app.py

import streamlit as st
import pdfplumber
import io
import spacy
import re
import pandas as pd
import matplotlib.pyplot as plt
from transformers import pipeline
from sentence_transformers import SentenceTransformer, util
import subprocess
import sys
import torch
import nltk
from nltk.tokenize import word_tokenize
from datetime import datetime
import plotly.express as px
import plotly.graph_objects as go
import numpy as np
from collections import defaultdict

# Fix for huggingface_hub import issue
try:
    # For newer versions of huggingface_hub
    from huggingface_hub import hf_hub_download
except ImportError:
    try:
        # For older versions of huggingface_hub
        from huggingface_hub import cached_download as hf_hub_download
    except ImportError:
        st.error("Could not import required functions from huggingface_hub. Please check your installation.")
        hf_hub_download = None

# Initialize NLTK
@st.cache_resource
def download_nltk_resources():
    try:
        nltk.data.find('tokenizers/punkt')
    except LookupError:
        nltk.download('punkt')

download_nltk_resources()

st.set_page_config(
    page_title="Comprehensive Resume Screener & Skill Extractor",
    page_icon="📄",
    layout="wide"
)

# Download spaCy model if not already downloaded
@st.cache_resource
def download_spacy_model():
    try:
        nlp = spacy.load("en_core_web_sm")
    except OSError:
        subprocess.check_call([sys.executable, "-m", "spacy", "download", "en_core_web_sm"])
        nlp = spacy.load("en_core_web_sm")
    return nlp

# Load the NLP models
@st.cache_resource
def load_models():
    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
    nlp = download_spacy_model()
    
    # Load sentence transformer for semantic matching
    try:
        sentence_model = SentenceTransformer('all-MiniLM-L6-v2')
    except Exception as e:
        st.error(f"Failed to load sentence transformer: {str(e)}")
        sentence_model = None
    
    return summarizer, nlp, sentence_model

# Initialize models
summarizer, nlp, sentence_model = load_models()

# Job descriptions and required skills
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.",
        "semantic_description": """
        We're seeking a talented Software Engineer to design, develop, and maintain high-quality software solutions.
        The ideal candidate has strong programming skills in languages like Python, Java, or JavaScript, and experience with
        SQL databases. You should be proficient in algorithms, data structures, and version control systems like Git.
        Experience with cloud platforms and web development frameworks is a plus. You'll be responsible for the full
        software development lifecycle, from requirements gathering to deployment and maintenance.
        """
    },
    "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.",
        "semantic_description": """
        We're looking for a creative Interaction Designer to craft intuitive and engaging user experiences.
        You should have expertise in UI/UX design principles and methods, with a portfolio demonstrating your
        ability to conduct user research, create wireframes, and develop interactive prototypes. Proficiency
        with design tools like Figma, Sketch, and Adobe Creative Suite is required. You'll collaborate with
        product managers and developers to iterate on designs based on user feedback and business requirements.
        """
    },
    "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.",
        "semantic_description": """
        We're seeking a skilled Data Scientist to extract insights from complex datasets and build predictive models.
        The ideal candidate has strong programming skills in Python or R, expertise in statistical analysis, and
        experience with machine learning algorithms. You should be proficient in SQL for data extraction and tools
        like TensorFlow or PyTorch for deep learning. Experience with data manipulation libraries like Pandas and NumPy
        is essential. You'll work on projects from exploratory data analysis to model deployment, collaborating with
        stakeholders to solve business problems through data-driven approaches.
        """
    }
}

def extract_text_from_pdf(pdf_file):
    text = ""
    with pdfplumber.open(pdf_file) as pdf:
        for page in pdf.pages:
            text += page.extract_text() or ""
    return text

def extract_work_experience(text):
    """Extract work experience details including company names, job titles, and dates"""
    # Find common section headers for work experience
    work_exp_patterns = [
        r"(?i)WORK EXPERIENCE|PROFESSIONAL EXPERIENCE|EMPLOYMENT HISTORY|EXPERIENCE",
        r"(?i)EDUCATION|ACADEMIC|QUALIFICATIONS"
    ]
    
    # Find the start of work experience section
    work_exp_start = None
    for pattern in work_exp_patterns[:1]:  # Use only the work experience patterns
        match = re.search(pattern, text)
        if match:
            work_exp_start = match.end()
            break
    
    if work_exp_start is None:
        return []
    
    # Find the end of work experience section (start of education or next major section)
    work_exp_end = len(text)
    for pattern in work_exp_patterns[1:]:  # Use only the education pattern
        match = re.search(pattern, text)
        if match and match.start() > work_exp_start:
            work_exp_end = match.start()
            break
    
    work_exp_text = text[work_exp_start:work_exp_end]
    
    # Extract job entries
    # Look for patterns of job titles, company names, and dates
    job_entries = []
    
    # Pattern for dates (MM/YYYY or Month YYYY)
    date_pattern = r"(?i)(Jan(?:uary)?|Feb(?:ruary)?|Mar(?:ch)?|Apr(?:il)?|May|Jun(?:e)?|Jul(?:y)?|Aug(?:ust)?|Sep(?:tember)?|Oct(?:ober)?|Nov(?:ember)?|Dec(?:ember)?)[,\s]+\d{4}|\d{1,2}/\d{4}|\d{4}"
    
    # Pattern for common job title indicators
    job_title_pattern = r"(?i)(Senior|Lead|Principal|Junior|Associate)?\s*(Software Engineer|Developer|Designer|Analyst|Manager|Director|Consultant|Specialist|Coordinator|Administrator)"
    
    # Split into paragraphs which often represent job entries
    paragraphs = re.split(r'\n\s*\n', work_exp_text)
    
    for paragraph in paragraphs:
        # Skip short paragraphs that are likely not job entries
        if len(paragraph.strip()) < 30:
            continue
        
        # Extract dates
        dates = re.findall(date_pattern, paragraph)
        start_date = dates[0] if dates else "Unknown"
        end_date = dates[-1] if len(dates) > 1 else "Present"
        
        # Extract job title
        title_match = re.search(job_title_pattern, paragraph)
        job_title = title_match.group(0) if title_match else "Unknown Position"
        
        # Extract company name (typically near the job title or at the start of the paragraph)
        lines = paragraph.split('\n')
        company = lines[0].strip() if lines else "Unknown Company"
        if job_title in company:
            company = company.replace(job_title, "").strip()
        
        # Clean company name
        for date in dates:
            company = company.replace(date, "").strip()
        company = re.sub(r'[,\.\|\-]', ' ', company).strip()
        
        job_entries.append({
            "company": company,
            "title": job_title,
            "start_date": start_date,
            "end_date": end_date,
            "description": paragraph
        })
    
    return job_entries

def estimate_skill_proficiency(text, skill):
    """Estimate proficiency level for a skill"""
    # Define proficiency indicators
    basic_indicators = ["familiar with", "basic knowledge", "understanding of", "exposure to"]
    intermediate_indicators = ["experience with", "proficient in", "worked with", "2-3 years", "2 years", "3 years"]
    advanced_indicators = ["expert in", "advanced", "extensive experience", "lead", "architected", "designed", "5+ years", "4+ years"]
    
    # Convert to lowercase for matching
    text_lower = text.lower()
    
    # Find skill mentions and surrounding context
    skill_lower = skill.lower()
    skill_index = text_lower.find(skill_lower)
    
    if skill_index == -1:
        return None
    
    # Extract context (100 characters before and after the skill mention)
    start = max(0, skill_index - 100)
    end = min(len(text_lower), skill_index + len(skill_lower) + 100)
    context = text_lower[start:end]
    
    # Check for proficiency indicators
    for indicator in advanced_indicators:
        if indicator in context:
            return "Advanced"
    
    for indicator in intermediate_indicators:
        if indicator in context:
            return "Intermediate"
    
    for indicator in basic_indicators:
        if indicator in context:
            return "Basic"
    
    # Default to basic if skill is mentioned but no proficiency indicators are found
    return "Basic"

def calculate_seniority_score(job_entries):
    """Calculate a seniority score based on job titles and years of experience"""
    # Define seniority levels for common job titles
    seniority_levels = {
        "intern": 1,
        "junior": 2,
        "associate": 3,
        "developer": 4,
        "engineer": 4,
        "designer": 4,
        "analyst": 4,
        "senior": 6,
        "lead": 7,
        "manager": 7,
        "principal": 8,
        "director": 9,
        "vp": 10,
        "cto": 10,
        "cio": 10,
        "ceo": 10
    }
    
    # Calculate total years of experience
    total_years = 0
    
    for job in job_entries:
        # Parse start and end dates
        try:
            start_year = re.search(r'\d{4}', job["start_date"])
            end_year = re.search(r'\d{4}', job["end_date"]) if job["end_date"] != "Present" else None
            
            if start_year:
                start_year = int(start_year.group(0))
                end_year = int(end_year.group(0)) if end_year else datetime.now().year
                years = end_year - start_year
                if 0 <= years <= 30:  # Sanity check
                    total_years += years
        except Exception:
            # Skip if there's an issue with date parsing
            pass
    
    # Calculate title-based seniority
    highest_seniority = 0
    
    for job in job_entries:
        title_lower = job["title"].lower()
        for level_title, score in seniority_levels.items():
            if level_title in title_lower and score > highest_seniority:
                highest_seniority = score
    
    # Combine years of experience and title-based seniority
    # Years of experience factor: 0-2 years (1), 3-5 years (2), 6-10 years (3), 11+ years (4)
    years_factor = 1
    if total_years >= 3:
        years_factor = 2
    if total_years >= 6:
        years_factor = 3
    if total_years >= 11:
        years_factor = 4
    
    # Final seniority score (1-10 scale)
    seniority_score = min(10, max(1, (highest_seniority * 0.6) + (years_factor * 1.0)))
    
    return round(seniority_score, 1), total_years

def detect_fraud_signals(text, job_entries):
    """Detect potential fraud signals in the resume"""
    fraud_signals = []
    
    # Check for impossible timelines (overlapping full-time roles)
    if len(job_entries) >= 2:
        for i in range(len(job_entries) - 1):
            for j in range(i+1, len(job_entries)):
                # Check if both jobs have date information
                if (job_entries[i]["start_date"] != "Unknown" and 
                    job_entries[i]["end_date"] != "Unknown" and
                    job_entries[j]["start_date"] != "Unknown" and
                    job_entries[j]["end_date"] != "Unknown"):
                    
                    # Get years for comparison
                    i_start = re.search(r'\d{4}', job_entries[i]["start_date"])
                    i_end = re.search(r'\d{4}', job_entries[i]["end_date"]) if job_entries[i]["end_date"] != "Present" else None
                    j_start = re.search(r'\d{4}', job_entries[j]["start_date"])
                    j_end = re.search(r'\d{4}', job_entries[j]["end_date"]) if job_entries[j]["end_date"] != "Present" else None
                    
                    # Convert to integers for comparison
                    if i_start and j_start:
                        i_start = int(i_start.group(0))
                        i_end = int(i_end.group(0)) if i_end else datetime.now().year
                        j_start = int(j_start.group(0))
                        j_end = int(j_end.group(0)) if j_end else datetime.now().year
                        
                        # Check for significant overlap (more than 6 months)
                        if ((i_start <= j_start < i_end) or (j_start <= i_start < j_end)) and job_entries[i]["company"] != job_entries[j]["company"]:
                            overlap_years = min(i_end, j_end) - max(i_start, j_start)
                            if overlap_years > 0.5:  # More than 6 months overlap
                                fraud_signals.append(f"Potential timeline inconsistency: Overlapping roles at {job_entries[i]['company']} and {job_entries[j]['company']} for {overlap_years:.1f} years")
    
    # Check for suspicious keywords or phrases
    suspicious_phrases = [
        "self-employed", 
        "freelance",
        "consultant",
        "entrepreneur",
        "founder",
        "ceo of own company"
    ]
    
    # Look for suspicious gap filling
    for phrase in suspicious_phrases:
        if phrase in text.lower():
            # Not all of these are fraudulent, but they warrant verification
            fraud_signals.append(f"Verification recommended: Contains '{phrase}' which may need additional verification")
    
    # Check for unexplained gaps in employment history
    if len(job_entries) >= 2:
        for i in range(len(job_entries) - 1):
            # Sort entries by start date
            if "Unknown" not in job_entries[i]["end_date"] and "Unknown" not in job_entries[i+1]["start_date"]:
                end_match = re.search(r'\d{4}', job_entries[i]["end_date"])
                start_match = re.search(r'\d{4}', job_entries[i+1]["start_date"])
                
                if end_match and start_match:
                    end_year = int(end_match.group(0))
                    start_year = int(start_match.group(0))
                    
                    # If there's more than a 1-year gap
                    if start_year - end_year > 1:
                        fraud_signals.append(f"Employment gap: {end_year} to {start_year} ({start_year - end_year} years)")
    
    return fraud_signals

def predict_career_trajectory(job_entries, current_skills):
    """Predict logical next roles based on career progression"""
    # Career path mappings based on common progressions
    career_paths = {
        "software engineer": ["Senior Software Engineer", "Lead Developer", "Software Architect", "Engineering Manager", "CTO"],
        "developer": ["Senior Developer", "Technical Lead", "Software Architect", "Development Manager", "CTO"],
        "designer": ["Senior Designer", "Lead Designer", "Design Manager", "Creative Director", "VP of Design"],
        "data scientist": ["Senior Data Scientist", "Lead Data Scientist", "Data Science Manager", "Director of Analytics", "Chief Data Officer"]
    }
    
    # Extract current role from latest job entry
    current_role = job_entries[0]["title"].lower() if job_entries else "unknown"
    
    # Find the best matching career path
    best_match = None
    for role_key in career_paths:
        if role_key in current_role:
            best_match = role_key
            break
    
    if not best_match:
        return ["Career path prediction requires more information"]
    
    # Find current position in the career path
    current_index = 0
    for i, role in enumerate(career_paths[best_match]):
        if any(indicator in current_role for indicator in ["senior", "lead", "manager", "director", "vp", "chief"]):
            # If current role contains seniority indicators, advance the index
            if "senior" in current_role and "senior" in role.lower():
                current_index = i
                break
            elif "lead" in current_role and "lead" in role.lower():
                current_index = i
                break
            elif "manager" in current_role and "manager" in role.lower():
                current_index = i
                break
            elif "director" in current_role and "director" in role.lower():
                current_index = i
                break
    
    # Get next potential roles (up to 3)
    next_roles = []
    for i in range(current_index + 1, min(current_index + 4, len(career_paths[best_match]))):
        next_roles.append(career_paths[best_match][i])
    
    if not next_roles:
        next_roles = ["You're at a senior level in your career path. Consider lateral moves or industry specialization."]
    
    return next_roles

def analyze_resume(text, job_title, sentence_model):
    # Extract work experience
    job_entries = extract_work_experience(text)
    
    # Sort job entries by start date (most recent first)
    job_entries.sort(key=lambda x: "9999" if x["start_date"] == "Unknown" else x["start_date"], reverse=True)
    
    # Extract relevant skills with basic keyword matching
    doc = nlp(text.lower())
    found_skills = []
    required_skills = job_descriptions[job_title]["skills"]
    
    for skill in required_skills:
        if skill in text.lower():
            found_skills.append(skill)
    
    # Determine skill proficiency levels
    skill_proficiencies = {}
    for skill in found_skills:
        proficiency = estimate_skill_proficiency(text, skill)
        if proficiency:
            skill_proficiencies[skill] = proficiency
    
    # Calculate seniority score
    seniority_score, years_experience = calculate_seniority_score(job_entries)
    
    # Detect fraud signals
    fraud_signals = detect_fraud_signals(text, job_entries)
    
    # Predict career trajectory
    next_roles = predict_career_trajectory(job_entries, found_skills)
    
    # Generate summary
    chunks = [text[i:i + 1000] for i in range(0, len(text), 1000)]
    summaries = []
    for chunk in chunks[:3]:  # Process first 3000 characters to avoid token limits
        summary = summarizer(chunk, max_length=150, min_length=50, do_sample=False)[0]["summary_text"]
        summaries.append(summary)
    
    # Semantic matching with job description
    semantic_score = 0
    if sentence_model:
        try:
            resume_embedding = sentence_model.encode(text[:5000])  # Limit to first 5000 chars to avoid memory issues
            job_embedding = sentence_model.encode(job_descriptions[job_title]["semantic_description"])
            semantic_score = float(util.pytorch_cos_sim(resume_embedding, job_embedding)[0][0])
        except Exception as e:
            st.error(f"Error in semantic matching: {str(e)}")
    
    return {
        "found_skills": found_skills,
        "skill_proficiencies": skill_proficiencies,
        "summary": " ".join(summaries),
        "job_entries": job_entries,
        "seniority_score": seniority_score,
        "years_experience": years_experience,
        "fraud_signals": fraud_signals,
        "next_roles": next_roles,
        "semantic_score": semantic_score
    }

def generate_career_advice(resume_text, job_title, found_skills, missing_skills):
    """
    Generate career advice using a template-based approach instead of Qwen3-8B
    to avoid dependency issues
    """
    # Template-based advice generation
    advice = f"""## Career Development Plan for {job_title} Position

### Skills to Develop

The following skills would strengthen your resume for this position:

"""
    
    # Add advice for each missing skill
    for skill in missing_skills:
        if skill == "python":
            advice += f"""#### Python
- **How to develop**: Take online courses focused on Python for {job_title.lower()} applications
- **Project idea**: Build a data analysis tool or web application using Python and popular frameworks
- **Resources**: Coursera's Python for Everybody, Python.org tutorials, Real Python website

"""
        elif skill == "java":
            advice += f"""#### Java
- **How to develop**: Complete a comprehensive Java course with practical exercises
- **Project idea**: Develop a backend service with Spring Boot
- **Resources**: Oracle's Java tutorials, Udemy courses on Java, "Effective Java" by Joshua Bloch

"""
        elif skill == "javascript":
            advice += f"""#### JavaScript
- **How to develop**: Practice with modern JavaScript frameworks
- **Project idea**: Create an interactive web application with React or Vue.js
- **Resources**: MDN Web Docs, freeCodeCamp, "Eloquent JavaScript" by Marijn Haverbeke

"""
        elif skill == "sql":
            advice += f"""#### SQL
- **How to develop**: Practice with database design and complex queries
- **Project idea**: Design a database system for a small business with reports and analytics
- **Resources**: SQLZoo, Mode Analytics SQL tutorial, W3Schools SQL course

"""
        elif "algorithms" in skill or "data structures" in skill:
            advice += f"""#### Algorithms & Data Structures
- **How to develop**: Solve coding problems regularly on platforms like LeetCode
- **Project idea**: Implement classic algorithms and optimize them for specific use cases
- **Resources**: "Cracking the Coding Interview" book, AlgoExpert, Coursera Algorithms specialization

"""
        elif "git" in skill:
            advice += f"""#### Git & Version Control
- **How to develop**: Contribute to open source projects to practice Git workflows
- **Project idea**: Set up a personal project with proper branching strategies and CI/CD
- **Resources**: Git documentation, GitHub Learning Lab, Atlassian Git tutorials

"""
        elif "cloud" in skill:
            advice += f"""#### Cloud Technologies
- **How to develop**: Get hands-on experience with a major cloud provider (AWS, Azure, GCP)
- **Project idea**: Deploy an application to the cloud with proper infrastructure as code
- **Resources**: Cloud provider documentation, A Cloud Guru courses, free tier accounts

"""
        elif "ui" in skill or "ux" in skill:
            advice += f"""#### UI/UX Design
- **How to develop**: Study design principles and practice creating user interfaces
- **Project idea**: Redesign an existing website or app with focus on user experience
- **Resources**: Nielsen Norman Group articles, Interaction Design Foundation, Figma tutorials

"""
        elif "machine learning" in skill:
            advice += f"""#### Machine Learning
- **How to develop**: Take courses on ML fundamentals and practice with datasets
- **Project idea**: Build a predictive model to solve a real-world problem
- **Resources**: Andrew Ng's Coursera courses, Kaggle competitions, "Hands-On Machine Learning" book

"""
        elif "data analysis" in skill:
            advice += f"""#### Data Analysis
- **How to develop**: Practice analyzing datasets and creating visualizations
- **Project idea**: Perform an exploratory data analysis on a public dataset
- **Resources**: DataCamp courses, Kaggle datasets, "Python for Data Analysis" by Wes McKinney

"""
        else:
            advice += f"""#### {skill.title()}
- **How to develop**: Research industry best practices and take relevant courses
- **Project idea**: Create a portfolio piece that showcases this skill
- **Resources**: Online courses, industry blogs, and practice projects

"""
    
    # Add project recommendations based on job title
    advice += f"""
### Recommended Projects for {job_title}

Based on the target position and the skills needed, here are some project ideas:

"""
    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. **Mobile Application**: Develop a cross-platform mobile app using React Native or Flutter
4. **Automation Tools**: Build scripts or applications that automate repetitive tasks
5. **Contribution to Open Source**: Find a project aligned with your skills and contribute meaningfully

"""
    elif job_title == "Interaction Designer":
        advice += """
1. **Design System**: Create a comprehensive design system with components and usage guidelines
2. **Website Redesign**: Redesign an existing website with focus on improved UX
3. **Mobile App Prototype**: Design a fully interactive mobile app prototype
4. **User Research Project**: Conduct user research and create a report with insights and recommendations
5. **Design Case Study**: Document your design process for solving a specific problem

"""
    elif job_title == "Data Scientist":
        advice += """
1. **Predictive Model**: Build a machine learning model that solves a real-world problem
2. **Data Visualization Dashboard**: Create an interactive dashboard to visualize complex data
3. **Natural Language Processing**: Develop a text analysis or sentiment analysis project
4. **Time Series Analysis**: Analyze time-based data and build forecasting models
5. **A/B Testing Framework**: Design and implement a framework for testing hypotheses

"""
    
    # General advice for all positions
    advice += """
### Learning Resources

- **Online Platforms**: Coursera, Udemy, Pluralsight, LinkedIn Learning
- **Documentation**: Official language and framework documentation
- **Communities**: Stack Overflow, GitHub, Reddit programming communities
- **Books**: O'Reilly publications specific to your target technologies
- **YouTube Channels**: Traversy Media, Tech With Tim, freeCodeCamp

### Positioning Your Experience

- Highlight transferable skills from your current experience
- Quantify achievements with metrics where possible
- Frame previous work in terms relevant to the target position
- Create a tailored resume that emphasizes relevant projects and responsibilities
"""
    
    return advice

# Streamlit UI
st.title("📄 Comprehensive Resume Analyzer")

# Add description
st.markdown("""
This app helps recruiters and job seekers analyze resumes with advanced features:

- **Semantic Job Matching**: Uses AI to match resumes to job descriptions beyond keywords
- **Skill Proficiency Detection**: Identifies skill levels from context
- **Career Progression Analysis**: Visualizes job history and seniority
- **Fraud Detection**: Flags potential inconsistencies for verification
- **Career Path Prediction**: Suggests logical next roles based on experience
- **Personalized Development Advice**: Recommends skills, projects, and resources
""")

# 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"**Job Description:**\n{job_descriptions[job_title]['description']}\n\n**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 with advanced AI..."):
            # Extract text from PDF
            text = extract_text_from_pdf(uploaded_file)
            
            # Analyze resume
            analysis_results = analyze_resume(text, job_title, sentence_model)
            
            # 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, tab5, tab6 = st.tabs([
            "📊 Match Score", 
            "🎯 Skills Analysis", 
            "👨‍💼 Experience", 
            "📈 Career Path", 
            "🚩 Verification", 
            "🚀 Career Advice"
        ])
        
        with tab1:
            # Display match scores
            st.subheader("📊 Job Match Analysis")
            
            # Calculate match scores
            keyword_match = len(analysis_results["found_skills"]) / len(job_descriptions[job_title]["skills"]) * 100
            semantic_match = analysis_results["semantic_score"] * 100
            
            # Display scores with gauges
            col1, col2 = st.columns(2)
            
            with col1:
                # Keyword match gauge
                fig = go.Figure(go.Indicator(
                    mode = "gauge+number",
                    value = keyword_match,
                    title = {'text': "Keyword Match"},
                    gauge = {
                        'axis': {'range': [0, 100]},
                        'bar': {'color': "darkblue"},
                        'steps': [
                            {'range': [0, 30], 'color': "lightgray"},
                            {'range': [30, 70], 'color': "gray"},
                            {'range': [70, 100], 'color': "lightblue"}
                        ],
                        'threshold': {
                            'line': {'color': "red", 'width': 4},
                            'thickness': 0.75,
                            'value': 70
                        }
                    }
                ))
                st.plotly_chart(fig, use_container_width=True)
            
            with col2:
                # Semantic match gauge
                fig = go.Figure(go.Indicator(
                    mode = "gauge+number",
                    value = semantic_match,
                    title = {'text': "Semantic Match"},
                    gauge = {
                        'axis': {'range': [0, 100]},
                        'bar': {'color': "darkgreen"},
                        'steps': [
                            {'range': [0, 30], 'color': "lightgray"},
                            {'range': [30, 70], 'color': "gray"},
                            {'range': [70, 100], 'color': "lightgreen"}
                        ],
                        'threshold': {
                            'line': {'color': "red", 'width': 4},
                            'thickness': 0.75,
                            'value': 70
                        }
                    }
                ))
                st.plotly_chart(fig, use_container_width=True)
            
            # Calculate overall match score (weighted average)
            overall_match = (keyword_match * 0.4) + (semantic_match * 0.6)
            
            # Create overall score gauge
            fig = go.Figure(go.Indicator(
                mode = "gauge+number+delta",
                value = overall_match,
                title = {'text': "Overall Match Score"},
                delta = {'reference': 75, 'increasing': {'color': "green"}},
                gauge = {
                    'axis': {'range': [0, 100]},
                    'bar': {'color': "darkblue"},
                    'steps': [
                        {'range': [0, 50], 'color': "lightgray"},
                        {'range': [50, 75], 'color': "gray"},
                        {'range': [75, 100], 'color': "darkblue"}
                    ],
                    'threshold': {
                        'line': {'color': "red", 'width': 4},
                        'thickness': 0.75,
                        'value': 75
                    }
                }
            ))
            
            st.plotly_chart(fig, use_container_width=True)
            
            # Display resume summary
            st.subheader("📝 Resume Summary")
            st.write(analysis_results["summary"])
        
        with tab2:
            # Display skills analysis
            st.subheader("🎯 Skills Analysis")
            
            # Create two columns
            col1, col2 = st.columns(2)
            
            with col1:
                # Display matched skills with proficiency levels
                st.subheader("🟢 Skills Present")
                
                # Create a DataFrame for the skills table
                skills_data = []
                for skill in analysis_results["found_skills"]:
                    proficiency = analysis_results["skill_proficiencies"].get(skill, "Basic")
                    skills_data.append({
                        "Skill": skill.title(),
                        "Proficiency": proficiency
                    })
                
                if skills_data:
                    skills_df = pd.DataFrame(skills_data)
                    
                    # Add proficiency color coding
                    def color_proficiency(val):
                        if val == "Advanced":
                            return 'background-color: #d4f7d4'
                        elif val == "Intermediate":
                            return 'background-color: #fff2cc'
                        else:
                            return 'background-color: #f2f2f2'
                    
                    st.dataframe(skills_df.style.applymap(color_proficiency, subset=['Proficiency']), 
                                 use_container_width=True)
                else:
                    st.warning("No direct skill matches found.")
            
            with col2:
                # Display missing skills
                st.subheader("🔴 Skills to Develop")
                if missing_skills:
                    missing_df = pd.DataFrame({"Skill": [skill.title() for skill in missing_skills]})
                    st.dataframe(missing_df, use_container_width=True)
                else:
                    st.success("Great! The candidate has all the required skills!")
            
            # Create a radar chart for skills coverage
            st.subheader("Skills Coverage")
            
            # Prepare data for radar chart
            categories = job_descriptions[job_title]["skills"]
            values = [1 if skill in analysis_results["found_skills"] else 0 for skill in categories]
            
            # Create radar chart
            fig = go.Figure()
            
            fig.add_trace(go.Scatterpolar(
                r=values,
                theta=categories,
                fill='toself',
                name='Present Skills'
            ))
            
            fig.add_trace(go.Scatterpolar(
                r=[1] * len(categories),
                theta=categories,
                fill='toself',
                name='Required Skills',
                opacity=0.3
            ))
            
            fig.update_layout(
                polar=dict(
                    radialaxis=dict(
                        visible=True,
                        range=[0, 1]
                    )),
                showlegend=True
            )
            
            st.plotly_chart(fig, use_container_width=True)
        
        with tab3:
            # Display experience analysis
            st.subheader("👨‍💼 Experience Analysis")
            
            # Display seniority metrics
            col1, col2 = st.columns(2)
            
            with col1:
                # Seniority score gauge
                fig = go.Figure(go.Indicator(
                    mode="gauge+number",
                    value=analysis_results["seniority_score"],
                    title={'text': "Seniority Score"},
                    gauge={
                        'axis': {'range': [0, 10]},
                        'bar': {'color': "darkblue"},
                        'steps': [
                            {'range': [0, 3], 'color': "lightgray"},
                            {'range': [3, 7], 'color': "gray"},
                            {'range': [7, 10], 'color': "lightblue"}
                        ],
                        'threshold': {
                            'line': {'color': "red", 'width': 4},
                            'thickness': 0.75,
                            'value': 7
                        }
                    }
                ))
                st.plotly_chart(fig, use_container_width=True)
            
            with col2:
                # Years of experience
                fig = go.Figure(go.Indicator(
                    mode="number+delta",
                    value=analysis_results["years_experience"],
                    number={'suffix': " years"},
                    title={"text": "Years of Experience"},
                    delta={'reference': 5, 'relative': False}
                ))
                st.plotly_chart(fig, use_container_width=True)
            
            # Display career progression timeline
            st.subheader("Career Progression Timeline")
            
            if analysis_results["job_entries"]:
                # Create timeline data
                timeline_data = []
                
                for job in analysis_results["job_entries"]:
                    # Extract years for visualization
                    start_year = re.search(r'\d{4}', job["start_date"])
                    end_year = re.search(r'\d{4}', job["end_date"]) if job["end_date"] != "Present" else None
                    
                    if start_year:
                        start_year = int(start_year.group(0))
                        end_year = int(end_year.group(0)) if end_year else datetime.now().year
                        
                        timeline_data.append({
                            "Role": job["title"],
                            "Company": job["company"],
                            "Start": start_year,
                            "End": end_year,
                            "Duration": end_year - start_year
                        })
                
                if timeline_data:
                    # Create DataFrame for timeline
                    timeline_df = pd.DataFrame(timeline_data)
                    
                    # Sort by start date (ascending)
                    timeline_df = timeline_df.sort_values(by="Start")
                    
                    # Create Gantt chart
                    fig = px.timeline(
                        timeline_df, 
                        x_start="Start", 
                        x_end="End", 
                        y="Company",
                        color="Role",
                        hover_data=["Duration"],
                        labels={"Company": "Employer"}
                    )
                    
                    fig.update_layout(
                        xaxis_title="Year",
                        yaxis_title="Employer",
                        title="Career Progression"
                    )
                    
                    st.plotly_chart(fig, use_container_width=True)
                else:
                    st.warning("Couldn't extract timeline data from the resume.")
            else:
                st.warning("No work experience entries found in the resume.")
        
        with tab4:
            # Display career path analysis
            st.subheader("📈 Career Path Analysis")
            
            # Display next role suggestions
            st.subheader("Suggested Next Roles")
            
            for i, role in enumerate(analysis_results["next_roles"]):
                st.info(f"**Option {i+1}:** {role}")
            
            # Add simple career progression visualization
            st.subheader("Career Progression Path")
            
            # Extract current role from latest job entry
            current_role = analysis_results["job_entries"][0]["title"] if analysis_results["job_entries"] else "Current Position"
            
            # Create nodes for career path
            career_nodes = [current_role] + analysis_results["next_roles"]
            
            # Create a simple digraph visualization
            career_df = pd.DataFrame({
                "From": [career_nodes[i] for i in range(len(career_nodes)-1)],
                "To": [career_nodes[i+1] for i in range(len(career_nodes)-1)],
                "Value": [10 for _ in range(len(career_nodes)-1)]
            })
            
            # Create a Sankey diagram
            fig = go.Figure(data=[go.Sankey(
                node=dict(
                    pad=15,
                    thickness=20,
                    line=dict(color="black", width=0.5),
                    label=career_nodes,
                    color="blue"
                ),
                link=dict(
                    source=[i for i in range(len(career_nodes)-1)],
                    target=[i+1 for i in range(len(career_nodes)-1)],
                    value=[1 for _ in range(len(career_nodes)-1)]
                )
            )])
            
            fig.update_layout(title_text="Potential Career Path", font_size=12)
            st.plotly_chart(fig, use_container_width=True)
        
        with tab5:
            # Display fraud detection analysis
            st.subheader("🚩 Verification Points")
            
            if analysis_results["fraud_signals"]:
                st.warning("The following points may require verification:")
                for signal in analysis_results["fraud_signals"]:
                    st.markdown(f"- {signal}")
            else:
                st.success("No significant inconsistencies detected in the resume.")
            
            # Add common verification tips
            st.subheader("Recommended Verification Steps")
            st.markdown("""
            Even when no inconsistencies are detected, consider these verification steps:
            
            1. **Reference Checks**: Contact previous employers to confirm employment dates and responsibilities
            2. **Skills Assessment**: Use technical interviews or tests to verify claimed skills
            3. **Education Verification**: Confirm degrees and certifications with educational institutions
            4. **Portfolio Review**: Examine work samples or project contributions
            5. **Online Presence**: Check LinkedIn, GitHub, or other professional profiles for consistency
            """)
        
        with tab6:
            # Display career advice
            st.subheader("🚀 Career Advice and Development Plan")
            
            if st.button("Generate Personalized Career Advice"):
                with st.spinner("Generating detailed career advice and development plan..."):
                    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 ❤️ using Streamlit, Hugging Face, and Advanced AI")