Upload app.py

app.py

@@ -14,12 +14,18 @@ def cosine_similarity(x,y):
 
 
 # Function to Load Glove Embeddings
-def load_glove_embeddings(glove_path="Data/embeddings.pkl"):
 
-    with open(glove_path,"rb") as f:
-        embeddings_dict = pickle.load(f)
-    
-    return embeddings_dict
+def load_glove_embeddings(file):
+    print("Loading Glove Model")
+    glove_model = {}
+    with open(file, 'r', encoding='utf-8') as f:
+        for line in f:
+            values = line.split()
+            word = values[0]
+            vector = np.asarray(values[1:], dtype='float32')
+            glove_model[word] = vector
+    print("Loaded {} words".format(len(glove_model)))
+    return glove_model
 
 # Get Averaged Glove Embedding of a sentence
 def averaged_glove_embeddings(sentence, embeddings_dict):
@@ -33,8 +39,7 @@ def averaged_glove_embeddings(sentence, embeddings_dict):
     
     return glove_embedding/max(count_words,1)
 
-# Load glove embeddings
-glove_embeddings = load_glove_embeddings()
+
 
 # Gold standard words to search from
 gold_words = ["flower","mountain","tree","car","building"]
@@ -45,6 +50,8 @@ st.title("Search Based Retrieval Demo")
 st.subheader("Pass in an input word or even a sentence (e.g. jasmine or mount adams)")
 text_search = st.text_input("", value="")
 
+# Load glove embeddings
+glove_embeddings = load_glove_embeddings('glove.6B.50d.txt')
 
 # Find closest word to an input word
 if text_search: