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import streamlit as st
from typing import Dict, List, Any, Optional
def render_header():
"""Render the application header"""
st.title("📄 Resume Analyzer & Job Matcher")
st.markdown("""
Upload your resume to get personalized job matches, identify skill gaps,
and receive recommendations for improvement.
""")
st.divider()
def render_upload_section():
"""Render the file upload section"""
st.subheader("Upload Your Resume")
st.markdown("Supported formats: PDF, DOCX")
uploaded_file = st.file_uploader("Choose a file", type=["pdf", "docx"])
if uploaded_file is not None:
st.success(f"File uploaded: {uploaded_file.name}")
file_details = {
"Filename": uploaded_file.name,
"File size": f"{uploaded_file.size / 1024:.2f} KB",
"File type": uploaded_file.type
}
with st.expander("File Details"):
for key, value in file_details.items():
st.write(f"**{key}:** {value}")
return uploaded_file
def render_results_section(
resume_data: Any,
job_matches: List[Dict[str, Any]],
skill_gaps: List[Dict[str, Any]],
improvement_tips: List[str]
):
"""Render the results section with analysis output"""
st.divider()
st.header("Analysis Results")
# Create tabs for different result categories
tab1, tab2, tab3 = st.tabs(["Job Matches", "Skill Gaps", "Resume Improvement"])
# Tab 1: Job Matches
with tab1:
st.subheader("Recommended Job Roles")
if not job_matches:
st.info("No job matches found. Please try uploading a different resume.")
else:
for i, job in enumerate(job_matches):
with st.container():
col1, col2 = st.columns([3, 1])
with col1:
st.markdown(f"### {i+1}. {job.get('title', 'Unknown Job')}")
st.markdown(f"**Match Score:** {job.get('match_score', 'N/A')}%")
st.markdown(f"**Description:** {job.get('description', 'No description available')}")
with col2:
st.markdown("**Matching Skills:**")
for skill in job.get('key_matching_skills', []):
st.markdown(f"- {skill}")
st.divider()
# Tab 2: Skill Gaps
with tab2:
st.subheader("Skill Gap Analysis")
if not skill_gaps:
st.info("No skill gaps identified.")
else:
for skill_gap in skill_gaps:
with st.container():
col1, col2 = st.columns([1, 2])
with col1:
st.markdown(f"### {skill_gap.get('skill', 'Unknown Skill')}")
st.markdown(f"**Importance:** {skill_gap.get('importance', 'Medium')}")
with col2:
st.markdown("**How to acquire this skill:**")
st.markdown(skill_gap.get('acquisition_recommendation', 'No recommendation available'))
st.divider()
# Tab 3: Resume Improvement
with tab3:
st.subheader("Resume Improvement Tips")
if not improvement_tips:
st.info("No improvement tips available.")
else:
for i, tip in enumerate(improvement_tips):
st.markdown(f"**{i+1}.** {tip}")
def render_footer():
"""Render the application footer"""
st.divider()
st.markdown("""
**Note:** This application uses AI to analyze your resume and provide recommendations.
The results should be considered as suggestions and may not be 100% accurate.
""") |