import streamlit as st
import base64
from io import BytesIO
import json
from datetime import datetime
import re
st.set_page_config(page_title="ResumeBuilder Pro", layout="wide", initial_sidebar_state="collapsed")
if "resume_data" not in st.session_state:
st.session_state.resume_data = {
"fullName": "Alexander Johnson",
"title": "Senior Frontend Developer",
"email": "alex.johnson@example.com",
"phone": "(555) 123-4567",
"location": "San Francisco, CA",
"summary": "Experienced frontend developer with 6+ years specializing in React and modern JavaScript frameworks. Passionate about creating intuitive user interfaces and optimizing web performance.",
"experience": [
{
"id": 1,
"company": "Tech Innovations Inc.",
"position": "Senior Frontend Developer",
"duration": "2019 - Present",
"description": "Lead frontend development for enterprise SaaS platform. Improved performance by 40% through code optimization. Mentored junior developers."
},
{
"id": 2,
"company": "WebSolutions Co.",
"position": "Frontend Developer",
"duration": "2017 - 2019",
"description": "Developed responsive web applications using React. Collaborated with design team to implement UI/UX improvements."
}
],
"education": [
{
"id": 1,
"institution": "University of California, Berkeley",
"degree": "B.S. Computer Science",
"duration": "2013 - 2017"
}
],
"skills": ["React", "JavaScript", "TypeScript", "HTML/CSS", "Redux", "Next.js", "Tailwind CSS", "UI/UX Design"]
}
if "dark_mode" not in st.session_state:
st.session_state.dark_mode = False
if "show_preview" not in st.session_state:
st.session_state.show_preview = True
if "new_skill" not in st.session_state:
st.session_state.new_skill = ""
if "job_description" not in st.session_state:
st.session_state.job_description = ""
if "ai_suggestions" not in st.session_state:
st.session_state.ai_suggestions = {}
if "cover_letter" not in st.session_state:
st.session_state.cover_letter = ""
if "suggested_skills" not in st.session_state:
st.session_state.suggested_skills = []
# Define a flag to check if API functionality is available
if "gemini_available" not in st.session_state:
st.session_state.gemini_available = False
# Try to import Google Generative AI library, but make it optional
try:
import google.generativeai as genai
st.session_state.gemini_import_success = True
except ImportError:
st.session_state.gemini_import_success = False
# Apply custom styling based on dark/light mode
def apply_custom_styling():
dark_mode = st.session_state.dark_mode
primary_color = "#4F46E5" # Indigo
if dark_mode:
background_color = "#111827"
text_color = "#F9FAFB"
card_bg = "#1F2937"
input_bg = "#374151"
secondary_bg = "#1E3A8A"
accent_light = "#818CF8"
else:
background_color = "#F9FAFB"
text_color = "#111827"
card_bg = "#FFFFFF"
input_bg = "#F3F4F6"
secondary_bg = "#EEF2FF"
accent_light = "#C7D2FE"
css = f"""
"""
st.markdown(css, unsafe_allow_html=True)
# Function to download resume as JSON
def download_resume_json():
json_str = json.dumps(st.session_state.resume_data, indent=2)
b64 = base64.b64encode(json_str.encode()).decode()
filename = f"resume_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
href = f'π Download JSON'
return href
# Function to create a placeholder for PDF export
def download_resume_pdf():
# In a real app, you would generate an actual PDF here
# For this example, we'll just return a placeholder button
return ''
# Initialize Gemini API (modified to handle missing API key gracefully)
def initialize_gemini_api():
if not st.session_state.gemini_import_success:
st.warning("Google Generative AI library not installed. Install with: `pip install google-generativeai`")
return False
try:
# Get API key from session state first (from text input)
api_key = st.session_state.get("api_key", "")
# Only try to configure if API key is provided
if api_key:
genai.configure(api_key=api_key)
# Test the API with a simple call to verify it works
model = genai.GenerativeModel(model_name="gemini-1.5-pro")
_ = model.generate_content("Hello")
return True
else:
# No API key provided yet, not an error
return False
except Exception as e:
st.error(f"Failed to initialize Gemini API: {str(e)}")
return False
# Function to get Gemini model response
def get_gemini_response(prompt, temperature=0.7):
if not st.session_state.gemini_available:
st.warning("Gemini API not available. Please set up your API key.")
return None
try:
# Create the model
generation_config = {
"temperature": temperature,
"top_p": 0.95,
"top_k": 64,
"max_output_tokens": 8192,
}
model = genai.GenerativeModel(
model_name="gemini-1.5-pro",
generation_config=generation_config,
)
response = model.generate_content(prompt)
return response.text
except Exception as e:
st.error(f"Error getting AI response: {str(e)}")
return None
# Main application header
def render_header():
col1, col2 = st.columns([6, 4])
with col1:
ai_badge = 'AI-Ready' if st.session_state.gemini_import_success else ""
st.markdown(f'
π ResumeBuilder Pro {ai_badge}
', unsafe_allow_html=True)
with col2:
download_buttons = f"""
{download_resume_json()}
{download_resume_pdf()}
"""
st.markdown(download_buttons, unsafe_allow_html=True)
# Hidden buttons that are triggered by the custom buttons above
col2_1, col2_2 = st.columns(2)
with col2_1:
if st.button("Toggle Theme", key="baseButton-secondary", type="secondary"):
st.session_state.dark_mode = not st.session_state.dark_mode
st.rerun()
with col2_2:
if st.button("Toggle Preview", key="baseButton-primary"):
st.session_state.show_preview = not st.session_state.show_preview
st.rerun()
# API Key Management - Improved to be more user-friendly and handle errors better
def manage_api_key():
if "api_key" not in st.session_state:
st.session_state.api_key = ""
st.markdown('
', unsafe_allow_html=True)
# Show different message if the generative AI library is not installed
if not st.session_state.gemini_import_success:
st.markdown("### π€ Enable AI Features")
st.markdown("""
To use AI features, you need to install the Google Generative AI library:
```
pip install google-generativeai
```
After installation, restart the app to access AI-powered resume enhancements.
""")
st.markdown('
', unsafe_allow_html=True)
return
# If library is installed but API key not set
st.markdown("### π Gemini API Setup")
st.markdown("To use AI features, enter your Google Gemini API key below:")
api_key = st.text_input("API Key",
value=st.session_state.api_key,
type="password",
placeholder="Enter your Gemini API key here",
key="api_key_input")
col1, col2 = st.columns([1, 3])
with col1:
if st.button("Save API Key", key="save_api_key_button"):
st.session_state.api_key = api_key
if initialize_gemini_api():
st.session_state.gemini_available = True
st.success("API key saved and verified successfully!")
else:
if api_key:
st.error("Invalid API key. Please check and try again.")
else:
st.warning("Please enter an API key to enable AI features.")
with col2:
st.markdown("Get your API key from [Google AI Studio](https://makersuite.google.com/app/apikey)")
# Show what AI features are available
if not st.session_state.gemini_available:
st.markdown('
', unsafe_allow_html=True)
st.markdown("### β¨ Unlock AI Features")
st.markdown("""
By adding your API key, you'll unlock powerful AI features:
- **Professional Summary Generator**: Create compelling summaries automatically
- **Job Match Analysis**: Score your resume against job descriptions
- **Skills Recommendations**: Get tailored skill suggestions for your role
- **Experience Description Enhancement**: Make your work history more impactful
- **Cover Letter Generator**: Create customized cover letters in seconds
""")
st.markdown('
', unsafe_allow_html=True)
st.subheader("Personal Information")
# Personal info fields
st.session_state.resume_data["fullName"] = st.text_input("Full Name", value=st.session_state.resume_data["fullName"], key="fullname_input")
st.session_state.resume_data["title"] = st.text_input("Professional Title", value=st.session_state.resume_data["title"], key="title_input")
col1_1, col1_2 = st.columns(2)
with col1_1:
st.session_state.resume_data["email"] = st.text_input("Email", value=st.session_state.resume_data["email"], key="email_input")
with col1_2:
st.session_state.resume_data["phone"] = st.text_input("Phone", value=st.session_state.resume_data["phone"], key="phone_input")
st.session_state.resume_data["location"] = st.text_input("Location", value=st.session_state.resume_data["location"], key="location_input")
# Summary with AI assistance
st.markdown("### Professional Summary")
# Show AI button if API is available
if st.session_state.gemini_available:
sum_col1, sum_col2 = st.columns([5, 1])
with sum_col1:
st.session_state.resume_data["summary"] = st.text_area(
"Summary (Professional Overview)",
value=st.session_state.resume_data["summary"],
height=150,
key="summary_input"
)
with sum_col2:
if st.button("β¨ AI Generate", key="generate_summary"):
summary = generate_ai_summary()
if summary:
st.session_state.resume_data["summary"] = summary
st.rerun()
else:
st.session_state.resume_data["summary"] = st.text_area(
"Summary (Professional Overview)",
value=st.session_state.resume_data["summary"],
height=150,
key="summary_input_no_ai"
)
st.markdown('
', unsafe_allow_html=True) # End of personal info card
# Experience section
st.markdown('
', unsafe_allow_html=True)
st.subheader("Experience")
# Create fields for each experience item
for i, exp in enumerate(st.session_state.resume_data["experience"]):
col_exp1, col_exp2, col_exp3 = st.columns([4, 4, 2])
with col_exp1:
st.session_state.resume_data["experience"][i]["position"] = st.text_input(
"Position/Title",
value=exp["position"],
key=f"position_{i}"
)
with col_exp2:
st.session_state.resume_data["experience"][i]["company"] = st.text_input(
"Company",
value=exp["company"],
key=f"company_{i}"
)
with col_exp3:
st.session_state.resume_data["experience"][i]["duration"] = st.text_input(
"Duration",
value=exp["duration"],
key=f"duration_{i}"
)
# Description with AI enhancement option
if st.session_state.gemini_available:
desc_col1, desc_col2 = st.columns([5, 1])
with desc_col1:
st.session_state.resume_data["experience"][i]["description"] = st.text_area(
"Description",
value=exp["description"],
height=100,
key=f"description_{i}"
)
with desc_col2:
if st.button("β¨ Enhance", key=f"enhance_desc_{i}"):
prompt = f"""
You are an expert resume writer. Improve this job description to make it more impactful and professional:
"{exp["description"]}"
Guidelines:
1. Use strong action verbs
2. Quantify achievements where possible
3. Highlight skills and technologies used
4. Keep similar length but make it more impactful
5. Focus on results and contributions
Return ONLY the improved description without quotes or explanations.
"""
enhanced_desc = get_gemini_response(prompt, temperature=0.7)
if enhanced_desc:
st.session_state.resume_data["experience"][i]["description"] = enhanced_desc.strip()
st.rerun()
else:
st.session_state.resume_data["experience"][i]["description"] = st.text_area(
"Description",
value=exp["description"],
height=100,
key=f"description_no_ai_{i}"
)
# Add buttons to remove this experience
if st.button("ποΈ Remove this experience", key=f"remove_exp_{i}"):
if i < len(st.session_state.resume_data["experience"]):
st.session_state.resume_data["experience"].pop(i)
st.rerun()
st.markdown("", unsafe_allow_html=True)
# Add new experience button
if st.button("β Add New Experience", key="add_new_experience"):
# Find the highest ID and increment by 1
max_id = 0
for exp in st.session_state.resume_data["experience"]:
if exp["id"] > max_id:
max_id = exp["id"]
st.session_state.resume_data["experience"].append({
"id": max_id + 1,
"company": "New Company",
"position": "Position Title",
"duration": "Start - End Date",
"description": "Describe your responsibilities and achievements."
})
st.rerun()
st.markdown('
', unsafe_allow_html=True) # End of experience card
# Education section
with st.container():
st.markdown('
', unsafe_allow_html=True)
st.subheader("Education")
# Create fields for each education item
for i, edu in enumerate(st.session_state.resume_data["education"]):
with st.container():
col_edu1, col_edu2, col_edu3 = st.columns([4, 4, 2], gap="small")
with col_edu1:
st.session_state.resume_data["education"][i]["institution"] = st.text_input(
"Institution",
value=edu["institution"],
key=f"edit_institution_{i}"
)
with col_edu2:
st.session_state.resume_data["education"][i]["degree"] = st.text_input(
"Degree",
value=edu["degree"],
key=f"edit_degree_{i}"
)
with col_edu3:
st.session_state.resume_data["education"][i]["duration"] = st.text_input(
"Duration",
value=edu["duration"],
key=f"edit_education_duration_{i}"
)
# Add button to remove this education
if st.button("ποΈ Remove this education", key=f"remove_edu_{i}"):
st.session_state.resume_data["education"].pop(i)
st.rerun()
st.markdown("", unsafe_allow_html=True)
# Add new education button
if st.button("β Add New Education", key="add_new_education"):
# Find the highest ID and increment by 1
max_id = 0
for edu in st.session_state.resume_data["education"]:
if edu["id"] > max_id:
max_id = edu["id"]
st.session_state.resume_data["education"].append({
"id": max_id + 1,
"institution": "University/College Name",
"degree": "Degree Name",
"duration": "Start - End Date"
})
st.rerun()
st.markdown('
', unsafe_allow_html=True) # End of education card
# Skills section
with st.container():
st.markdown('
', unsafe_allow_html=True)
st.subheader("Skills")
# Display current skills as tags with remove option
with st.container():
st.markdown('
', unsafe_allow_html=True)
for i, skill in enumerate(st.session_state.resume_data["skills"]):
st.markdown(
f'{skill} ',
unsafe_allow_html=True
)
# Hidden button that will be triggered by the β in the skill tag
if st.button("β", key=f"remove_skill_{i}", type="secondary"):
st.session_state.resume_data["skills"].remove(skill)
st.rerun()
st.markdown('
', unsafe_allow_html=True)
# Add new skill
col_skill1, col_skill2 = st.columns([4, 1])
with col_skill1:
new_skill = st.text_input("Add Skill", value=st.session_state.new_skill, key="new_skill_input")
st.session_state.new_skill = new_skill
with col_skill2:
if st.button("Add", key="add_skill_button") and st.session_state.new_skill.strip():
if st.session_state.new_skill not in st.session_state.resume_data["skills"]:
st.session_state.resume_data["skills"].append(st.session_state.new_skill)
st.session_state.new_skill = ""
st.rerun()
# AI skill suggestions if API is available
if st.session_state.gemini_available:
if st.button("β¨ Suggest relevant skills", key="suggest_skills_section1"):
with st.spinner("Generating skill suggestions..."):
# Create prompt using current resume data
prompt = f"""
You are a career advisor. Based on this person's profile, suggest 5-7 relevant technical skills they might want to add to their resume.
Current professional title: {st.session_state.resume_data["title"]}
Current skills: {', '.join(st.session_state.resume_data["skills"])}
Experience:
{' '.join([f"{exp['position']} at {exp['company']}: {exp['description']}" for exp in st.session_state.resume_data["experience"]])}
Return ONLY a Python list of strings with the skill names, like this:
["Skill 1", "Skill 2", "Skill 3", "Skill 4", "Skill 5"]
Suggest only skills that are NOT already listed. Focus on technical and professional skills relevant to their field.
"""
skills_response = get_gemini_response(prompt, temperature=0.2)
if skills_response:
try:
# Improved parsing of AI response - more robust handling
# First try to find a list pattern in the response
list_pattern = re.search(r'\[.*?\]', skills_response, re.DOTALL)
if list_pattern:
list_str = list_pattern.group(0)
# Try to safely parse the list
try:
suggested_skills = json.loads(list_str)
if isinstance(suggested_skills, list):
# Filter out skills they already have
st.session_state.suggested_skills = [s for s in suggested_skills if s not in st.session_state.resume_data["skills"]]
else:
st.error("AI response was not in the expected format. Please try again.")
except json.JSONDecodeError:
# If JSON parsing fails, try a more lenient approach
# Extract items that look like skills from the response
skill_matches = re.findall(r'"([^"]+)"', list_str)
if skill_matches:
st.session_state.suggested_skills = [s for s in skill_matches if s not in st.session_state.resume_data["skills"]]
else:
st.error("Could not parse skills from AI response. Please try again.")
else:
# If no list pattern found, look for quoted strings that might be skills
skill_matches = re.findall(r'"([^"]+)"', skills_response)
if skill_matches:
st.session_state.suggested_skills = [s for s in skill_matches if s not in st.session_state.resume_data["skills"]]
else:
st.error("Could not identify skills in AI response. Please try again.")
except Exception as e:
st.error(f"Error processing skill suggestions: {str(e)}")
# Display suggested skills if any
if st.session_state.suggested_skills:
st.markdown('
', unsafe_allow_html=True)
st.markdown("##### Suggested Skills:")
for i, skill in enumerate(st.session_state.suggested_skills):
st.markdown(
f'{skill} ',
unsafe_allow_html=True
)
# Hidden button that will be triggered by the + in the skill tag
if st.button("+", key=f"add_suggested_skill_{i}", type="secondary"):
if skill not in st.session_state.resume_data["skills"]:
st.session_state.resume_data["skills"].append(skill)
st.session_state.suggested_skills.remove(skill)
st.rerun()
st.markdown('
', unsafe_allow_html=True)
st.markdown('
', unsafe_allow_html=True) # End of skills card
# Preview in right column (if enabled)
if st.session_state.show_preview:
if col2 is not None:
with col2:
st.markdown('
', unsafe_allow_html=True)
# Header section with clean styling
st.markdown('
', unsafe_allow_html=True)
st.markdown(f'
{st.session_state.resume_data["fullName"]}
', unsafe_allow_html=True)
st.markdown(f'
{st.session_state.resume_data["title"]}
', unsafe_allow_html=True)
# Contact info with better spacing
contact_info = f'
', unsafe_allow_html=True)
st.subheader("Import Resume")
uploaded_file = st.file_uploader("Import Resume JSON", type=["json"], key="resume_uploader")
if uploaded_file is not None:
try:
imported_data = json.loads(uploaded_file.getvalue().decode())
# Validate the imported data has the required fields
required_fields = ["fullName", "title", "email", "phone", "location", "summary", "experience", "education", "skills"]
if all(field in imported_data for field in required_fields):
st.session_state.resume_data = imported_data
st.success("Resume data imported successfully!")
st.rerun()
else:
st.error("Invalid resume data format. Please upload a valid JSON file.")
except Exception as e:
st.error(f"Error importing data: {str(e)}")
st.markdown('
', unsafe_allow_html=True) # End of import card
# About section
st.markdown('
', unsafe_allow_html=True)
st.subheader("About ResumeBuilder Pro")
st.markdown("""
**ResumeBuilder Pro** is a streamlined tool for creating, editing, and optimizing resumes.
Features:
- Create and edit professional resumes with a user-friendly interface
- AI-powered suggestions to enhance your resume content (with Gemini API)
- Match your resume against job descriptions
- Generate customized cover letters
- Export your resume in various formats
Version 1.0.0
""")
st.markdown('
', unsafe_allow_html=True) # End of about card
# Job Match Tab
with tabs[1]:
st.markdown('
', unsafe_allow_html=True)
st.subheader("Resume-Job Matching")
if not st.session_state.gemini_available:
manage_api_key()
else:
st.markdown("""
Analyze how well your resume matches a specific job description.
Paste the job description below to get personalized recommendations.
""")
# Job description input
st.session_state.job_description = st.text_area(
"Paste the job description here",
height=200,
value=st.session_state.job_description,
key="job_description_input"
)
if st.button("π Analyze Match", key="analyze_match_button") and st.session_state.job_description:
analysis = analyze_job_description(st.session_state.job_description)
if analysis:
st.session_state.ai_suggestions = analysis
# Extract suggested skills for adding to resume
if "missing_skills" in analysis:
st.session_state.suggested_skills = analysis["missing_skills"]
# Display analysis results if available
if st.session_state.ai_suggestions and "match_percentage" in st.session_state.ai_suggestions:
match_percentage = st.session_state.ai_suggestions["match_percentage"]
st.markdown(f"### Resume Match: {match_percentage}%")
# Match percentage meter
st.markdown(f'
', unsafe_allow_html=True)
# Missing skills
if "missing_skills" in st.session_state.ai_suggestions and st.session_state.ai_suggestions["missing_skills"]:
st.markdown('
', unsafe_allow_html=True)
st.markdown("#### Missing Skills")
st.markdown("Consider adding these skills to your resume:")
for skill in st.session_state.ai_suggestions["missing_skills"]:
skill_hash = hash(skill)
st.markdown(
f'{skill} ',
unsafe_allow_html=True
)
if st.button("+", key=f"add_missing_skill_{skill_hash}", type="secondary"):
if skill not in st.session_state.resume_data["skills"]:
st.session_state.resume_data["skills"].append(skill)
st.rerun()
st.markdown('
', unsafe_allow_html=True)
# Highlight skills
if "highlight_skills" in st.session_state.ai_suggestions and st.session_state.ai_suggestions["highlight_skills"]:
st.markdown('
', unsafe_allow_html=True)
st.markdown("#### Relevant Skills")
st.markdown("These skills from your resume are particularly relevant:")
for skill in st.session_state.ai_suggestions["highlight_skills"]:
st.markdown(f'{skill}', unsafe_allow_html=True)
st.markdown('
', unsafe_allow_html=True)
# Emphasis suggestions
if "emphasis_suggestions" in st.session_state.ai_suggestions and st.session_state.ai_suggestions["emphasis_suggestions"]:
st.markdown('
', unsafe_allow_html=True)
st.markdown("#### Emphasis Suggestions")
st.markdown("Consider emphasizing these aspects of your experience:")
for suggestion in st.session_state.ai_suggestions["emphasis_suggestions"]:
st.markdown(f"- {suggestion}")
st.markdown('
', unsafe_allow_html=True)
# Improvement tips
if "improvement_tips" in st.session_state.ai_suggestions and st.session_state.ai_suggestions["improvement_tips"]:
st.markdown('
', unsafe_allow_html=True)
st.markdown("#### Improvement Tips")
for tip in st.session_state.ai_suggestions["improvement_tips"]:
st.markdown(f"- {tip}")
st.markdown('
', unsafe_allow_html=True)
# Generate optimized summary button
if st.button("β¨ Generate Optimized Summary", key="generate_optimized_summary"):
summary = generate_ai_summary()
if summary: # Only update if summary was successfully generated
st.session_state.resume_data["summary"] = summary
st.success("Professional summary updated to better match the job description!")
st.rerun()
st.markdown('
', unsafe_allow_html=True) # End of job match card
# Cover Letter Tab
with tabs[2]:
st.markdown('
', unsafe_allow_html=True)
st.subheader("Cover Letter Generator")
if not st.session_state.gemini_available:
manage_api_key()
else:
st.markdown("""
Generate a customized cover letter based on your resume and a job description.
Paste the job description below to create a tailored cover letter.
""")
# Job description input for cover letter
job_desc_for_letter = st.text_area(
"Paste the job description here",
height=200,
value=st.session_state.job_description,
key="job_description_for_letter"
)
if st.button("β¨ Generate Cover Letter", key="generate_cover_letter") and job_desc_for_letter:
cover_letter = generate_cover_letter(job_desc_for_letter)
if cover_letter:
st.session_state.cover_letter = cover_letter
# Display and edit cover letter
if st.session_state.cover_letter:
st.markdown("### Your Cover Letter")
st.session_state.cover_letter = st.text_area(
"Edit your cover letter as needed",
value=st.session_state.cover_letter,
height=400,
key="cover_letter_editor"
)
# Display formatted cover letter
st.markdown('
', unsafe_allow_html=True)
# Copy to clipboard button (note: this is a placeholder, as direct clipboard access requires JavaScript)
st.markdown(
f'',
unsafe_allow_html=True
)
st.markdown('
', unsafe_allow_html=True) # End of cover letter card
# Entry point of the application
if __name__ == "__main__":
# Try to import the Gemini library
try:
import google.generativeai as genai
ST_GEMINI_IMPORT_SUCCESS = True
except ImportError:
ST_GEMINI_IMPORT_SUCCESS = False
# Initialize session state variables if they don't exist
if "initialized" not in st.session_state:
# General app state
st.session_state.initialized = True
st.session_state.dark_mode = True # Default to dark mode
st.session_state.show_preview = True # Show preview by default
# API and AI state
st.session_state.gemini_import_success = ST_GEMINI_IMPORT_SUCCESS
st.session_state.gemini_available = False
st.session_state.api_key = ""
# Resume data
st.session_state.resume_data = {
"fullName": "Alexander Johnson",
"title": "Senior Frontend Developer",
"email": "alex.johnson@example.com",
"phone": "(555) 123-4567",
"location": "San Francisco, CA",
"summary": "Experienced frontend developer with 6+ years specializing in React and modern JavaScript frameworks. Passionate about creating intuitive user interfaces and optimizing web performance.",
"experience": [
{
"id": 1,
"company": "Tech Innovations Inc.",
"position": "Senior Frontend Developer",
"duration": "2019 - Present",
"description": "Lead frontend development for enterprise SaaS platform. Improved performance by 40% through code optimization. Mentored junior developers."
},
{
"id": 2,
"company": "WebSolutions Co.",
"position": "Frontend Developer",
"duration": "2017 - 2019",
"description": "Developed responsive web applications using React. Collaborated with design team to implement UI/UX improvements."
}
],
"education": [
{
"id": 1,
"institution": "University of California, Berkeley",
"degree": "B.S. Computer Science",
"duration": "2013 - 2017"
}
],
"skills": ["React", "JavaScript", "TypeScript", "HTML/CSS", "Redux", "Next.js", "Tailwind CSS", "UI/UX Design"]
}
st.session_state.new_skill = ""
st.session_state.suggested_skills = []
# Job matching
st.session_state.ai_suggestions = {}
st.session_state.job_description = ""
# Cover letter
st.session_state.cover_letter = ""
# Run the main application
main()