Upload app.py

app.py

@@ -0,0 +1,64 @@
+import streamlit as st
+import numpy as np
+import numpy.linalg as la
+import pickle 
+#import streamlit_analytics
+
+
+# Compute Cosine Similarity
+def cosine_similarity(x,y):
+
+    x_arr = np.array(x)
+    y_arr = np.array(y)
+    return np.dot(x_arr,y_arr)/(la.norm(x_arr)*la.norm(y_arr))
+
+
+# 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
+
+# Get Averaged Glove Embedding of a sentence
+def averaged_glove_embeddings(sentence, embeddings_dict):
+    words = sentence.split(" ")
+    glove_embedding = np.zeros(50)
+    count_words = 0
+    for word in words:
+        if word in embeddings_dict:
+            glove_embedding += embeddings_dict[word]
+            count_words += 1
+    
+    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"]
+
+# Text Search
+#with streamlit_analytics.track():
+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="")
+
+
+# Find closest word to an input word
+if text_search:
+    input_embedding = averaged_glove_embeddings(text_search, glove_embeddings)
+    cosine_sim = {}
+    for index in range(len(gold_words)):
+        cosine_sim[index] = cosine_similarity(input_embedding, glove_embeddings[gold_words[index]])
+
+    print(cosine_sim)
+    sorted_cosine_sim = sorted(cosine_sim.items(), key = lambda x: x[1], reverse=True)
+
+    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]] )
+    st.write("")
+    st.write("Demo developed by Dr. Karthik Mohan")
+