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import streamlit as st |
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import random |
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import pandas as pd |
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from PIL import Image |
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import requests |
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import json |
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from transformers import pipeline |
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import numpy as np |
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from transformers import AutoFeatureExtractor |
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from transformers import AutoModelForImageClassification |
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import plotly.graph_objects as go |
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import plotly |
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import re |
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import io |
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from fpdf import FPDF |
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import base64 |
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from fpdf import FPDF, HTMLMixin |
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st.set_page_config(layout='wide', |
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page_title='Food Category Classification & Recipes' |
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) |
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sidebar_acc = ['App Description', 'About Project'] |
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sidebar_acc_nav = st.sidebar.radio('**INFORMATION SECTION**', sidebar_acc) |
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if sidebar_acc_nav == 'App Description': |
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st.sidebar.markdown("<h2 style='text-align: center;'> Food Category Classification Description </h2> ", unsafe_allow_html=True) |
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st.sidebar.markdown("This is a Food Category Image Classifier model that has been trained by [Kaludi](https://huggingface.co/Kaludi) to recognize **12** different categories of foods, which includes **Bread**, **Dairy**, **Dessert**, **Egg**, **Fried Food**, **Fruit**, **Meat**, **Noodles**, **Rice**, **Seafood**, **Soup**, and **Vegetable**. It can accurately classify an image of food into one of these categories by analyzing its visual features. This model can be used by food bloggers, restaurants, and recipe websites to quickly categorize and sort their food images, making it easier to manage their content and provide a better user experience.") |
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elif sidebar_acc_nav == 'About Project': |
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st.sidebar.markdown("<h2 style='text-align: center;'> About Project </h2>", unsafe_allow_html=True) |
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st.sidebar.markdown("<hr style='text-align: center;'>", unsafe_allow_html=True) |
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st.sidebar.markdown("<h3 style='text-align: center;'>Project Location:</h3>", unsafe_allow_html=True) |
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st.sidebar.markdown("<p style='text-align: center;'><strong><a href='https://huggingface.co/Kaludi/food-category-classification-v2.0'>Model</a></strong> | <strong><a href='https://huggingface.co/datasets/Kaludi/food-category-classification-v2.0'>Dataset</a></strong></p>", unsafe_allow_html=True) |
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st.sidebar.markdown("<hr style='text-align: center;'>", unsafe_allow_html=True) |
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st.sidebar.markdown("<h3 style='text-align: center;'>Project Creators:</h3>", unsafe_allow_html=True) |
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st.sidebar.markdown("<p style='text-align: center;'><a href='https://github.com/Kaludii'><strong>AA</strong></a></p>", unsafe_allow_html=True) |
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st.sidebar.markdown("<p style='text-align: center;'><a href='https://github.com/Kaludii'><strong>AM</strong></a></p>", unsafe_allow_html=True) |
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st.sidebar.markdown("<p style='text-align: center;'><a href='https://github.com/Kaludii'><strong>BK</strong></a></p>", unsafe_allow_html=True) |
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st.sidebar.markdown("<p style='text-align: center;'><a href='https://github.com/Kaludii'><strong>DK</strong></a></p>", unsafe_allow_html=True) |
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def main(): |
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st.title("Food Category Classification & Recipes") |
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st.markdown("### Backgroud") |
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st.markdown("This is a Food Category Image Classifier model that has been trained by [Kaludi](https://huggingface.co/Kaludi) to recognize **12** different categories of foods, which includes **Bread**, **Dairy**, **Dessert**, **Egg**, **Fried Food**, **Fruit**, **Meat**, **Noodles**, **Rice**, **Seafood**, **Soup**, and **Vegetable**. It can accurately classify an image of food into one of these categories by analyzing its visual features. This model can be used by food bloggers, restaurants, and recipe websites to quickly categorize and sort their food images, making it easier to manage their content and provide a better user experience.") |
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st.header("Try it out!") |
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if st.checkbox("Show/Hide Examples"): |
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st.header("Example Images") |
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col1, col2, col3, col4 = st.columns(4) |
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with col1: |
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st.image("examples/example_0.jpg", width=260) |
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st.image("examples/example_1.jpg", width=260) |
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with col2: |
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st.image("examples/example_2.jpg", width=260) |
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st.image("examples/example_3.jpg", width=260) |
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with col3: |
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st.image("examples/example_4.jpg", width=260) |
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st.image("examples/example_5.jpg", width=260) |
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with col4: |
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st.image("examples/example_6.jpg", width=260) |
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st.image("examples/example_7.jpg", width=260) |
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diet_options = ['All', 'Gluten-Free', 'Vegan', 'Vegetarian', 'Dairy-Free'] |
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diet = st.selectbox('Diet', diet_options) |
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cuisine_options = ['All', 'African', 'Asian', 'Caribbean', 'Central American', 'Europe', 'Middle Eastern', 'North American', 'Oceanic', 'South American'] |
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cuisine = st.selectbox('Cuisine', cuisine_options) |
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calories = st.slider("Select Max Calories (Per Serving)", 25, 1000, 500) |
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st.write("Selected: **{}** Max Calories.".format(calories)) |
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uploaded_file = st.file_uploader("Upload Files", type=['png','jpeg','jpg']) |
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loading_text = st.empty() |
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if uploaded_file != None: |
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loading_text.markdown("Loading...") |
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img = Image.open(uploaded_file) |
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extractor = AutoFeatureExtractor.from_pretrained("Kaludi/food-category-classification-v2.0") |
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model = AutoModelForImageClassification.from_pretrained("Kaludi/food-category-classification-v2.0") |
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inputs = extractor(img, return_tensors="pt") |
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outputs = model(**inputs) |
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loading_text.empty() |
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label_num=outputs.logits.softmax(1).argmax(1) |
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label_num=label_num.item() |
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probs = outputs.logits.softmax(dim=1) |
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percentage = round(probs[0, label_num].item() * 100, 2) |
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st.markdown("### Your Image:") |
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st.image(img, width=260) |
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st.write("The Predicted Classification is:") |
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if label_num==0: |
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st.write("**Bread** (" + f"{percentage}%)") |
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elif label_num==1: |
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st.write("**Dairy** (" + f"{percentage}%)") |
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elif label_num==2: |
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st.write("**Dessert** (" + f"{percentage}%)") |
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elif label_num==3: |
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st.write("**Egg** (" + f"{percentage}%)") |
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elif label_num==4: |
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st.write("**Fried Food** (" + f"{percentage}%)") |
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elif label_num==5: |
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st.write("**Fruit** (" + f"{percentage}%)") |
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elif label_num==6: |
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st.write("**Meat** (" + f"{percentage}%)") |
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elif label_num==7: |
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st.write("**Noodles** (" + f"{percentage}%)") |
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elif label_num==8: |
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st.write("**Rice** (" + f"{percentage}%)") |
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elif label_num==9: |
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st.write("**Seafood** (" + f"{percentage}%)") |
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elif label_num==10: |
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st.write("**Soup** (" + f"{percentage}%)") |
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else: |
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st.write("**Vegetable** (" + f"{percentage}%)") |
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st.write("You Selected **{}** For Diet and **{}** For Cuisine with Max".format(diet, cuisine), calories, "Calories For", ( "**Bread**" if label_num==0 else "**Dairy**" if label_num==1 else "**Dessert**" if label_num==2 else "**Egg**" if label_num==3 else "**Fried Food**" if label_num==4 else "**Fruit**" if label_num==5 else "**Meat**" if label_num==6 else "**Noodles**" if label_num==7 else "**Rice**" if label_num==8 else "**Seafood**" if label_num==9 else "**Soup**" if label_num==10 else "**Vegetable**")) |
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url = "https://alcksyjrmd.execute-api.us-east-2.amazonaws.com/default/nutrients_response" |
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category = ("Bread" if label_num==0 else "Dairy" if label_num==1 else "Dessert" if label_num==2 else "Egg" if label_num==3 else "Fried" if label_num==4 else "Fruit" if label_num==5 else "Meat" if label_num==6 else "Noodles" if label_num==7 else "Rice" if label_num==8 else "Seafood" if label_num==9 else "**Soup**" if label_num==10 else "Vegetable") |
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params = {"f": category, "k": str(calories)} |
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if diet != "All": |
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params["d"] = diet |
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if cuisine != "All": |
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params["c"] = cuisine |
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response = requests.get(url, params=params) |
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response_json = json.loads(response.content) |
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response_json = list(response_json) |
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if len(response_json) == 0: |
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st.markdown("### No Recipe Found:") |
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st.write("**No recipes found. Please adjust your search criteria.**") |
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else: |
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if len(response_json) > 1: |
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random_recipe = random.choice(response_json) |
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if st.button("Get Another Recipe"): |
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response_json.remove(random_recipe) |
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if len(response_json) == 0: |
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st.write("No more recipes. Please adjust your search criteria.") |
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else: |
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random_recipe = random.choice(response_json) |
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st.markdown("### Recommended Recipe:") |
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st.write("**Title:** ", random_recipe['Title']) |
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if random_recipe['Image Link'].endswith(".jpg") or random_recipe['Image Link'].endswith(".jpeg") or random_recipe['Image Link'].endswith(".png"): |
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st.image(random_recipe['Image Link'], width=300) |
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else: |
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st.write("**Image Link:** ", random_recipe['Image Link']) |
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st.write("**Rating:** ", random_recipe['Rating']) |
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if random_recipe['Description'] != "Description not found": |
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st.write("**Description:** ", random_recipe['Description']) |
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st.write("**Ingredients:** ", random_recipe['Ingredients']) |
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st.write("**Recipe Facts:** ", random_recipe['Recipe Facts']) |
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st.write("**Directions:** ", random_recipe['Directions']) |
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values = [ |
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float(re.sub(r'[^\d.]+', '', random_recipe['Total Fat'])), |
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float(re.sub(r'[^\d.]+', '', random_recipe['Saturated Fat'])), |
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float(re.sub(r'[^\d.]+', '', random_recipe['Cholesterol'])) / 1000, |
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float(re.sub(r'[^\d.]+', '', random_recipe['Sodium'])) / 1000, |
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float(re.sub(r'[^\d.]+', '', random_recipe['Total Carbohydrate'])), |
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float(re.sub(r'[^\d.]+', '', random_recipe['Dietary Fiber'])), |
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float(re.sub(r'[^\d.]+', '', random_recipe['Total Sugars'])), |
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float(re.sub(r'[^\d.]+', '', random_recipe['Protein'])), |
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float(re.sub(r'[^\d.]+', '', random_recipe['Vitamin C'])) / 1000, |
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float(re.sub(r'[^\d.]+', '', random_recipe['Calcium'])) / 1000, |
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float(re.sub(r'[^\d.]+', '', random_recipe['Iron'])) / 1000, |
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float(re.sub(r'[^\d.]+', '', random_recipe['Potassium'])) / 1000 |
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] |
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dv = [65, 20, 0.3, 2.3, 300, 28, 50, 50, 0.09, 1, 0.018, 4.7] |
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dv_percent = [round(value * 100 / dv[i]) for i, value in enumerate(values)] |
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nutrition_html = """ |
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<div id="nutrition-info_6-0" class="comp nutrition-info"> |
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<table class="nutrition-info__table"> |
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<thead> |
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<tr> |
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<th class="nutrition-info__heading" colspan="3">Number of Servings: <span class="nutrition-info__heading-aside">{servings}</span></th> |
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</tr> |
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</thead> |
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<tbody class="nutrition-info__table--body"> |
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<tr class="nutrition-info__table--row"> |
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<td class="nutrition-info__table--cell">Calories</td> |
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<td class="nutrition-info__table--cell">{calories}</td> |
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<td class="nutrition-info__table--cell"></td> |
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</tr> |
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<tr class="nutrition-info__table--row"> |
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<td class="nutrition-info__table--cell">Total Fat</td> |
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<td class="nutrition-info__table--cell">{total_fat}</td> |
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<td class="nutrition-info__table--cell">{fat_percent}% DV</td> |
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</tr> |
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<tr class="nutrition-info__table--row"> |
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<td class="nutrition-info__table--cell">Saturated Fat</td> |
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<td class="nutrition-info__table--cell">{saturated_fat}</td> |
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<td class="nutrition-info__table--cell">{sat_fat_percent}% DV</td> |
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</tr> |
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<tr class="nutrition-info__table--row"> |
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<td class="nutrition-info__table--cell">Cholesterol</td> |
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<td class="nutrition-info__table--cell">{cholesterol}</td> |
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<td class="nutrition-info__table--cell">{chol_percent}% DV</td> |
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</tr> |
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<tr class="nutrition-info__table--row"> |
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<td class="nutrition-info__table--cell">Sodium</td> |
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<td class="nutrition-info__table--cell">{sodium}</td> |
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<td class="nutrition-info__table--cell">{sodium_percent}% DV</td> |
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</tr> |
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<tr class="nutrition-info__table--row"> |
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<td class="nutrition-info__table--cell">Total Carbohydrate</td> |
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<td class="nutrition-info__table--cell">{total_carbohydrate}</td> |
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<td class="nutrition-info__table--cell">{carb_percent}% DV</td> |
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</tr> |
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<tr class="nutrition-info__table--row"> |
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<td class="nutrition-info__table--cell">Dietary Fiber</td> |
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<td class="nutrition-info__table--cell">{dietary_fiber}</td> |
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<td class="nutrition-info__table--cell">{diet_fibe_percent}% DV</td> |
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</tr> |
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<tr class="nutrition-info__table--row"> |
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<td class="nutrition-info__table--cell">Total Sugars</td> |
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<td class="nutrition-info__table--cell">{total_sugars}</td> |
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<td class="nutrition-info__table--cell">{tot_sugars_percent}% DV</td> |
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</tr> |
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<tr class="nutrition-info__table--row"> |
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<td class="nutrition-info__table--cell">Protein</td> |
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<td class="nutrition-info__table--cell">{protein}</td> |
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<td class="nutrition-info__table--cell">{protein_percent}% DV</td> |
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</tr> |
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<tr class="nutrition-info__table--row"> |
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<td class="nutrition-info__table--cell">Vitamin C</td> |
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<td class="nutrition-info__table--cell">{vitc}</td> |
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<td class="nutrition-info__table--cell">{vitc_percent}% DV</td> |
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</tr> |
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<tr class="nutrition-info__table--row"> |
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<td class="nutrition-info__table--cell">Calcium</td> |
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<td class="nutrition-info__table--cell">{calc}</td> |
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<td class="nutrition-info__table--cell">{calc_percent}% DV</td> |
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</tr> |
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<tr class="nutrition-info__table--row"> |
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<td class="nutrition-info__table--cell">Iron</td> |
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<td class="nutrition-info__table--cell">{iron}</td> |
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<td class="nutrition-info__table--cell">{iron_percent}% DV</td> |
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</tr> |
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<tr class="nutrition-info__table--row"> |
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<td class="nutrition-info__table--cell">Potassium</td> |
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<td class="nutrition-info__table--cell">{pota}</td> |
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<td class="nutrition-info__table--cell">{pota_percent}% DV</td> |
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</tr> |
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</tbody> |
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</table> |
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</div> |
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""" |
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formatted_html = nutrition_html.format( |
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calories=random_recipe['Calories'], |
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total_fat=random_recipe['Total Fat'], |
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saturated_fat=random_recipe['Saturated Fat'], |
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cholesterol=random_recipe['Cholesterol'], |
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sodium=random_recipe['Sodium'], |
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total_carbohydrate=random_recipe['Total Carbohydrate'], |
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dietary_fiber=random_recipe['Dietary Fiber'], |
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total_sugars=random_recipe['Total Sugars'], |
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servings=random_recipe['Number of Servings'], |
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vitc=random_recipe['Vitamin C'], |
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calc=random_recipe['Calcium'], |
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iron=random_recipe['Iron'], |
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pota=random_recipe['Potassium'], |
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protein=random_recipe['Protein'], |
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fat_percent=dv_percent[0], |
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sat_fat_percent=dv_percent[1], |
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chol_percent=dv_percent[2], |
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sodium_percent=dv_percent[3], |
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carb_percent=dv_percent[4], |
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diet_fibe_percent=dv_percent[5], |
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tot_sugars_percent=dv_percent[6], |
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protein_percent=dv_percent[7], |
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vitc_percent=dv_percent[8], |
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calc_percent=dv_percent[9], |
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iron_percent=dv_percent[10], |
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pota_percent=dv_percent[11] |
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) |
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def format_table(val): |
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return f"background-color: #133350; color: #fff; border: 1px solid #ddd; border-radius: .25rem; padding: .625rem .625rem 0; font-family: Helvetica; font-size: 1rem;" |
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with st.container(): |
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st.write("<h2 style='text-align:left;'>Nutrition Facts (per serving)</h2>", unsafe_allow_html=True) |
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st.write(f"<div style='max-height:none; overflow:auto'>{formatted_html}</div>", unsafe_allow_html=True) |
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st.write("<p style='text-align:left;'>*The % Daily Value (DV) tells you how much a nutrient in a food serving contributes to a daily diet. 2,000 calories a day is used for general nutrition advice.</p>", unsafe_allow_html=True) |
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labels = ['Total Fat', 'Saturated Fat', 'Cholesterol', 'Sodium', 'Total Carbohydrate', 'Dietary Fiber', 'Total Sugars', 'Protein', 'Vitamin C', 'Calcium', 'Iron', 'Potassium'] |
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fig = go.Figure(data=[go.Pie(labels=labels, values=values)]) |
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st.markdown("### Macronutrients Pie Chart ;) (In Grams)") |
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st.plotly_chart(fig) |
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st.write("**Tags:** ", random_recipe['Tags']) |
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st.write("**Recipe URL:** ", random_recipe['Recipe URLs']) |
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st.write("*To download this recipe as a PDF, open the hamburger menu on the top right and click on Print.*") |
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st.markdown("### JSON Response:") |
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st.write(response_json) |
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else: |
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st.markdown("### Recommended Recipe:") |
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st.write("**Title:** ", response_json[0]['Title']) |
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if response_json[0]['Image Link'].endswith(".jpg") or response_json[0]['Image Link'].endswith(".jpeg") or response_json[0]['Image Link'].endswith(".png"): |
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st.image(response_json[0]['Image Link'], width=300) |
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else: |
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st.write("**Image Link:** ", response_json[0]['Image Link']) |
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st.write("**Rating:** ", response_json[0]['Rating']) |
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if response_json[0]['Description'] != "Description not found": |
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st.write("**Description:** ", response_json[0]['Description']) |
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st.write("**Ingredients:** ", response_json[0]['Ingredients']) |
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st.write("**Recipe Facts:** ", response_json[0]['Recipe Facts']) |
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st.write("**Directions:** ", response_json[0]['Directions']) |
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values = [ |
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float(re.sub(r'[^\d.]+', '', response_json[0]['Total Fat'])), |
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float(re.sub(r'[^\d.]+', '', response_json[0]['Saturated Fat'])), |
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float(re.sub(r'[^\d.]+', '', response_json[0]['Cholesterol'])) / 1000, |
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float(re.sub(r'[^\d.]+', '', response_json[0]['Sodium'])) / 1000, |
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float(re.sub(r'[^\d.]+', '', response_json[0]['Total Carbohydrate'])), |
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float(re.sub(r'[^\d.]+', '', response_json[0]['Dietary Fiber'])), |
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float(re.sub(r'[^\d.]+', '', response_json[0]['Total Sugars'])), |
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float(re.sub(r'[^\d.]+', '', response_json[0]['Protein'])), |
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float(re.sub(r'[^\d.]+', '', response_json[0]['Vitamin C'])) / 1000, |
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float(re.sub(r'[^\d.]+', '', response_json[0]['Calcium'])) / 1000, |
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float(re.sub(r'[^\d.]+', '', response_json[0]['Iron'])) / 1000, |
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float(re.sub(r'[^\d.]+', '', response_json[0]['Potassium'])) / 1000 |
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] |
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dv = [65, 20, 0.3, 2.3, 300, 28, 50, 50, 0.09, 1, 0.018, 4.7] |
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dv_percent = [round(value * 100 / dv[i]) for i, value in enumerate(values)] |
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nutrition_html = """ |
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<div id="nutrition-info_6-0" class="comp nutrition-info"> |
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<table class="nutrition-info__table"> |
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<thead> |
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<tr> |
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<th class="nutrition-info__heading" colspan="3">Number of Servings: <span class="nutrition-info__heading-aside">{servings}</span></th> |
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</tr> |
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</thead> |
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<tbody class="nutrition-info__table--body"> |
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<tr class="nutrition-info__table--row"> |
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<td class="nutrition-info__table--cell">Calories</td> |
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<td class="nutrition-info__table--cell">{calories}</td> |
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<td class="nutrition-info__table--cell"></td> |
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</tr> |
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<tr class="nutrition-info__table--row"> |
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<td class="nutrition-info__table--cell">Total Fat</td> |
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<td class="nutrition-info__table--cell">{total_fat}</td> |
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<td class="nutrition-info__table--cell">{fat_percent}% DV</td> |
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</tr> |
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<tr class="nutrition-info__table--row"> |
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<td class="nutrition-info__table--cell">Saturated Fat</td> |
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<td class="nutrition-info__table--cell">{saturated_fat}</td> |
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<td class="nutrition-info__table--cell">{sat_fat_percent}% DV</td> |
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</tr> |
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<tr class="nutrition-info__table--row"> |
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<td class="nutrition-info__table--cell">Cholesterol</td> |
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<td class="nutrition-info__table--cell">{cholesterol}</td> |
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<td class="nutrition-info__table--cell">{chol_percent}% DV</td> |
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</tr> |
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<tr class="nutrition-info__table--row"> |
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<td class="nutrition-info__table--cell">Sodium</td> |
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<td class="nutrition-info__table--cell">{sodium}</td> |
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<td class="nutrition-info__table--cell">{sodium_percent}% DV</td> |
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</tr> |
|
<tr class="nutrition-info__table--row"> |
|
<td class="nutrition-info__table--cell">Total Carbohydrate</td> |
|
<td class="nutrition-info__table--cell">{total_carbohydrate}</td> |
|
<td class="nutrition-info__table--cell">{carb_percent}% DV</td> |
|
</tr> |
|
<tr class="nutrition-info__table--row"> |
|
<td class="nutrition-info__table--cell">Dietary Fiber</td> |
|
<td class="nutrition-info__table--cell">{dietary_fiber}</td> |
|
<td class="nutrition-info__table--cell">{diet_fibe_percent}% DV</td> |
|
</tr> |
|
<tr class="nutrition-info__table--row"> |
|
<td class="nutrition-info__table--cell">Total Sugars</td> |
|
<td class="nutrition-info__table--cell">{total_sugars}</td> |
|
<td class="nutrition-info__table--cell">{tot_sugars_percent}% DV</td> |
|
</tr> |
|
<tr class="nutrition-info__table--row"> |
|
<td class="nutrition-info__table--cell">Protein</td> |
|
<td class="nutrition-info__table--cell">{protein}</td> |
|
<td class="nutrition-info__table--cell">{protein_percent}% DV</td> |
|
</tr> |
|
<tr class="nutrition-info__table--row"> |
|
<td class="nutrition-info__table--cell">Vitamin C</td> |
|
<td class="nutrition-info__table--cell">{vitc}</td> |
|
<td class="nutrition-info__table--cell">{vitc_percent}% DV</td> |
|
</tr> |
|
<tr class="nutrition-info__table--row"> |
|
<td class="nutrition-info__table--cell">Calcium</td> |
|
<td class="nutrition-info__table--cell">{calc}</td> |
|
<td class="nutrition-info__table--cell">{calc_percent}% DV</td> |
|
</tr> |
|
<tr class="nutrition-info__table--row"> |
|
<td class="nutrition-info__table--cell">Iron</td> |
|
<td class="nutrition-info__table--cell">{iron}</td> |
|
<td class="nutrition-info__table--cell">{iron_percent}% DV</td> |
|
</tr> |
|
<tr class="nutrition-info__table--row"> |
|
<td class="nutrition-info__table--cell">Potassium</td> |
|
<td class="nutrition-info__table--cell">{pota}</td> |
|
<td class="nutrition-info__table--cell">{pota_percent}% DV</td> |
|
</tr> |
|
</tbody> |
|
</table> |
|
</div> |
|
""" |
|
|
|
formatted_html = nutrition_html.format( |
|
calories=response_json[0]['Calories'], |
|
total_fat=response_json[0]['Total Fat'], |
|
saturated_fat=response_json[0]['Saturated Fat'], |
|
cholesterol=response_json[0]['Cholesterol'], |
|
sodium=response_json[0]['Sodium'], |
|
total_carbohydrate=response_json[0]['Total Carbohydrate'], |
|
dietary_fiber=response_json[0]['Dietary Fiber'], |
|
total_sugars=response_json[0]['Total Sugars'], |
|
servings=response_json[0]['Number of Servings'], |
|
vitc=response_json[0]['Vitamin C'], |
|
calc=response_json[0]['Calcium'], |
|
iron=response_json[0]['Iron'], |
|
pota=response_json[0]['Potassium'], |
|
protein=response_json[0]['Protein'], |
|
fat_percent=dv_percent[0], |
|
sat_fat_percent=dv_percent[1], |
|
chol_percent=dv_percent[2], |
|
sodium_percent=dv_percent[3], |
|
carb_percent=dv_percent[4], |
|
diet_fibe_percent=dv_percent[5], |
|
tot_sugars_percent=dv_percent[6], |
|
protein_percent=dv_percent[7], |
|
vitc_percent=dv_percent[8], |
|
calc_percent=dv_percent[9], |
|
iron_percent=dv_percent[10], |
|
pota_percent=dv_percent[11] |
|
|
|
) |
|
|
|
|
|
def format_table(val): |
|
return f"background-color: #133350; color: #fff; border: 1px solid #ddd; border-radius: .25rem; padding: .625rem .625rem 0; font-family: Helvetica; font-size: 1rem;" |
|
|
|
with st.container(): |
|
|
|
st.write("<h2 style='text-align:left;'>Nutrition Facts (per serving)</h2>", unsafe_allow_html=True) |
|
st.write(f"<div style='max-height:none; overflow:auto'>{formatted_html}</div>", unsafe_allow_html=True) |
|
st.write("<p style='text-align:left;'>*The % Daily Value (DV) tells you how much a nutrient in a food serving contributes to a daily diet. 2,000 calories a day is used for general nutrition advice.</p>", unsafe_allow_html=True) |
|
|
|
labels = ['Total Fat', 'Saturated Fat', 'Cholesterol', 'Sodium', 'Total Carbohydrate', 'Dietary Fiber', 'Total Sugars', 'Protein', 'Vitamin C', 'Calcium', 'Iron', 'Potassium'] |
|
fig = go.Figure(data=[go.Pie(labels=labels, values=values)]) |
|
st.markdown("### Macronutrients Pie Chart ;) (In Grams)") |
|
st.plotly_chart(fig) |
|
st.write("**Tags:** ", response_json[0]['Tags']) |
|
st.write("**Recipe URL:** ", response_json[0]['Recipe URLs']) |
|
st.write("*To download this recipe as a PDF, open the hamburger menu on the top right and click on Print.*") |
|
st.markdown("### JSON Response:") |
|
st.write(response_json) |
|
|
|
st.markdown("<hr style='text-align: center;'>", unsafe_allow_html=True) |
|
st.markdown("<p style='text-align: center'><a href='https://github.com/Kaludii'>Github</a> | <a href='https://huggingface.co/Kaludi'>HuggingFace</a></p>", unsafe_allow_html=True) |
|
|
|
if __name__ == '__main__': |
|
main() |
|
|
