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import streamlit as st
import pdfplumber
import re
import pandas as pd
import matplotlib.pyplot as plt
import torch
from datetime import datetime
import plotly.express as px
import plotly.graph_objects as go
import numpy as np
# Display startup message
st.set_page_config(
page_title="Resume Screener & Skill Extractor",
page_icon="π",
layout="wide"
)
st.title("π Resume Screener & Skill Extractor")
startup_message = st.empty()
startup_message.info("Loading dependencies and models... This may take a minute on first run.")
# Import dependencies with fallbacks
try:
import spacy
spacy_available = True
except ImportError:
spacy_available = False
st.warning("spaCy is not available. Some features will be limited.")
try:
from transformers import pipeline
transformers_available = True
except ImportError:
transformers_available = False
st.warning("Transformers is not available. Summary generation will be limited.")
try:
import nltk
from nltk.tokenize import word_tokenize
nltk_available = True
# Download required NLTK resources
try:
nltk.data.find('tokenizers/punkt')
except LookupError:
nltk.download('punkt')
except ImportError:
nltk_available = False
st.warning("NLTK is not available. Some text processing features will be limited.")
# Custom sentence-transformers fallback
try:
from sentence_transformers import SentenceTransformer
try:
from sentence_transformers import util as st_util
sentence_transformers_available = True
except ImportError:
# Define our own utility functions
class CustomSTUtil:
@staticmethod
def pytorch_cos_sim(a, b):
if not isinstance(a, torch.Tensor):
a = torch.tensor(a)
if not isinstance(b, torch.Tensor):
b = torch.tensor(b)
if len(a.shape) == 1:
a = a.unsqueeze(0)
if len(b.shape) == 1:
b = b.unsqueeze(0)
a_norm = torch.nn.functional.normalize(a, p=2, dim=1)
b_norm = torch.nn.functional.normalize(b, p=2, dim=1)
return torch.mm(a_norm, b_norm.transpose(0, 1))
st_util = CustomSTUtil()
sentence_transformers_available = True
except ImportError:
sentence_transformers_available = False
st.warning("Sentence Transformers is not available. Semantic matching will be disabled.")
# Load models with exception handling
@st.cache_resource
def load_models():
models = {}
# Load spaCy if available
if spacy_available:
try:
models['nlp'] = spacy.load("en_core_web_sm")
except OSError:
try:
import subprocess
import sys
subprocess.check_call([sys.executable, "-m", "spacy", "download", "en_core_web_sm"])
models['nlp'] = spacy.load("en_core_web_sm")
except Exception as e:
st.warning(f"Could not load spaCy model: {e}")
models['nlp'] = None
else:
models['nlp'] = None
# Load summarizer if transformers available
if transformers_available:
try:
models['summarizer'] = pipeline("summarization", model="facebook/bart-large-cnn")
except Exception as e:
st.warning(f"Could not load summarizer model: {e}")
# Simple fallback summarizer
models['summarizer'] = lambda text, **kwargs: [{"summary_text": ". ".join(text.split(". ")[:5]) + "."}]
else:
# Simple fallback summarizer
models['summarizer'] = lambda text, **kwargs: [{"summary_text": ". ".join(text.split(". ")[:5]) + "."}]
# Load sentence transformer if available
if sentence_transformers_available:
try:
models['sentence_model'] = SentenceTransformer('paraphrase-MiniLM-L6-v2')
except Exception as e:
st.warning(f"Could not load sentence transformer model: {e}")
models['sentence_model'] = None
else:
models['sentence_model'] = None
return models
# Job descriptions dictionary
job_descriptions = {
"Software Engineer": {
"skills": ["python", "java", "javascript", "sql", "algorithms", "data structures",
"git", "cloud", "web development", "software development", "coding"],
"description": "Looking for software engineers with strong programming skills and experience in software development.",
"must_have": ["python", "git", "algorithms"],
"nice_to_have": ["cloud", "java", "javascript"],
"seniority_levels": {
"Junior": "0-2 years of experience, familiar with basic programming concepts",
"Mid-level": "3-5 years of experience, proficient in multiple languages, experience with system design",
"Senior": "6+ years of experience, expert in software architecture, mentoring, and leading projects"
}
},
"Interaction Designer": {
"skills": ["ui", "ux", "user research", "wireframing", "prototyping", "figma",
"sketch", "adobe", "design thinking", "interaction design"],
"description": "Seeking interaction designers with expertise in user experience and interface design.",
"must_have": ["ui", "ux", "prototyping"],
"nice_to_have": ["figma", "sketch", "user research"],
"seniority_levels": {
"Junior": "0-2 years of experience, basic design skills, understanding of UX principles",
"Mid-level": "3-5 years of experience, strong portfolio, experience with user research",
"Senior": "6+ years of experience, leadership in design systems, driving design strategy"
}
},
"Data Scientist": {
"skills": ["python", "r", "statistics", "machine learning", "data analysis",
"sql", "tensorflow", "pytorch", "pandas", "numpy"],
"description": "Looking for data scientists with strong analytical and machine learning skills.",
"must_have": ["python", "statistics", "machine learning"],
"nice_to_have": ["tensorflow", "pytorch", "r"],
"seniority_levels": {
"Junior": "0-2 years of experience, basic knowledge of statistics and ML algorithms",
"Mid-level": "3-5 years of experience, model development, feature engineering",
"Senior": "6+ years of experience, advanced ML techniques, research experience"
}
}
}
# Core functionality
def extract_text_from_pdf(pdf_file):
"""Extract text from PDF file."""
text = ""
try:
with pdfplumber.open(pdf_file) as pdf:
for page in pdf.pages:
text += page.extract_text() or ""
except Exception as e:
st.error(f"Error extracting text from PDF: {e}")
return text
def extract_skills(text, job_title, nlp=None):
"""Extract skills from resume text."""
found_skills = []
required_skills = job_descriptions[job_title]["skills"]
# Simple keyword matching (no NLP needed)
for skill in required_skills:
if skill.lower() in text.lower():
found_skills.append(skill)
return found_skills
def extract_experience(text):
"""Extract work experience from resume text."""
experiences = []
# Define regex pattern for experiences
experience_pattern = r"(?i)(\w+[\w\s&,.']+)\s*(?:[-|β’]|\bat\b)\s*([A-Za-z][\w\s&,.']+)\s*(?:[-|β’]|\bfrom\b)\s*(\d{4}(?:\s*[-β]\s*(?:\d{4}|present|current)))"
matches = re.finditer(experience_pattern, text)
for match in matches:
company = match.group(1).strip()
role = match.group(2).strip()
duration = match.group(3).strip()
# Process dates
try:
date_parts = re.split(r'[-β]', duration)
start_year = int(date_parts[0].strip())
if len(date_parts) > 1 and 'present' not in date_parts[1].lower() and 'current' not in date_parts[1].lower():
end_year = int(date_parts[1].strip())
end_date = datetime(end_year, 12, 31)
else:
end_year = datetime.now().year
end_date = datetime.now()
start_date = datetime(start_year, 1, 1)
duration_months = (end_date.year - start_date.year) * 12 + (end_date.month - start_date.month)
experiences.append({
'company': company,
'role': role,
'start_date': start_date,
'end_date': end_date,
'duration_months': duration_months
})
except:
experiences.append({
'company': company,
'role': role,
'duration': duration
})
return experiences
def analyze_resume(text, job_title, models):
"""Analyze resume text."""
# Extract skills
found_skills = extract_skills(text, job_title, models.get('nlp'))
# Generate summary
if models.get('summarizer'):
try:
summary = models['summarizer'](text[:3000], max_length=150, min_length=50, do_sample=False)[0]["summary_text"]
except Exception as e:
st.warning(f"Error generating summary: {e}")
summary = text[:500] + "..."
else:
summary = text[:500] + "..."
# Extract work experience
experiences = extract_experience(text)
# Calculate semantic match score
match_score = 0
if models.get('sentence_model') and sentence_transformers_available:
try:
resume_embedding = models['sentence_model'].encode(text[:5000], convert_to_tensor=True)
job_embedding = models['sentence_model'].encode(job_descriptions[job_title]["description"], convert_to_tensor=True)
match_score = float(st_util.pytorch_cos_sim(resume_embedding, job_embedding)[0][0]) * 100
except Exception as e:
st.warning(f"Error calculating semantic match: {e}")
else:
# Fallback to keyword-based score
match_score = (len(found_skills) / len(job_descriptions[job_title]["skills"])) * 100
# Calculate seniority level
years_exp = sum(exp.get('duration_months', 0) for exp in experiences if 'duration_months' in exp) / 12
if years_exp < 3:
seniority = "Junior"
elif years_exp < 6:
seniority = "Mid-level"
else:
seniority = "Senior"
# Detect skill levels
skill_levels = {}
for skill in found_skills:
# Default level
skill_levels[skill] = "intermediate"
# Look for advanced indicators
advanced_patterns = [
f"expert in {skill}",
f"advanced {skill}",
f"extensive experience with {skill}"
]
if any(pattern in text.lower() for pattern in advanced_patterns):
skill_levels[skill] = "advanced"
# Look for basic indicators
basic_patterns = [
f"familiar with {skill}",
f"basic knowledge of {skill}",
f"introduced to {skill}"
]
if any(pattern in text.lower() for pattern in basic_patterns):
skill_levels[skill] = "basic"
# Check for inconsistencies in timeline
inconsistencies = []
if len(experiences) >= 2:
# Sort experiences by start date
sorted_exps = sorted(
[exp for exp in experiences if 'start_date' in exp],
key=lambda x: x['start_date']
)
# Check for overlaps
for i in range(len(sorted_exps) - 1):
current = sorted_exps[i]
next_exp = sorted_exps[i+1]
if current['end_date'] > next_exp['start_date']:
inconsistencies.append({
'type': 'overlap',
'description': f"Overlapping roles at {current['company']} and {next_exp['company']}"
})
# Generate a simple career prediction
career_prediction = predict_career_path(seniority, job_title)
return {
'found_skills': found_skills,
'skill_levels': skill_levels,
'summary': summary,
'experiences': experiences,
'match_score': match_score,
'seniority': seniority,
'years_experience': years_exp,
'inconsistencies': inconsistencies,
'career_prediction': career_prediction
}
def predict_career_path(seniority, job_title):
"""Generate a simple career prediction."""
if seniority == "Junior":
return f"Next potential role: Senior {job_title}"
elif seniority == "Mid-level":
roles = {
"Software Engineer": "Team Lead, Technical Lead, or Engineering Manager",
"Data Scientist": "Senior Data Scientist or Data Science Lead",
"Interaction Designer": "Senior Designer or UX Lead"
}
return f"Next potential roles: {roles.get(job_title, f'Senior {job_title}')}"
else: # Senior
roles = {
"Software Engineer": "Engineering Manager, Software Architect, or CTO",
"Data Scientist": "Head of Data Science, ML Engineering Manager, or Chief Data Officer",
"Interaction Designer": "Design Director, Head of UX, or VP of Design"
}
return f"Next potential roles: {roles.get(job_title, f'Director of {job_title}')}"
def generate_career_advice(resume_text, job_title, found_skills, missing_skills):
"""Generate career advice based on resume analysis."""
advice = f"""## Career Development Plan for {job_title}
### Skills to Develop
The following skills would strengthen your profile for this position:
"""
for skill in missing_skills:
advice += f"- **{skill.title()}**: "
if skill == "python":
advice += "Take online courses like Coursera's Python for Everybody or follow tutorials on Real Python."
elif skill == "java":
advice += "Complete the Oracle Java Certification or contribute to open-source Java projects."
elif skill == "javascript":
advice += "Build interactive web applications using modern frameworks like React or Vue."
elif skill == "cloud":
advice += "Get hands-on experience with AWS, Azure, or GCP through their free tier offerings."
elif "algorithm" in skill or "data structure" in skill:
advice += "Practice on platforms like LeetCode or HackerRank and study algorithm design principles."
elif "ui" in skill or "ux" in skill:
advice += "Create a portfolio of design work and study interaction design principles."
elif "machine learning" in skill:
advice += "Take Andrew Ng's Machine Learning course on Coursera and work on ML projects with real datasets."
else:
advice += f"Research and practice this skill through online courses, tutorials, and hands-on projects."
advice += "\n\n"
advice += f"""
### Project Ideas
Consider these projects to showcase your skills for a {job_title} position:
"""
if job_title == "Software Engineer":
advice += """
1. **Full-Stack Web Application**: Build a complete web app with frontend, backend, and database
2. **API Service**: Create a RESTful or GraphQL API with proper authentication and documentation
3. **Open Source Contribution**: Contribute to relevant open-source projects in your area of interest
"""
elif job_title == "Data Scientist":
advice += """
1. **Predictive Model**: Build and deploy a machine learning model that solves a real-world problem
2. **Data Dashboard**: Create an interactive visualization dashboard for complex datasets
3. **Natural Language Processing**: Develop a text classification or sentiment analysis project
"""
elif job_title == "Interaction Designer":
advice += """
1. **Design System**: Create a comprehensive design system with components and usage guidelines
2. **UX Case Study**: Document your design process for a real or fictional product improvement
3. **Interactive Prototype**: Design a fully functional prototype that demonstrates your interaction design skills
"""
advice += """
### Learning Resources
- **Online Platforms**: Coursera, Udemy, Pluralsight, LinkedIn Learning
- **Practice Sites**: GitHub, HackerRank, LeetCode, Kaggle
- **Communities**: Stack Overflow, Reddit programming communities, relevant Discord servers
"""
return advice
# Load models
models = load_models()
# Clear startup message
startup_message.empty()
# App description
st.markdown("""
This app helps recruiters analyze resumes by:
- Extracting relevant skills for specific job positions
- Generating a concise summary of the candidate's background
- Identifying skill gaps for the selected role
- Providing personalized career advice and project recommendations
""")
# Create two columns
col1, col2 = st.columns([2, 1])
with col1:
# File upload
uploaded_file = st.file_uploader("Upload Resume (PDF)", type=["pdf"])
with col2:
# Job selection
job_title = st.selectbox("Select Job Position", list(job_descriptions.keys()))
# Show job description
if job_title:
st.info(f"**Required Skills:**\n" +
"\n".join([f"- {skill.title()}" for skill in job_descriptions[job_title]["skills"]]))
if uploaded_file and job_title:
try:
# Show spinner while processing
with st.spinner("Analyzing resume..."):
# Extract text from PDF
text = extract_text_from_pdf(uploaded_file)
# Analyze resume
analysis_results = analyze_resume(text, job_title, models)
# Calculate missing skills
missing_skills = [skill for skill in job_descriptions[job_title]["skills"]
if skill not in analysis_results['found_skills']]
# Display results in tabs
tab1, tab2, tab3, tab4 = st.tabs([
"π Skills Match",
"π Resume Summary",
"π― Skills Gap",
"π Career Advice"
])
with tab1:
# Create two columns
col1, col2 = st.columns(2)
with col1:
# Display matched skills
st.subheader("π― Matched Skills")
if analysis_results['found_skills']:
for skill in analysis_results['found_skills']:
# Show skill with proficiency level
level = analysis_results['skill_levels'].get(skill, 'intermediate')
level_emoji = "π’" if level == 'advanced' else "π‘" if level == 'intermediate' else "π "
st.success(f"{level_emoji} {skill.title()} ({level.title()})")
# Calculate match percentage
match_percentage = len(analysis_results['found_skills']) / len(job_descriptions[job_title]["skills"]) * 100
st.metric("Skills Match", f"{match_percentage:.1f}%")
else:
st.warning("No direct skill matches found.")
with col2:
# Display semantic match score
st.subheader("π‘ Semantic Match")
st.metric("Overall Match Score", f"{analysis_results['match_score']:.1f}%")
# Display must-have skills match
must_have_skills = job_descriptions[job_title]["must_have"]
must_have_count = sum(1 for skill in must_have_skills if skill in analysis_results['found_skills'])
must_have_percentage = (must_have_count / len(must_have_skills)) * 100
st.write("Must-have skills:")
st.progress(must_have_percentage / 100)
st.write(f"{must_have_count} out of {len(must_have_skills)} ({must_have_percentage:.1f}%)")
# Professional level assessment
st.subheader("π§ Seniority Assessment")
st.info(f"**{analysis_results['seniority']}** ({analysis_results['years_experience']:.1f} years equivalent experience)")
st.write(job_descriptions[job_title]["seniority_levels"][analysis_results['seniority']])
with tab2:
# Display resume summary
st.subheader("π Resume Summary")
st.write(analysis_results['summary'])
# Display experience timeline
st.subheader("β³ Experience Timeline")
if analysis_results['experiences']:
# Convert experiences to dataframe for display
exp_data = []
for exp in analysis_results['experiences']:
if 'start_date' in exp and 'end_date' in exp:
exp_data.append({
'Company': exp['company'],
'Role': exp['role'],
'Start Date': exp['start_date'].strftime('%b %Y') if exp['start_date'] else 'Unknown',
'End Date': exp['end_date'].strftime('%b %Y') if exp['end_date'] != datetime.now() else 'Present',
'Duration (months)': exp.get('duration_months', 'Unknown')
})
else:
exp_data.append({
'Company': exp['company'],
'Role': exp['role'],
'Duration': exp.get('duration', 'Unknown')
})
if exp_data:
exp_df = pd.DataFrame(exp_data)
st.dataframe(exp_df)
# Create a timeline visualization if dates are available
timeline_data = [exp for exp in analysis_results['experiences'] if 'start_date' in exp and 'end_date' in exp]
if timeline_data and len(timeline_data) > 0:
try:
# Sort by start date
timeline_data = sorted(timeline_data, key=lambda x: x['start_date'])
# Create figure
fig = go.Figure()
for i, exp in enumerate(timeline_data):
fig.add_trace(go.Bar(
x=[(exp['end_date'] - exp['start_date']).days / 30], # Duration in months
y=[exp['company']],
orientation='h',
name=exp['role'],
hovertext=f"{exp['role']} at {exp['company']}",
marker=dict(color=px.colors.qualitative.Plotly[i % len(px.colors.qualitative.Plotly)])
))
fig.update_layout(
title="Career Timeline",
xaxis_title="Duration (months)",
yaxis_title="Company",
height=400,
margin=dict(l=0, r=0, b=0, t=30)
)
st.plotly_chart(fig, use_container_width=True)
except Exception as e:
st.warning(f"Could not create timeline visualization: {e}")
else:
st.warning("No work experience data could be extracted.")
with tab3:
# Display missing skills
st.subheader("π Skills to Develop")
# Create two columns
col1, col2 = st.columns(2)
with col1:
# Missing skills
if missing_skills:
for skill in missing_skills:
st.warning(f"β {skill.title()}")
else:
st.success("Great! The candidate has all the required skills!")
with col2:
# Skills gap analysis
st.subheader("π Gap Analysis")
# Show must-have skills that are missing
missing_must_have = [skill for skill in job_descriptions[job_title]["must_have"]
if skill not in analysis_results['found_skills']]
if missing_must_have:
st.error("**Critical Skills Missing:**")
for skill in missing_must_have:
st.write(f"- {skill.title()}")
st.markdown("These are must-have skills for this position.")
else:
st.success("Candidate has all the must-have skills for this position!")
# Show nice-to-have skills gap
missing_nice_to_have = [skill for skill in job_descriptions[job_title]["nice_to_have"]
if skill not in analysis_results['found_skills']]
if missing_nice_to_have:
st.warning("**Nice-to-Have Skills Missing:**")
for skill in missing_nice_to_have:
st.write(f"- {skill.title()}")
else:
st.success("Candidate has all the nice-to-have skills!")
# Display career trajectory
st.subheader("π¨βπΌ Career Trajectory")
st.info(analysis_results['career_prediction'])
with tab4:
# Display career advice
st.subheader("π Career Advice and Project Recommendations")
if st.button("Generate Career Advice"):
with st.spinner("Generating personalized career advice..."):
advice = generate_career_advice(text, job_title, analysis_results['found_skills'], missing_skills)
st.markdown(advice)
except Exception as e:
st.error(f"An error occurred while processing the resume: {str(e)}")
st.exception(e)
# Add footer
st.markdown("---")
st.markdown("Made with β€οΈ using Streamlit and Hugging Face") |