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Calorie_calculator.py

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+import streamlit as st
+def app():
+	#st.set_page_config(page_title='Calorie Calculator for Korean Food')
+	st.title("Calorie Calculator for Korean Food")
+	st.write("Enter the name of a food item with the serving amount")
+	st.markdown("""
+<style>
+body {
+    color: #000;
+    background-color: #CF9FFF;
+}
+</style>
+    """, unsafe_allow_html=True)
+	import pandas as pd
+	df1 = pd.read_csv('Korean food_calorie__.csv',index_col=0)
+	dish = st.selectbox("Select the name of dish ",('Kimbap/김밥','Tteokbokki/떡볶이','Kalguksu/칼국수','Gopchang-jeongol/곱창전골','Chadolbagi/차돌바기','Samgyeopsal/삼겹살','Panjeon/판전','Korean Tofu Kimchi Soup/두부 김치찌개','Samgetang/삼계탕','Pork Ribs/돼지 갈비','Dumplings/만두','Carrots In Korean/당근 한국어','Galbi Jjim/갈비찜','Bibimbap/비빔밥','Yeongyang dolsatbap/영양 돌솥밥','Carrot Salad/당근 샐러드','Korean Food Lunch/한식 점심','Ddukbaegi Bulgogi/뚝배기 불고기','Jjamppong/짬뽕','Octopus bibimbap/문어비빔밥','Sujebi/수제비','Dumpling Soup/만두 수프','Yukgaejang/육개장','Beef bulgogi/소불고기','Vegetable Dumbling/야채 덤블링','Bulgogi/불고기','Korean cold noodles/한국 냉면','Korean Eel soup/장어탕','Sundubu/순두부','Korean Homemade food/한국 집에서 만든 음식','Bibimnangmein/비빔낭마인','Bi Bim Bap/비빔밥','Noodle/국수','Galbi-tang /갈비탕','Bibimbap With Beef/소고기 비빔밥','Ginseng Chicken Soup/인삼 치킨 수프','Dwen Jang Jigae/드웬 장지개','Korean Beef Noodles/한우국수','Tofu Kimchi/두부','Makchang/막창','Steamed Monkfish/아귀찜','Sundaegukbab/순대국밥','Doganitang/도가니탕','Bulgogi /불고기','Eomukguk/어묵국','Soon Dae/순대','Sirloin Steak/등심 스테이크','Oi Kimchi/오이 김치','Tempah/템파','Seaweed Salad/미역 샐러드','Hobbang/호뱅','Broiled Eels/구운 장어','Korean bbq/한국식 바베큐','Nori/Seaweed/김','Upjinsal roast/웁진살 구이','Samgyupsal Pork Belly/삼겹살 삼겹살','JwiPo/주이포','Kim Bugak/미역 스낵','Sogogi Youkgajang/소고기육가장','Korean bbq marinade/한국식 바베큐 마리네이드','Self heating food/자체 가열 식품','Korean Chicken Stir-Fry/한국 치킨 볶음','Beef Intestine /쇠고기 창자','Pork Bone Soup – Gamjatang/감자탕','Ojingeo Deopbap/오징어덮밥','Ddukbokkie/떡볶이','Dwenjanggigae/웬장기개','Ja Jang Myeon/자장면','Doenjang Soup With Spinach/시금치 된장국','Korean Biscuit/한국 과자','Seasoned Tofu /양념 두부','Korean Short Rib Soup/한식 갈비탕','Roasted Pork Belly /구운 삼겹살','Tteokguk/축하 수프','Korean beef/한우','Dak kkochi/떡볶이','Rice Cake Soup/떡국','Stuffed Cucumber Kimchi/오이 박제 김치','Yeot/쯧쯧','Gim/김','Triangular kimbap/삼각김밥','Baked Chub Mackerel/구운 고등어','Bebimbap/비빔밥','Bread Ttoah/빵또아','Korean seaweed/한국 해초','Saeukkang/새강','Bread Ttoah/빵또아','Korean seaweed/한국 해초','Udon boeuf/우동부프','Korean Kimchi/한국 김치','Korean Bbq/한국식 바베큐','Dashida/다시다','Galbitang/갈비탕','Kimchi/김치','Bibimbap vegetables/비빔밥 야채','Korean Chicken/한국 치킨','Sikhye/식혜','Saeng Maekju/생맥주','Patbingsu/팥빙수','Tangpyeongchae/탕평채'))
+	serving_ = st.number_input("Enter number of Servings")
+	try: 
+		index_ = df1.index[df1['Name of the food']==dish].item()	
+	except ValueError:
+		index_ = 1
+	else:
+		recipe_ = df1['Recipe'][index_]
+		cal_ = df1['Calories'][index_]
+		carb_ = df1['Carb(g)'][index_]
+		fat_ = df1['Fat (g)'][index_]
+		proteins_ = df1['Proteins(g)'][index_]
+		image_ = df1['Image'][index_]
+		kd = df1['Korean Description'][index_]
+		st.write("**Calorie count** ",round(cal_*serving_,2))
+		st.write("**Carbohydrate value** ",round(carb_*serving_,2))
+		st.write("**Fat value** ",round(fat_*serving_,2))
+		st.write("**Protein intake** ",round(proteins_*serving_,2))
+		st.write("**Recipe**",recipe_)
+		st.write("**Description in Korean**",kd)	
+		st.image(image_,width=500)
+		

Procfile

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+web: sh setup.sh && streamlit run multiapp.py

README.md

@@ -1,12 +1 @@
----
-title: Calorie Calculator
-emoji: 💻
-colorFrom: indigo
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Calorie-Calculator

calorie_deficit.py

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+import streamlit as st
+import pandas as pd
+import datetime
+from datetime import date
+
+def app():
+	st.title("Calorie Deficit Tracker")
+	st.markdown("""
+<style>
+body {
+    color: #000;
+    background-color: #CF9FFF;
+}
+</style>
+    """, unsafe_allow_html=True)
+	sweight = st.number_input("Enter the current weight in kilograms")
+	height = st.number_input("Enter the height in centimetres")
+	gender = st.selectbox("Enter the Gender",("Male","Female"))
+	age = st.number_input("Enter the age in years")
+	calfood = st.number_input("Enter the food calorie intake")
+	act = st.selectbox("Enter the type of lifestyle ",("Sedentary : Sitting most of the day with no structured exercise","Moderately active : Sedentary and low active job with 1 hr exercise daily or Active job (moderate movement 8+ hrs per day) but no structured exercise","Vigorously active : Active job (moderate movement 8+ hours per day) and 1 hr exercise per day or Sedentary or low active job but 2 hours of exercise daily","Extremely active : Training more than 2 hrs per day or Moderately active job (walking all day) plus at least 1 hr of exercise daily"))
+	if act=="Sedentary : Sitting most of the day with no structured exercise":
+		act = 1.55
+	elif act == "Moderately active : Sedentary and low active job with 1 hr exercise daily or Active job (moderate movement 8+ hrs per day) but no structured exercise":
+		act = 1.85
+	elif act == "Vigorously active : Active job (moderate movement 8+ hours per day) and 1 hr exercise per day or Sedentary or low active job but 2 hours of exercise daily":
+		act = 2.2
+	else:
+		act = 2.4
+	rmr_male = 88.362 + (13.397 * sweight) + (4.799 * height) - (5.677 * age)
+	rmr_female = 447.593 + (9.247 * sweight) + (3.098 * height) - (4.330 * age)
+	if gender == "Male":
+		bmr = 10 * sweight + 6.25 * height - 5 * age  + 5
+		st.write("**Resting Metabolic Rate: **",round(rmr_male,2))
+	else:
+		bmr = 10 * sweight + 6.25 * height - 5 * age  - 161
+		st.write("**Resting Metabolic Rate: **",round(rmr_female,2))
+	mcal = bmr * act
+	mcalw = mcal * 7
+	st.write("**Calorie Deficit for the day: **",round(mcal,2))
+app()

multiapp.py

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+import streamlit as st
+st.set_page_config(page_title='Calorie Calculator')
+import Calorie_calculator
+import weightloss_tracker
+import calorie_deficit
+import rec_system
+PAGES = {
+    "Calorie Calculator": Calorie_calculator,
+    "Calorie Deficit Calculator": calorie_deficit,
+    "Weight Loss Tracker": weightloss_tracker,
+    "Recommendation System": rec_system
+}
+st.sidebar.title('Calorie cum weight loss tracker')
+selection = st.sidebar.radio("Go to", list(PAGES.keys()))
+page = PAGES[selection]
+page.app()

rec_system.py

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+import nltk
+import string
+import ast
+import re
+import unidecode
+import pandas as pd
+import streamlit as st
+import nltk
+nltk.download('wordnet')
+from nltk.stem import WordNetLemmatizer
+from nltk.corpus import wordnet
+from collections import Counter
+from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.feature_extraction.text import TfidfVectorizer
+import pickle
+def ingredient_parser(ingreds):
+    measures = ['teaspoon', 't', 'tsp', 'tablespoon', 'T', 'tbl.', 'tb', 'tbsp.', 'fluid ounce', 'fl oz', 'gill', 'cup', 'c', 'pint', 'p', 'pt', 'fl pt', 'quart', 'q', 'qt', 'fl qt', 'gallon', 'g', 'gal', 'ml', 'milliliter', 'millilitre', 'cc', 'mL', 'l', 'liter', 'litre', 'L', 'dl', 'deciliter', 'decilitre', 'dL', 'bulb', 'level', 'heaped', 'rounded', 'whole', 'pinch', 'medium', 'slice', 'pound', 'lb', '#', 'ounce', 'oz', 'mg', 'milligram', 'milligramme', 'g', 'gram', 'gramme', 'kg', 'kilogram', 'kilogramme', 'x', 'of', 'mm', 'millimetre', 'millimeter', 'cm', 'centimeter', 'centimetre', 'm', 'meter', 'metre', 'inch', 'in', 'milli', 'centi', 'deci', 'hecto', 'kilo']
+    words_to_remove = ['fresh', 'oil', 'a', 'red', 'bunch', 'and', 'clove', 'or', 'leaf', 'chilly','chillies', 'large', 'extra', 'sprig', 'ground', 'handful', 'free', 'small', 'pepper', 'virgin', 'range', 'from', 'dried', 'sustainable', 'black', 'peeled', 'higher', 'welfare', 'seed', 'for', 'finely', 'freshly', 'sea', 'quality', 'white', 'ripe', 'few', 'piece', 'source', 'to', 'organic', 'flat', 'smoked', 'ginger', 'sliced', 'green', 'picked', 'the', 'stick', 'plain', 'plus', 'mixed', 'mint', 'bay', 'basil', 'your', 'cumin', 'optional', 'fennel', 'serve', 'mustard', 'unsalted', 'baby', 'paprika', 'fat', 'ask', 'natural', 'skin', 'roughly', 'into', 'such', 'cut', 'good', 'brown', 'grated', 'trimmed', 'oregano', 'powder', 'yellow', 'dusting', 'knob', 'frozen', 'on', 'deseeded', 'low', 'runny', 'balsamic', 'cooked', 'streaky', 'nutmeg', 'sage', 'rasher', 'zest', 'pin', 'groundnut', 'breadcrumb', 'turmeric', 'halved', 'grating', 'stalk', 'light', 'tinned', 'dry', 'soft', 'rocket', 'bone', 'colour', 'washed', 'skinless', 'leftover', 'splash', 'removed', 'dijon', 'thick', 'big', 'hot', 'drained', 'sized', 'chestnut', 'watercress', 'fishmonger', 'english', 'dill', 'caper', 'raw', 'worcestershire', 'flake', 'cider', 'cayenne', 'tbsp', 'leg', 'pine', 'wild', 'if', 'fine', 'herb', 'almond', 'shoulder', 'cube', 'dressing', 'with', 'chunk', 'spice', 'thumb', 'garam', 'new', 'little', 'punnet', 'peppercorn', 'shelled', 'saffron', 'other''chopped', 'salt', 'olive', 'taste', 'can', 'sauce', 'water', 'diced', 'package', 'italian', 'shredded', 'divided', 'parsley', 'vinegar', 'all', 'purpose', 'crushed', 'juice', 'more', 'coriander', 'bell', 'needed', 'thinly', 'boneless', 'half', 'thyme', 'cubed', 'cinnamon', 'cilantro', 'jar', 'seasoning', 'rosemary', 'extract', 'sweet', 'baking', 'beaten', 'heavy', 'seeded', 'tin', 'vanilla', 'uncooked', 'crumb', 'style', 'thin', 'nut', 'coarsely', 'spring', 'chili', 'cornstarch', 'strip', 'cardamom', 'rinsed', 'honey', 'cherry', 'root', 'quartered', 'head', 'softened', 'container', 'crumbled', 'frying', 'lean', 'cooking', 'roasted', 'warm', 'whipping', 'thawed', 'corn', 'pitted', 'sun', 'kosher', 'bite', 'toasted', 'lasagna', 'split', 'melted', 'degree', 'lengthwise', 'romano', 'packed', 'pod', 'anchovy', 'rom', 'prepared', 'juiced', 'fluid', 'floret', 'room', 'active', 'seasoned', 'mix', 'deveined', 'lightly', 'anise', 'thai', 'size', 'unsweetened', 'torn', 'wedge', 'sour', 'basmati', 'marinara', 'dark', 'temperature', 'garnish', 'bouillon', 'loaf', 'shell', 'reggiano', 'canola', 'parmigiano', 'round', 'canned', 'ghee', 'crust', 'long', 'broken', 'ketchup', 'bulk', 'cleaned', 'condensed', 'sherry', 'provolone', 'cold', 'soda', 'cottage', 'spray', 'tamarind', 'pecorino', 'shortening', 'part', 'bottle', 'sodium', 'cocoa', 'grain', 'french', 'roast', 'stem', 'link', 'firm', 'asafoetida', 'mild', 'dash', 'boiling']
+    if isinstance(ingreds, list):
+        ingredients = ingreds
+    else:
+        ingredients = ast.literal_eval(ingreds)
+    translator = str.maketrans('', '', string.punctuation)
+    lemmatizer = WordNetLemmatizer()
+    ingred_list = []
+    for i in ingredients:
+        i.translate(translator)
+        # We split up with hyphens as well as spaces
+        items = re.split(' |-', i)
+        # Get rid of words containing non alphabet letters
+        items = [word for word in items if word.isalpha()]
+        # Turn everything to lowercase
+        items = [word.lower() for word in items]
+        # remove accents
+        items = [unidecode.unidecode(word) for word in items] #''.join((c for c in unicodedata.normalize('NFD', items) if unicodedata.category(c) != 'Mn'))
+        # Lemmatize words so we can compare words to measuring words
+        items = [lemmatizer.lemmatize(word) for word in items]
+        # Gets rid of measuring words/phrases, e.g. heaped teaspoon
+        items = [word for word in items if word not in measures]
+        # Get rid of common easy words
+        items = [word for word in items if word not in words_to_remove]
+        if items:
+            ingred_list.append(' '.join(items)) 
+    ingred_list = " ".join(ingred_list)
+    return ingred_list
+# load in tdidf model and encodings
+def scorefunc(ingredients):
+    with open('tfidf_encodings.pkl', 'rb') as f:
+        tfidf_encodings = pickle.load(f)
+    with open('tfidf_model.pkl', "rb") as f:
+        tfidf = pickle.load(f)
+# parse the ingredients using my ingredient_parser
+    try:
+        ingredients_parsed = ingredient_parser(ingredients)
+    except:
+        ingredients_parsed = ingredient_parser([ingredients])
+# use our pretrained tfidf model to encode our input ingredients
+    ingredients_tfidf = tfidf.transform([ingredients_parsed])
+# calculate cosine similarity between actual recipe ingreds and test ingreds
+    cos_sim = map(lambda x: cosine_similarity(ingredients_tfidf, x), tfidf_encodings)
+    scores = list(cos_sim)
+    return scores
+def get_recommendations(N, scores):
+    # load in recipe dataset
+    df_recipes = pd.read_csv('JO_parsed.csv')
+    # order the scores with and filter to get the highest N scores(desc order)
+    top = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:N]
+    # create dataframe to load in recommendations
+    recommendation = pd.DataFrame(columns = ['Recipe', 'Ingredients', 'Score', 'Url'])
+    count = 0
+    for i in top:
+        recommendation.at[count, 'Recipe'] = df_recipes['recipe_name'][i]
+        recommendation.at[count, 'Ingredients'] = df_recipes['ingredients'][i]
+        recommendation.at[count, 'Url'] = df_recipes['recipe_urls'][i]
+        recommendation.at[count, 'Score'] = "{:.3f}".format(float(scores[i]))
+        count += 1
+    return recommendation
+def app():
+	st.markdown("*Recipe Recommendation System*")
+	recipe_box = st.selectbox(
+            "Display the top 5 recommendations or pick a particular recipe",
+            ["Show the top picks", "Select a single recipe"],
+        )
+	if recipe_box =="Show the top picks":
+        	N=5
+	else:
+        	N=1
+	ing = st.text_input("Enter the ingredients you would like to cook with")
+	if ing:
+		scores = scorefunc(ing)
+		rec = get_recommendations(N,scores)
+		st.write("These are some recommendation(s) for you")
+		st.write(rec.head(N))
+

requirements.txt

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+altair==4.1.0
+blinker==1.4
+dlib==19.22.1
+face-recognition==1.3.0
+gitdb==4.0.5
+GitPython==3.1.11
+html5lib==1.0.1
+imbalanced-learn==0.7.0
+ipaddr==2.2.0
+ipykernel==5.3.4
+ipython==7.15.0
+ipython-genutils==0.2.0
+ipywidgets==7.5.1
+jupyter-client==6.1.7
+jupyter-core==4.7.0
+jupyterlab-pygments==0.1.2
+MarkupSafe==1.1.1
+matplotlib==3.2.2
+nbclient==0.5.1
+nltk==3.5
+notebook==6.1.5
+numpy==1.19.4
+pandas==1.1.4
+pathtools==0.1.2
+pickleshare==0.7.5
+Pillow==8.0.1
+pip==21.3.0
+pycparser==2.20
+pydeck==0.5.0
+Pygments==2.7.2
+pyparsing==2.4.7
+pyrsistent==0.17.3
+python-dateutil==2.8.1
+pytoml==0.1.21
+pytz==2020.4
+pyzmq==20.0.0
+six==1.15.0
+scikit-learn==0.23.2
+streamlit==0.71.0
+unidecode==1.3.2
+urllib3==1.26.2
+validators==0.18.1
+watchdog==0.10.4
+webencodings==0.5.1
+widgetsnbextension==3.5.1

setup.sh

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+mkdir -p ~/.streamlit
+
+echo "[server]
+headless = true
+port = $PORT
+enableCORS = false
+" > ~/.streamlit/config.toml

tfidf_encodings.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c75868951060ee04bbd6b35d5884d7248dead1785923401ce2abd6e0e5fa53c7
+size 132948

tfidf_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fe36aa25df7cdb05145c6b400a0afe26054ee14b2019e8f6552de343c0860107
+size 42698

weightloss_tracker.py

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+import streamlit as st
+import pandas as pd
+import datetime
+from datetime import date
+def shortcut():
+	scval = st.beta_expander("Information about Calories Burnt",False)
+	scval.markdown("**Walking**")
+	scval.markdown("1 Hour--> 350 Cal")
+	scval.markdown("1,000 Steps-->60 Cal")
+	scval.markdown("10,000 Steps-->600 Cal")
+	scval.markdown("**Running at an average speed**")
+	scval.markdown("1 Mile--> 100 Cal")
+	scval.markdown("1 KM-->60 Cal")
+	scval.markdown("5 Miles-->500 Cal")	
+	scval.markdown("5 KM-->300 Cal")
+	scval.markdown("**Swimming at a slow pace**")
+	scval.markdown("1 Hour--> 500 Cal")
+	scval.markdown("1 Lap of 50m Pool-->25 Cal")
+	scval.markdown("**Swimming at a fast pace**")
+	scval.markdown("1 Hour--> 750 Cal")
+	scval.markdown("1 Lap of 50m Pool-->50 Cal")
+	scval.markdown("**Gym Workout for 1 Hour**")
+	scval.markdown("Light workout--> 200 Cal")
+	scval.markdown("Medium workout--> 350 Cal")
+	scval.markdown("Heavy workout--> 500 Cal")
+	scval.markdown("**Bike Ride for 1 Hour**")
+	scval.markdown("At 20 Km/hr or 13 Miles/hr--> 600 Cal")
+	scval.markdown("At 25 Km/hr or 16 Miles/hr--> 740 Cal")	
+	scval1 = st.beta_expander("Information about Calorie Intake",False)
+	scval1.markdown("**Average general Calorie intake--> 2000-2500 Cal**")
+	scval1.markdown("**No. of Calorie Deficit required to lose 1 kg--> 7800 Cal**")
+	scval1.markdown("**Deficit of 1,100 cals per day --> 1 kg loss per week**")
+	scval1.markdown("**Daily loss for 2 kg per week is 0.3 kg per day**")
+	scval1.markdown("**1 kg of fat--> 7,500 calories**")
+	scval1.markdown("Calories for 2 small meals--> 1000 Cal")
+	scval1.markdown("Calories for 3 small meals--> 1800 Cal")
+	scval1.markdown("Apple, orange or banana--> 100 Cal")
+	scval1.markdown("Muesli with 1 cup of milk--> 500 Cal")
+	scval1.markdown("Fish 400 g with salad--> 600 Cal")
+	scval1.markdown("Steak 300 g with salad--> 800 Cal")
+def func(val,sweight,height,gender,age,lr,cw,calfood,calburn):
+	st.write("**Days from the Start: **",val)
+	target_t = sweight - (val*lr)/7 
+	st.write("**Start Weight: **",sweight)
+	st.write("**Target Today: **",round(target_t,2))
+	prg = target_t - cw
+	if prg>0:
+		st.write("**Progress: Ahead of target by **",round(prg,2)," **kg**")
+	else:
+		st.write("**Progress: Behind target by **",round(prg,2)," **kg**")
+	rmr_male = 88.362 + (13.397 * cw) + (4.799 * height) - (5.677 * age)
+	rmr_female = 447.593 + (9.247 * cw) + (3.098 * height) - (4.330 * age)
+	if gender =="Male":
+		cd = rmr_male + calburn - calfood
+		st.write("**Resting Metabolic Rate: **",round(rmr_male,2))
+	else:
+		cd = rmr_female + calburn - calfood
+		st.write("**Resting Metabolic Rate: **",round(rmr_female,2))
+	st.write("**Calorie Deficit: **",round(cd,2))
+	loss_exp = cd/7500
+	st.write("**Loss Expected: **",round(loss_exp,2))
+	st.markdown("____")
+	shortcut()
+def app():
+	st.title("Weight Loss Tracker")
+	st.markdown("""
+<style>
+body {
+    color: #000;
+    background-color: #CF9FFF;
+}
+</style>
+    """, unsafe_allow_html=True)
+	date = st.date_input("Enter the start date",datetime.date(2022, 1, 1))
+	sweight = st.number_input("Enter the start weight in kilograms")
+	height = st.number_input("Enter the height in centimetres")
+	gender = st.selectbox("Enter the Gender",("Male","Female"))
+	age = st.number_input("Enter the age in years")
+	lr = st.number_input("Enter the loss rate(in kgs) per week")
+	cw = st.number_input("Enter the weight now")
+	calfood = st.number_input("Enter the food calorie intake")
+	calburn = st.number_input("Enter the calories burnt")
+	cdate = datetime.date.today()
+	if cdate == date:
+		val = 1
+		func(val,sweight,height,gender,age,lr,cw,calfood,calburn)
+	elif cdate>date:
+		delta = cdate - date
+		val = delta.days
+		func(val,sweight,height,gender,age,lr,cw,calfood,calburn)
+	else:
+		st.write("** Please select a Start date before current date**")
+
+
+
+
+
+
+