Make proper link

app.py

@@ -3,6 +3,7 @@ import pandas as pd
 from sentence_transformers import SentenceTransformer, util
 import streamlit as st
 from st_aggrid import AgGrid, GridOptionsBuilder, JsCode
+import numpy as np
 st.set_page_config(layout='wide')
 
 @st.cache(allow_output_mutation=True)
@@ -27,11 +28,11 @@ def get_similarity_score(model, data, query, corpus_embeddings):
     """Get similarity score for each data point and sort by similarity score and day"""
     hits = semantic_search(model, [query], corpus_embeddings)
     result = pd.merge(data, hits, left_on='ID', right_on='corpus_id')
-    result['Last Day'] = pd.to_datetime(result['Last Day'], format='%d/%m/%Y')
+    result['Last Day'] = pd.to_datetime(result['Last Day'], format='%d/%m/%Y').dt.date
     result.sort_values(by=['score', 'Last Day'], ascending=[False, True], inplace=True)
     return result
 
-@st.cache(allow_output_mutation=True)
+@st.cache(ttl=24*3600)
 def create_embedding(model: SentenceTransformer, data: pd.DataFrame, key: str) -> Tuple[list, list]:
     """Create vector embeddings from the dataset"""
     corpus_sentences = data[key].astype(str).tolist()
@@ -83,8 +84,19 @@ def main():
     st.write('This app lets you search and sort talent by job title or relevant job descriptions from ecommurz talent list in real-time.')
 
     columns = ['Timestamp', 'Full Name', 'Company', 'Previous Role',
-               'Experience', 'Last Day', 'LinkedIn Profile']
+               'Experience (months)', 'Last Day', 'LinkedIn Profile']
     data = load_dataset(columns)
+
+    # Preprocess Data
+    data['Full Name'] = data['Full Name'].str.title()
+    data['LinkedIn Profile'] = np.where(data['LinkedIn Profile'].str.startswith('www.linkedin.com'),
+                                        "https://" + data['LinkedIn Profile'],
+                                        data['LinkedIn Profile'])
+    data['LinkedIn Profile'] = np.where(data['LinkedIn Profile'].str.startswith('linkedin.com'),
+                                        "https://www." + data['LinkedIn Profile'],
+                                        data['LinkedIn Profile'])
+
+
     model = load_model()
     corpus_embeddings = create_embedding(model, data, 'Previous Role')