettus": "lettuce",
|
226 |
+
"letuce": "lettuce",
|
227 |
+
"lime": "lime",
|
228 |
+
"lime jell-o": "[\" lime jello\"",
|
229 |
+
"lime juice": "lime juice\"",
|
230 |
+
"limes": "lime",
|
231 |
+
"loin end pork": "[\"4 to loin end pork\"",
|
232 |
+
"long grain white rice": "\"2 c. long grain white rice\"",
|
233 |
+
"low": "orange flower water",
|
234 |
+
"lower than 20 g of fiber": "lower than of fiber",
|
235 |
+
"magarine": "margarine",
|
236 |
+
"magerine": "margarine",
|
237 |
+
"majarine": "margarine",
|
238 |
+
"manderine": "mandarin",
|
239 |
+
"mapel": "maple",
|
240 |
+
"mapple": "maple",
|
241 |
+
"margerine": "margarine",
|
242 |
+
"marj": "marjoram",
|
243 |
+
"marjaram": "marjoram",
|
244 |
+
"marjoram": "marjoram",
|
245 |
+
"marjorem": "marjoram",
|
246 |
+
"mayo": "mayonnaise",
|
247 |
+
"mayonaise": "mayonnaise",
|
248 |
+
"mayonaisse": "mayonnaise",
|
249 |
+
"mayonase": "mayonnaise",
|
250 |
+
"mayonese": "mayonnaise",
|
251 |
+
"mayonez": "mayonnaise",
|
252 |
+
"menth": "mint",
|
253 |
+
"miilk": "milk",
|
254 |
+
"milc": "milk",
|
255 |
+
"milks": "milk",
|
256 |
+
"mixed pickling spice": "\" mixed pickling spices\"]",
|
257 |
+
"mlik": "milk",
|
258 |
+
"mlik products": "milk",
|
259 |
+
"molk": "milk",
|
260 |
+
"mollases": "molasses",
|
261 |
+
"mollasses": "molasses",
|
262 |
+
"morton tender quick meat cure": "\"3 tbsp. morton tender quick meat cure\"]",
|
263 |
+
"mozarella": "mozzarella",
|
264 |
+
"mozzarela": "mozzarella",
|
265 |
+
"mozzerella": "mozzarella",
|
266 |
+
"mozzorella": "mozzarella",
|
267 |
+
"mushram": "mushroom",
|
268 |
+
"mushroms": "mushrooms",
|
269 |
+
"mushroomz": "mushrooms",
|
270 |
+
"mushrums": "mushrooms",
|
271 |
+
"must": "mustard",
|
272 |
+
"musterd": "mustard",
|
273 |
+
"my": "\"1 single serving size envelope instant creamy chicken soup mix\"",
|
274 |
+
"notmeg": "nutmeg",
|
275 |
+
"nut meg": "nutmeg",
|
276 |
+
"nutmag": "nutmeg",
|
277 |
+
"ocra": "okra",
|
278 |
+
"oil products": "oil",
|
279 |
+
"oile": "oil",
|
280 |
+
"olif oil": "olive oil",
|
281 |
+
"oliv oil": "olive oil",
|
282 |
+
"oliveoil": "olive oil",
|
283 |
+
"ollives": "olives",
|
284 |
+
"onin": "onion",
|
285 |
+
"onino": "onion",
|
286 |
+
"oninon": "onion",
|
287 |
+
"onions": "onions\"",
|
288 |
+
"onnion": "onion",
|
289 |
+
"onoin": "onion",
|
290 |
+
"oranje": "orange",
|
291 |
+
"oreg": "oregano",
|
292 |
+
"oriental chili sesame oil": "\"1/ oriental chili sesame oil\"",
|
293 |
+
"origano": "oregano",
|
294 |
+
"oring": "orange",
|
295 |
+
"orinj": "orange",
|
296 |
+
"over": "liver",
|
297 |
+
"oyl": "oil",
|
298 |
+
"panchetta": "pancetta",
|
299 |
+
"papreka": "paprika",
|
300 |
+
"paprica": "paprika",
|
301 |
+
"parley flakes": "parsley flakes\"",
|
302 |
+
"parmasean": "parmesan",
|
303 |
+
"parmazan": "parmesan",
|
304 |
+
"parmejan": "parmesan",
|
305 |
+
"parmesan cheese": "parmesan cheese\"",
|
306 |
+
"parmezon": "parmesan",
|
307 |
+
"parsely": "parsley",
|
308 |
+
"parsley": "parsley",
|
309 |
+
"parsli": "parsley",
|
310 |
+
"parsly": "parsley",
|
311 |
+
"patato": "potato",
|
312 |
+
"patatos": "potato",
|
313 |
+
"peans": "peas",
|
314 |
+
"pecan halves": "\"pecan halves\"]",
|
315 |
+
"peer": "pear",
|
316 |
+
"penut": "peanut",
|
317 |
+
"penut butter": "peanut butter",
|
318 |
+
"peper": "pepper",
|
319 |
+
"pepers": "peppers",
|
320 |
+
"peppr": "pepper",
|
321 |
+
"pettitoes": "pig's feet",
|
322 |
+
"pickels": "pickles",
|
323 |
+
"pimienta": "pimiento",
|
324 |
+
"pinaple": "pineapple",
|
325 |
+
"pineaple": "pineapple",
|
326 |
+
"plain yogurt": "plain yogurt\"",
|
327 |
+
"potatoe": "potato",
|
328 |
+
"potatoes": "potatoes\"",
|
329 |
+
"potatos": "potato",
|
330 |
+
"powderd sugar": "powdered sugar",
|
331 |
+
"processed meats": "unprocessed bran\"",
|
332 |
+
"raisens": "raisins",
|
333 |
+
"raisin": "raisin",
|
334 |
+
"rasberries": "raspberries",
|
335 |
+
"rasberry": "raspberry",
|
336 |
+
"rasins": "raisins",
|
337 |
+
"red chili pepper": "\"1 red chili pepper\"",
|
338 |
+
"regular fritos": "regular salt\"",
|
339 |
+
"resins": "raisins",
|
340 |
+
"rhubarbe": "rhubarb",
|
341 |
+
"rhubard": "rhubarb",
|
342 |
+
"rise": "rice",
|
343 |
+
"ro-tel tomatoes": "1 can ro-tel tomatoes",
|
344 |
+
"ruis": "rice",
|
345 |
+
"russet potatoe": "russet potato",
|
346 |
+
"s potatoes": "sweet potatoes",
|
347 |
+
"saffran": "saffron",
|
348 |
+
"saffrin": "saffron",
|
349 |
+
"saffrum": "saffron",
|
350 |
+
"safron": "saffron",
|
351 |
+
"said": "squid",
|
352 |
+
"salad dressing": "salad dressing\"",
|
353 |
+
"salad dressing mix": "salad dressing \"",
|
354 |
+
"sallt": "salt",
|
355 |
+
"sammon": "salmon",
|
356 |
+
"samon": "salmon",
|
357 |
+
"sault": "salt",
|
358 |
+
"saurkraut": "sauerkraut",
|
359 |
+
"sausge": "sausage",
|
360 |
+
"scallions": "scallion",
|
361 |
+
"seame seeds": "sesame seeds",
|
362 |
+
"seshuan pepper": "sichuan pepper",
|
363 |
+
"shalott": "shallot",
|
364 |
+
"shampinon": "mushroom",
|
365 |
+
"shampinyons": "mushrooms",
|
366 |
+
"sigar": "sugar",
|
367 |
+
"sinamon": "cinnamon",
|
368 |
+
"sirrup": "syrup",
|
369 |
+
"sirup": "syrup",
|
370 |
+
"sodium": "medium\"",
|
371 |
+
"soja": "soy",
|
372 |
+
"sojakeim": "soybeans",
|
373 |
+
"solt": "salt",
|
374 |
+
"sowmilk": "soy milk",
|
375 |
+
"soy sce": "soy sauce",
|
376 |
+
"soysauce": "soy sauce",
|
377 |
+
"spageti": "spaghetti",
|
378 |
+
"spagetti": "spaghetti",
|
379 |
+
"spaggeti": "spaghetti",
|
380 |
+
"spaghetii": "spaghetti",
|
381 |
+
"spinach": "spinach",
|
382 |
+
"spinage": "spinach",
|
383 |
+
"spinich": "spinach",
|
384 |
+
"squach": "squash",
|
385 |
+
"sqwash": "squash",
|
386 |
+
"stek": "steak",
|
387 |
+
"stew meat": "stew meat\"",
|
388 |
+
"strawberies": "strawberries",
|
389 |
+
"strawberrys": "strawberries",
|
390 |
+
"strawbery": "strawberry",
|
391 |
+
"strwberries": "strawberries",
|
392 |
+
"suagr": "sugar",
|
393 |
+
"subgr": "sugar",
|
394 |
+
"suger": "sugar",
|
395 |
+
"sugr": "sugar",
|
396 |
+
"sugur": "sugar",
|
397 |
+
"sunflowr seeds": "sunflower seeds",
|
398 |
+
"sweatner": "sweetener",
|
399 |
+
"sweet potatoe": "sweet potato",
|
400 |
+
"sweet potatos": "sweet potatoes",
|
401 |
+
"sweet relish": "\"sweet relish\"",
|
402 |
+
"termaric": "turmeric",
|
403 |
+
"termerick": "turmeric",
|
404 |
+
"termuric": "turmeric",
|
405 |
+
"that": "wheat",
|
406 |
+
"thim": "thyme",
|
407 |
+
"tomaotes": "tomatoes",
|
408 |
+
"tomatoe": "tomato",
|
409 |
+
"tomatoe paste": "tomato paste",
|
410 |
+
"tomatoe sauce": "tomato sauce",
|
411 |
+
"tomatos": "tomato",
|
412 |
+
"tomoatoes": "tomatoes",
|
413 |
+
"tomotoes": "tomato",
|
414 |
+
"tortilla chips": "tortilla chips\"",
|
415 |
+
"tumeric": "turmeric",
|
416 |
+
"tuna": "tuna",
|
417 |
+
"turkey burger": "turkey breast",
|
418 |
+
"turmeric": "turmeric",
|
419 |
+
"tyme": "thyme",
|
420 |
+
"under": "\"flounder",
|
421 |
+
"vagetable oil": "vegetable oil",
|
422 |
+
"vainilla": "vanilla",
|
423 |
+
"vanella": "vanilla",
|
424 |
+
"vanil": "vanilla",
|
425 |
+
"vanila": "vanilla",
|
426 |
+
"vanillin": "vanilla",
|
427 |
+
"vegitable oil": "vegetable oil",
|
428 |
+
"velveeta cheese": "velveeta cheese\"",
|
429 |
+
"venegar": "vinegar",
|
430 |
+
"vinager": "vinegar",
|
431 |
+
"vinaigre": "vinegar",
|
432 |
+
"vinega": "vinegar",
|
433 |
+
"vinger": "vinegar",
|
434 |
+
"vinigar": "vinegar",
|
435 |
+
"vinnegar": "vinegar",
|
436 |
+
"wall nuts": "walnuts",
|
437 |
+
"wallnut": "walnut",
|
438 |
+
"wallnuts": "walnuts",
|
439 |
+
"worscestershire": "worcestershire",
|
440 |
+
"worsestershire": "worcestershire",
|
441 |
+
"worstersire": "worcestershire",
|
442 |
+
"wostershire": "worcestershire",
|
443 |
+
"yeest": "yeast",
|
444 |
+
"yellow pepper": "yellow pepper\"",
|
445 |
+
"yogert": "yogurt",
|
446 |
+
"yoghourt": "yogurt",
|
447 |
+
"yoghurt": "yogurt",
|
448 |
+
"yohurt": "yogurt",
|
449 |
+
"youghurt": "yogurt",
|
450 |
+
"yougurt": "yogurt",
|
451 |
+
"zuccini": "zucchini",
|
452 |
+
"zucini": "zucchini",
|
453 |
+
"zukini": "zucchini"
|
454 |
+
}
|
docs/common_nutrition.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
requirements.txt
ADDED
@@ -0,0 +1,12 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
Flask==3.1.0
|
2 |
+
pandas==2.2.3
|
3 |
+
transformers==4.29.2
|
4 |
+
torch==2.6.0
|
5 |
+
torchaudio==2.6.0
|
6 |
+
torchvision==0.21.0
|
7 |
+
requests==2.32.3
|
8 |
+
textblob==0.17.1
|
9 |
+
tokenizers==0.13.3
|
10 |
+
nltk==3.8.1
|
11 |
+
asttokens==3.0.0
|
12 |
+
urllib3==2.3.0
|