File size: 8,411 Bytes
96a706d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 |
"""
Display utilities for HRHUB Streamlit UI.
Contains formatted display components for candidates and companies.
"""
import streamlit as st
import pandas as pd
import ast
from typing import Dict, Any, List, Tuple
def display_candidate_profile(candidate):
"""
Display comprehensive candidate profile in Streamlit.
Args:
candidate: Pandas Series with candidate data
"""
st.markdown("### 👤 Candidate Profile")
st.markdown("---")
# Career Objective
with st.expander("🎯 Career Objective", expanded=True):
st.write(candidate.get('career_objective', 'Not provided'))
# Skills
with st.expander("💻 Skills & Expertise", expanded=True):
try:
skills = ast.literal_eval(candidate.get('skills', '[]'))
if skills:
# Display as tags
skills_html = " ".join([f'<span style="background-color: #0066CC; color: white; padding: 5px 10px; border-radius: 15px; margin: 3px; display: inline-block;">{skill}</span>' for skill in skills[:15]])
st.markdown(skills_html, unsafe_allow_html=True)
else:
st.write("No skills listed")
except:
st.write(candidate.get('skills', 'No skills listed'))
# Education
with st.expander("🎓 Education"):
try:
institutions = ast.literal_eval(candidate.get('educational_institution_name', '[]'))
degrees = ast.literal_eval(candidate.get('degree_names', '[]'))
majors = ast.literal_eval(candidate.get('major_field_of_studies', '[]'))
years = ast.literal_eval(candidate.get('passing_years', '[]'))
if institutions and any(institutions):
for i in range(len(institutions)):
degree = degrees[i] if i < len(degrees) else 'N/A'
major = majors[i] if i < len(majors) else 'N/A'
year = years[i] if i < len(years) else 'N/A'
st.write(f"**{degree}** in {major}")
st.write(f"📍 {institutions[i]}")
st.write(f"📅 {year}")
if i < len(institutions) - 1:
st.write("---")
else:
st.write("No education information provided")
except:
st.write("No education information provided")
# Work Experience
with st.expander("💼 Work Experience"):
try:
companies = ast.literal_eval(candidate.get('professional_company_names', '[]'))
positions = ast.literal_eval(candidate.get('positions', '[]'))
starts = ast.literal_eval(candidate.get('start_dates', '[]'))
ends = ast.literal_eval(candidate.get('end_dates', '[]'))
if companies and any(companies):
for i in range(len(companies)):
position = positions[i] if i < len(positions) else 'N/A'
start = starts[i] if i < len(starts) else 'N/A'
end = ends[i] if i < len(ends) else 'N/A'
st.write(f"**{position}** at {companies[i]}")
st.write(f"📅 {start} - {end}")
if i < len(companies) - 1:
st.write("---")
# Show responsibilities
responsibilities = candidate.get('responsibilities', '')
if responsibilities:
st.markdown("**Key Responsibilities:**")
st.text(responsibilities)
else:
st.write("No work experience listed")
except:
st.write("No work experience listed")
def display_company_card(
company_data,
similarity_score: float,
rank: int
):
"""
Display company information as a card.
Args:
company_data: Pandas Series with company data
similarity_score: Match score
rank: Ranking position
"""
with st.container():
# Header with rank and score
col1, col2, col3 = st.columns([1, 4, 2])
with col1:
st.markdown(f"### #{rank}")
with col2:
st.markdown(f"### 🏢 {company_data.get('name', 'Unknown Company')}")
with col3:
# Color-coded score
if similarity_score >= 0.7:
color = "#00FF00" # Green
label = "Excellent"
elif similarity_score >= 0.6:
color = "#FFD700" # Gold
label = "Very Good"
elif similarity_score >= 0.5:
color = "#FFA500" # Orange
label = "Good"
else:
color = "#FF6347" # Red
label = "Fair"
st.markdown(
f'<div style="text-align: center; padding: 10px; background-color: {color}20; border: 2px solid {color}; border-radius: 10px;">'
f'<span style="font-size: 24px; font-weight: bold; color: {color};">{similarity_score:.1%}</span><br>'
f'<span style="font-size: 12px;">{label} Match</span>'
f'</div>',
unsafe_allow_html=True
)
# Company details
st.markdown(f"**Company ID:** {company_data.name}")
# Description
description = company_data.get('description', company_data.get('text', 'No description available'))
if len(str(description)) > 200:
description = str(description)[:200] + "..."
st.markdown(f"**About:** {description}")
st.markdown("---")
def display_match_table(
matches: List[Tuple[int, float, Any]],
show_top_n: int = 10
):
"""
Display match results as a formatted table.
Args:
matches: List of (company_id, score, company_data) tuples
show_top_n: Number of matches to display
"""
st.markdown(f"### 🎯 Top {show_top_n} Company Matches")
st.markdown("---")
# Prepare data for table
table_data = []
for rank, (comp_id, score, comp_data) in enumerate(matches[:show_top_n], 1):
table_data.append({
'Rank': f"#{rank}",
'Company ID': comp_id,
'Score': f"{score:.1%}",
'Match Quality': '🔥 Excellent' if score >= 0.7 else '✨ Very Good' if score >= 0.6 else '👍 Good' if score >= 0.5 else '⭐ Fair'
})
# Display as dataframe
df = pd.DataFrame(table_data)
# Style the dataframe
st.dataframe(
df,
use_container_width=True,
hide_index=True,
column_config={
"Rank": st.column_config.TextColumn(width="small"),
"Score": st.column_config.TextColumn(width="small"),
"Company ID": st.column_config.TextColumn(width="medium"),
"Match Quality": st.column_config.TextColumn(width="medium")
}
)
st.info("💡 **Tip:** Scores above 0.6 indicate strong alignment between candidate skills and company requirements!")
def display_stats_overview(
candidate_data,
matches: List[Tuple[int, float, Any]]
):
"""
Display overview statistics about the matching results.
Args:
candidate_data: Candidate information
matches: List of matches
"""
st.markdown("### 📊 Matching Overview")
col1, col2, col3, col4 = st.columns(4)
with col1:
st.metric(
"Total Matches",
len(matches),
help="Number of companies analyzed"
)
with col2:
avg_score = sum(score for _, score, _ in matches) / len(matches) if matches else 0
st.metric(
"Average Score",
f"{avg_score:.1%}",
help="Average similarity score"
)
with col3:
excellent = sum(1 for _, score, _ in matches if score >= 0.7)
st.metric(
"Excellent Matches",
excellent,
help="Matches with score ≥ 70%"
)
with col4:
best_score = max((score for _, score, _ in matches), default=0)
st.metric(
"Best Match",
f"{best_score:.1%}",
help="Highest similarity score"
)
st.markdown("---") |