Optimised load take #3

app.py

@@ -42,8 +42,8 @@ def detect_food(query, text_emb, labels, k=1):
 
 def run_search(food_image, col2):
 
-    # with open("./Pretrained/labels.pkl", 'rb') as fIn:
-    #     labels = pickle.load(fIn) 
+    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'))
@@ -52,11 +52,12 @@ def run_search(food_image, col2):
     food_recognised, score  = results[0]
 
     # del text_emb
-    # del labels
+    del labels
 
     import pysos 
-    id2recipe = pysos.Dict("./Pretrained/id2recipe")
     food2id = pysos.Dict("./Pretrained/food2id")  
+    # id2recipe = pysos.Dict("./Pretrained/id2recipe")
+    
 
 
     id = food2id[food_recognised]
@@ -73,7 +74,7 @@ def run_search(food_image, col2):
     source= id2recipe[id]['recipesource']
     
 
-    del id2recipe
+    # del id2recipe
     del food2id
 
     st.markdown("<br/>", unsafe_allow_html=True)     
@@ -125,7 +126,7 @@ if 'models_loaded' not in st.session_state:
 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 is 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.</p>) </i>""", 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.</p>) </i>""", unsafe_allow_html=True)
 
 
 def load_image(image_file):
@@ -133,7 +134,7 @@ def load_image(image_file):
 	return img
 
 def load_models():
-    with st.spinner(text="Loading 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')
@@ -142,13 +143,11 @@ def load_models():
         st.session_state['nlp']  = nlp
         st.session_state['model']  = model
         st.session_state['stop_words']  = stop_words
-        # id2recipe = pysos.Dict("./Pretrained/id2recipe")
-        # food2id = pysos.Dict("./Pretrained/food2id")  
-        # st.session_state['id2recipe'] = id2recipe
-        # st.session_state['food2id'] = food2id
-        with open("./Pretrained/labels.pkl", 'rb') as fIn:
-            labels = pickle.load(fIn) 
-        st.session_state['labels'] = labels            
+        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
@@ -166,10 +165,11 @@ 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']
-# food2id = st.session_state['food2id']
-labels = st.session_state['labels']
+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)