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WhatTheFood

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prithivida commited on Nov 10, 2021

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e551c21

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1 Parent(s): 92160fe

maintenance

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Files changed (1) hide show

app.py +4 -185

app.py CHANGED Viewed

@@ -1,127 +1,7 @@
 import streamlit as st
-import streamlit.components.v1 as components
-import pickle
-import sentence_transformers
-from sentence_transformers import SentenceTransformer, util
 from PIL import Image
-import torch
-import spacy
-import os
-import glob
-import random
-import pysos
-from random import shuffle
-torch.set_num_threads(4)
-def get_spacy_dbpedia_highlights(ingredients):
-    import spacy
-    import spacy_dbpedia_spotlight
-    raw_ingredients = ingredients
-    import re
-    ingredients = re.sub("[0-9,()\/\-\.]", "", ingredients)
-    doc = nlp(ingredients)
-    for ent in doc.ents:
-        if ent.text.lower() not in stop_words and ent.text in raw_ingredients:
-            replace_str = '<mark style="color: green; background-color:yellow">  <a href="' + ent.kb_id_ + '" target="_blank"> ' +  ent.text + '</a> </mark>'
-            raw_ingredients = raw_ingredients.replace(ent.text, replace_str)
-    return raw_ingredients
-def detect_food(query, text_emb, labels, k=1):
-    query_emb = model.encode(Image.open(query), convert_to_tensor=True, show_progress_bar=False)
-    hits = util.semantic_search(query_emb, text_emb, top_k=k)[0]
-    results = []
-    for i, hit in enumerate(hits):
-        results.append((labels[hit['corpus_id']], hit['score']))
-        if i > 2:
-            break
-    return results
-def run_search(food_image, col2):
-    with open("./Pretrained/labels.pkl", 'rb') as fIn:
-        labels = pickle.load(fIn)
-    emb_filename = './Pretrained/food_embeddings.pkl'
-    text_emb = torch.load(emb_filename, map_location=torch.device('cpu'))
-    results  = detect_food(food_image, text_emb, labels, 3)
-    food_recognised, score  = results[0]
-    del text_emb
-    del labels
-    import pysos
-    food2id = pysos.Dict("./Pretrained/food2id")
-    id2recipe = pysos.Dict("./Pretrained/id2recipe")
-    id = food2id[food_recognised]
-    recipe_name = food_recognised.title()
-    ingredients_list =id2recipe[id]['ingredients']
-    highlighted_ingredients= get_spacy_dbpedia_highlights(ingredients_list)
-    recipe= id2recipe[id]['instructions']
-    dataset = " " + id2recipe[id]['dataset']
-    if dataset.strip() == "Recipe1M":
-        nutritional_facts= "For nutritional facts, schedule and servings, visit the link in the footer"
-    else:
-            nutritional_facts = id2recipe[id]['nutrition_facts']
-    source= id2recipe[id]['recipesource']
-    del id2recipe
-    del food2id
-    st.markdown("<br/>", unsafe_allow_html=True)
-    with col2:
-        st.markdown("<b>Top 3 predictions &nbsp </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)
-    title_tag = '<h4> Recipe for top result: &nbsp' + 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)
-    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")
@@ -133,70 +13,9 @@ def load_image(image_file):
 	img = Image.open(image_file)
 	return img
-def load_models():
-    with st.spinner(text="Loading models, please wait..."):
-        os.system("python -m spacy download en_core_web_sm")
-        nlp = spacy.load('en_core_web_sm')
-        nlp.add_pipe('dbpedia_spotlight')
-        model = SentenceTransformer('clip-ViT-B-32')
-        stop_words = set(['chopped', 'freshly ground', 'skinless', 'freshly squeezed', 'dash', 'powder', 'rice', 'ice', 'noodles', 'pepper', 'milk', 'ced', 'cheese', 'sugar', 'salt', 'pkt', 'minced', 'onion', 'onions', 'garlic', 'butter', 'slices', 'ounce', 'sauce', 'freshly', 'grated', 'teaspoon', 'cup', 'oz', '⁄', 'to', 'or', 'diced', 'into', 'pound', 'dried', 'water', 'about', 'whole', 'small', 'vegetable', 'inch', 'tbsp', 'cooked', 'large', 'sliced', 'dry', 'optional', 'package', 'ounces', 'unsalted', 'lbs', 'green', 'flour', 'for', 'wine', 'crushed', 'drained', 'lb', 'frozen', 'tsp', 'finely', 'medium', 'tablespoon', 'tablespoons', 'juice', 'shredded', 'can', 'minced', 'fresh', 'cut', 'pieces', 'in', 'thinly', 'of', 'extract', 'teaspoons', 'ground', 'and', 'cups', 'peeled', 'taste', 'ml', 'lengths'])
-        st.session_state['nlp']  = nlp
-        st.session_state['model']  = model
-        st.session_state['stop_words']  = stop_words
-        #st.session_state['id2recipe'] = pysos.Dict("./Pretrained/id2recipe")
-        # st.session_state['food2id'] = pysos.Dict("./Pretrained/food2id")
-        # with open("./Pretrained/labels.pkl", 'rb') as fIn:
-        #     labels = pickle.load(fIn)
-        # st.session_state['labels'] = labels
-        #emb_filename = './Pretrained/food_embeddings.pkl'
-        #text_emb = torch.load(emb_filename, map_location=torch.device('cpu'))
-        #st.session_state['text_emb'] = text_emb
-if  not st.session_state['models_loaded']:
-    load_models()
-    st.session_state['models_loaded'] = True
-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"])
-nlp = st.session_state['nlp']
-model = st.session_state['model']
-stop_words = st.session_state['stop_words']
-#id2recipe = st.session_state['id2recipe']
-#text_emb = st.session_state['text_emb']
-# food2id = st.session_state['food2id']
-# labels = st.session_state['labels']
-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, col2)
-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, col2)
-            os.system('rm -r "' + image_file.name + '"')
-#<hr style='height:1px;border:none;color:violet;background-color:gray;' />

 import streamlit as st
+import streamlit.components.v1 as components
 from PIL import Image
 st.title('WTF - What The Food 🤬')
 st.subheader("Image to Recipe - 1.5M foods supported")
 	img = Image.open(image_file)
 	return img
+pil_image = load_image("./samples/under_maint.png")
+st.image(pil_image)
+st.write("Please come back later")