Adding app to demo space HF

paper_recommender.py

@@ -0,0 +1,59 @@
+import sys
+import os
+import re
+import torch
+import base64
+import pandas as pd 
+import streamlit as st
+from sklearn.metrics.pairwise import cosine_similarity
+from sentence_transformers import SentenceTransformer
+
+sys.path.insert(0, os.getcwd())
+
+st.title("ArXiV Paper Recommender")
+
+def set_background(main_bg):
+    main_bg_ext = "jpg"
+    st.markdown(
+         f"""
+         <style>
+         .stApp {{
+             background: url(data:image/{main_bg_ext};base64,{base64.b64encode(open(main_bg, "rb").read()).decode()});
+             background-size: cover
+         }}
+         </style>
+         """,
+         unsafe_allow_html=True
+     )
+
+set_background("../images/p1.jpg")
+
+topic = st.text_input('What kind of paper would you wish to be recommended?', 'I want to read a paper on Bayesian Optimization!')
+number = st.number_input('Show me these many papers.', min_value=1, max_value=10, value=3, step=1)
+
+def process_text(text):
+    rep = {"\n": " ", "(": "", ")": "", "!": ""}
+    rep = dict((re.escape(k), v) for k, v in rep.items()) 
+    pattern = re.compile("|".join(rep.keys()))
+    text = pattern.sub(lambda m: rep[re.escape(m.group(0))], text).lower()
+    return text
+
+def get_cosine_similarity(feature_vec_1, feature_vec_2):    
+    return cosine_similarity(feature_vec_1.reshape(1, -1), feature_vec_2.reshape(1, -1))[0][0]
+
+def get_model():
+	device = 'cuda' if torch.cuda.is_available() else None
+	model = SentenceTransformer('paraphrase-MiniLM-L6-v2', device=device)
+	return model
+
+if st.button("GO!"):
+	prompt = process_text(topic)
+	model = get_model()
+	prompt_embedded = model.encode(prompt)
+	df_embed = pd.read_pickle('../data/embeddings_pkl.pkl').drop_duplicates(subset=['titles'])
+	df_embed["similarity_scores"] = df_embed["abstracts_embeddings"].apply(lambda x: get_cosine_similarity(x, prompt_embedded))
+	top_n = df_embed.nlargest(number, 'similarity_scores').head(5)["titles"].to_list()
+	st.text(" ")
+	st.subheader('Have a look at the following: :sunglasses:')
+	for rec_title in top_n:
+		st.markdown("-> " + rec_title)