Create Create ui_components.py

Create ui_components.py

@@ -0,0 +1,99 @@
+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.
+    """)