Added Images

.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 glove.6B.50d.txt filter=lfs diff=lfs merge=lfs -text
+images/building.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -53,7 +53,6 @@ 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:
     input_embedding = averaged_glove_embeddings(text_search, glove_embeddings)
     cosine_sim = {}
@@ -65,7 +64,23 @@ if text_search:
 
     st.write("(My search uses glove embeddings)")
     st.write("Closest word I have between flower, mountain, tree, car and building for your input is: ")
-    st.subheader(gold_words[sorted_cosine_sim[0][0]] )
+    word = gold_words[sorted_cosine_sim[0][0]]
+    if word == "flower":
+        st.subheader(word)
+        st.image('images/flower.png', width=200)
+    elif word == "mountain":
+        st.subheader(word)
+        st.image('images/mountain.png', width=200)
+    elif word == "tree":
+        st.subheader(word)
+        st.image('images/tree.png', width=200)
+    elif word == "car":
+        st.subheader(word)
+        st.image('images/car.png', width=200)
+    else:
+        st.subheader(word)
+        st.image('images/building.png', width=200)
+ 
     st.write("")
-    st.write("Demo developed by Dr. Karthik Mohan")
+