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