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| import streamlit as st | |
| import streamlit.components.v1 as components | |
| from PIL import Image | |
| import os | |
| import glob | |
| import random | |
| from random import shuffle | |
| import requests | |
| import time | |
| from multiprocessing import Process | |
| import json | |
| def load_image(image_file): | |
| img = Image.open(image_file) | |
| return img | |
| def start_server(): | |
| os.system("uvicorn InferenceServer:app --port 8080 --host 0.0.0.0 --workers 3") | |
| def load_models(): | |
| if not is_port_in_use(8080): | |
| with st.spinner(text="Loading models, please wait..."): | |
| proc = Process(target=start_server, args=(), daemon=True) | |
| proc.start() | |
| while not is_port_in_use(8080): | |
| time.sleep(1) | |
| st.success("Model server started.") | |
| else: | |
| st.success("Model server already running...") | |
| st.session_state['models_loaded'] = True | |
| def is_port_in_use(port): | |
| import socket | |
| with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: | |
| return s.connect_ex(('0.0.0.0', port)) == 0 | |
| def run_search(food_image): | |
| get_request = "http://0.0.0.0:8080/food?food_input="+food_image | |
| food_response = requests.get(get_request) | |
| food_response_obj = json.loads(food_response.text) | |
| results = food_response_obj["top3"] | |
| st.markdown("<br/>", unsafe_allow_html=True) | |
| with col2: | |
| st.markdown("<b>Top 3 predictions   </b>", unsafe_allow_html=True) | |
| results_static_tag = '<html><title>W3.CSS</title><meta name="viewport" content="width=device-width, initial-scale=1"><link rel="stylesheet" href="https://www.w3schools.com/w3css/4/w3.css"><body><div class="w3-container">{}</div></body></html>' | |
| result_rows = "" | |
| for i, result in enumerate(results): | |
| results_dynamic_tag= '{} <br/> <div class="w3-light-grey"> <div class="{}" style="height:4px;width:{}%"></div> </div><br>' | |
| if i == 0: | |
| results_dynamic_tag = results_dynamic_tag.format("<b>" + str(i+1) + "." + result[0].title() + "</b>", 'w3-blue', result[1] * 100) | |
| else: | |
| results_dynamic_tag = results_dynamic_tag.format(str(i+1) + "." + result[0].title(), "w3-orange" ,result[1] * 100) | |
| result_rows += results_dynamic_tag | |
| results_static_tag = results_static_tag.format(result_rows) | |
| st.markdown(results_static_tag, unsafe_allow_html=True) | |
| recipe_response_obj = food_response_obj["recipe"] | |
| recipe_name = recipe_response_obj['recipe_name'] | |
| highlighted_ingredients =recipe_response_obj['highlighted_ingredients'] | |
| recipe = recipe_response_obj['recipe'] | |
| source = recipe_response_obj['source'] | |
| nutritional_facts = recipe_response_obj['nutritional_facts'] | |
| title_tag = '<h4> Recipe for top result:  ' + recipe_name + '</h4>' | |
| st.markdown(title_tag, unsafe_allow_html=True) | |
| ing_hdr_tag = '<h5> Ingredients </h5>' | |
| ing_style= "{border: 3x outset white; background-color: #ccf5ff; color: black; text-align: left; font-size: 14px; padding: 5px;}" | |
| ing_tag = '<html><head><style>.ingdiv{}</style></head><body><div class="ingdiv">{}</div></body></html>' | |
| ing_tag = ing_tag.format(ing_style, highlighted_ingredients.strip()) | |
| st.markdown(ing_hdr_tag, unsafe_allow_html=True) | |
| st.markdown(ing_tag + "<br/>", unsafe_allow_html=True) | |
| rec_hdr_tag = '<h5> Recipe </h5>' | |
| rec_style= "{border: 3x outset white; background-color: #ffeee6; color: black; text-align: left; font-size: 14px; padding: 5px;}" | |
| rec_tag = '<html><head><style>.recdiv{}</style></head><body><div class="recdiv">{}</div></body></html>' | |
| rec_tag = rec_tag.format(rec_style, recipe.strip()) | |
| st.markdown(rec_hdr_tag, unsafe_allow_html=True) | |
| st.markdown(rec_tag + "<br/>", unsafe_allow_html=True) | |
| nut_hdr_tag = '<h5> Nutritional facts </h5>' | |
| nut_style= "{border: 3x outset white; background-color: #e6e6ff; color: black; text-align: left; font-size: 14px; padding: 5px;}" | |
| nut_tag = '<html><head><style>.nutdiv{}</style></head><body><div class="nutdiv">{}</div></body></html>' | |
| nut_tag = nut_tag.format(nut_style, nutritional_facts.strip()) | |
| st.markdown(nut_hdr_tag, unsafe_allow_html=True) | |
| st.markdown(nut_tag + "<br/>", unsafe_allow_html=True) | |
| src_hdr_tag = '<h5> Recipe source </h5>' | |
| src_tag = '<a href={} target="_blank">{}</a>' | |
| src_tag = src_tag.format(source, source) | |
| st.markdown(src_hdr_tag, unsafe_allow_html=True) | |
| st.markdown(src_tag + "<br/>", unsafe_allow_html=True) | |
| return 1 | |
| if 'models_loaded' not in st.session_state: | |
| st.session_state['models_loaded'] = False | |
| st.title('WTF - What The Food 🤬') | |
| st.subheader("Image to Recipe - 1.5M foods supported") | |
| st.markdown("Built for fun with 💙 by a quintessential foodie - Prithivi Da, The maker of [Gramformer](https://github.com/PrithivirajDamodaran/Gramformer), [Styleformer](https://github.com/PrithivirajDamodaran/Styleformer) and [Parrot paraphraser](https://github.com/PrithivirajDamodaran/Parrot_Paraphraser) | ✍️ [@prithivida](https://twitter.com/prithivida) |[[GitHub]](https://github.com/PrithivirajDamodaran)", unsafe_allow_html=True) | |
| st.markdown("""<i> (Read Me: The idea: Food Image => Recipe. So it works on single foods and platters <p style='color:red; display:inline'> but May Not perform well on custom combinations or hyper-local foods. (NOT intended for commercial use.) </p>) </i>""", unsafe_allow_html=True) | |
| if __name__ == '__main__': | |
| if not st.session_state['models_loaded']: | |
| load_models() | |
| random_button = st.button('⚡ Try a Random Food') | |
| st.write("(or)") | |
| st.info('Upload a HD, landscape image') | |
| image_file = st.file_uploader("", type=["jpg","jpeg"]) | |
| col1, col2 = st.columns(2) | |
| if random_button: | |
| with st.spinner(text="Detecting food..."): | |
| samples = glob.glob('./samples' + "/*") | |
| shuffle(samples) | |
| random_sample = random.choice(samples) | |
| pil_image = load_image(random_sample) | |
| with col1: | |
| st.image(pil_image, use_column_width='auto') | |
| return_code = run_search(random_sample) | |
| else: | |
| if image_file is not None: | |
| pil_image = load_image(image_file) | |
| with open(image_file.name, 'wb') as f: | |
| pil_image.save(f) | |
| with col1: | |
| st.image(pil_image, use_column_width='auto') | |
| with st.spinner(text="Detecting food..."): | |
| return_code = run_search(image_file.name) | |
| os.system('rm -r "' + image_file.name + '"') | |