Implement SkillSync AI-Powered Resume & Job Match Hub backend

- Complete FastAPI backend with SQLite database
- AI-powered resume parsing and job matching using OpenAI
- JWT authentication with role-based access control
- Resume upload, job management, and matching endpoints
- Recruiter dashboard with candidate ranking
- Analytics and skill gap analysis features
- Comprehensive API documentation with OpenAPI
- Alembic database migrations
- File upload support for PDF, DOCX, and TXT resumes
- CORS enabled for frontend integration

🤖 Generated with BackendIM

Co-Authored-By: Claude <noreply@anthropic.com>

README.md

@@ -1,3 +1,212 @@
-# FastAPI Application
+# SkillSync - AI Resume & Job Match Hub
 
-This is a FastAPI application bootstrapped by BackendIM, the AI-powered backend generation platform.
+SkillSync is an AI-powered backend API that helps job seekers match their resumes with job descriptions, provides skill gap analysis, resume improvement suggestions, and generates personalized cover letters. It also includes a recruiter dashboard for candidate ranking and management.
+
+## Features
+
+### Core Features
+- **Resume Upload & Parsing**: Upload PDF, DOCX, or TXT resume files with AI-powered text extraction
+- **Job Description Analysis**: AI analysis of job postings to extract required skills and requirements
+- **AI Matching & Scoring**: Intelligent matching between resumes and jobs with detailed scoring
+- **Skill Gap Analysis**: Identify missing skills and get suggestions for improvement
+- **Resume Suggestions**: AI-powered recommendations to improve resume content
+- **Cover Letter Generation**: Personalized cover letter creation based on resume and job match
+- **Recruiter Dashboard**: Advanced candidate ranking and job management for recruiters
+- **Analytics & Tracking**: User improvement tracking and engagement analytics
+
+### Technical Features
+- **FastAPI Framework**: Modern, fast web framework for building APIs
+- **SQLite Database**: Lightweight, serverless database with SQLAlchemy ORM
+- **JWT Authentication**: Secure user authentication with role-based access control
+- **OpenAI Integration**: Powered by GPT models for intelligent text analysis
+- **File Upload Support**: Secure file handling with validation
+- **CORS Enabled**: Cross-origin resource sharing for frontend integration
+- **Comprehensive API Documentation**: Auto-generated OpenAPI/Swagger docs
+
+## Tech Stack
+
+- **Backend**: Python 3.9+ with FastAPI
+- **Database**: SQLite with SQLAlchemy ORM
+- **AI/ML**: OpenAI GPT-3.5/4 for text analysis and generation
+- **Authentication**: JWT tokens with bcrypt password hashing
+- **File Processing**: PyPDF2, python-docx for document parsing
+- **API Documentation**: Automatic OpenAPI/Swagger documentation
+
+## Installation & Setup
+
+### Prerequisites
+- Python 3.9 or higher
+- pip package manager
+
+### Local Development Setup
+
+1. **Clone the repository**
+   ```bash
+   git clone <repository-url>
+   cd airesumejobmatchingapi-5g56kf
+   ```
+
+2. **Install dependencies**
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. **Set up environment variables**
+   Create a `.env` file in the root directory:
+   ```env
+   SECRET_KEY=your-super-secret-key-change-in-production
+   OPENAI_API_KEY=your-openai-api-key
+   ```
+
+4. **Run database migrations**
+   ```bash
+   alembic upgrade head
+   ```
+
+5. **Start the development server**
+   ```bash
+   uvicorn main:app --reload --host 0.0.0.0 --port 8000
+   ```
+
+### Production Deployment
+
+For production deployment, ensure you set the following environment variables:
+
+- `SECRET_KEY`: A strong secret key for JWT token signing
+- `OPENAI_API_KEY`: Your OpenAI API key for AI functionality
+
+## API Documentation
+
+Once the server is running, you can access:
+
+- **Interactive API Documentation**: http://localhost:8000/docs
+- **Alternative API Documentation**: http://localhost:8000/redoc
+- **OpenAPI Schema**: http://localhost:8000/openapi.json
+- **Health Check**: http://localhost:8000/health
+
+## API Endpoints
+
+### Authentication
+- `POST /api/v1/auth/register` - User registration
+- `POST /api/v1/auth/login` - User login
+
+### Resume Management
+- `POST /api/v1/resumes/upload` - Upload and parse resume
+- `GET /api/v1/resumes/` - Get user's resumes
+- `GET /api/v1/resumes/{resume_id}` - Get specific resume
+- `PUT /api/v1/resumes/{resume_id}` - Update resume
+- `DELETE /api/v1/resumes/{resume_id}` - Delete resume
+
+### Job Management
+- `POST /api/v1/jobs/` - Create job posting (recruiters only)
+- `GET /api/v1/jobs/` - Get all active jobs with filters
+- `GET /api/v1/jobs/my-jobs` - Get recruiter's jobs
+- `GET /api/v1/jobs/{job_id}` - Get specific job
+- `PUT /api/v1/jobs/{job_id}` - Update job posting
+- `DELETE /api/v1/jobs/{job_id}` - Delete job posting
+
+### AI Matching
+- `POST /api/v1/matching/analyze` - Analyze resume-job match
+- `GET /api/v1/matching/` - Get user's matches
+- `GET /api/v1/matching/{match_id}` - Get specific match
+- `POST /api/v1/matching/{match_id}/cover-letter` - Generate cover letter
+
+### Recruiter Dashboard
+- `GET /api/v1/dashboard/candidates/{job_id}` - Get ranked candidates for job
+- `GET /api/v1/dashboard/jobs/stats` - Get job statistics
+- `GET /api/v1/dashboard/overview` - Get dashboard overview
+
+### Analytics
+- `GET /api/v1/analytics/user-stats` - Get user analytics
+- `GET /api/v1/analytics/skill-gaps` - Get skill gap analysis
+- `GET /api/v1/analytics/improvement-suggestions` - Get improvement suggestions
+- `POST /api/v1/analytics/track-event` - Track custom events
+
+## User Roles
+
+### Applicant (Default)
+- Upload and manage resumes
+- Search and view job postings
+- Get AI-powered job matching and scoring
+- Receive skill gap analysis and resume suggestions
+- Generate personalized cover letters
+- Track improvement analytics
+
+### Recruiter
+- All applicant features
+- Create and manage job postings
+- Access recruiter dashboard
+- View ranked candidates for jobs
+- Get job performance statistics
+
+### Admin
+- All recruiter and applicant features
+- System administration capabilities
+
+## Database Schema
+
+The application uses SQLite with the following main tables:
+- `users` - User accounts and authentication
+- `resumes` - Resume data and metadata
+- `jobs` - Job postings and requirements
+- `matches` - Resume-job matches with AI scoring
+- `skill_gaps` - Identified skill gaps from matches
+- `analytics` - User activity and improvement tracking
+
+## Environment Variables
+
+### Required Variables
+- `OPENAI_API_KEY`: Your OpenAI API key for AI functionality
+
+### Optional Variables (with defaults)
+- `SECRET_KEY`: JWT secret key (default: change in production)
+- `ACCESS_TOKEN_EXPIRE_MINUTES`: Token expiration time (default: 30)
+- `MAX_FILE_SIZE`: Maximum upload file size in bytes (default: 10MB)
+
+## File Storage
+
+The application stores uploaded files in `/app/storage/` with the following structure:
+- `/app/storage/db/` - SQLite database file
+- `/app/storage/uploads/` - Uploaded resume files
+
+## Development
+
+### Code Style
+The project uses Ruff for linting and code formatting:
+```bash
+ruff check .
+ruff format .
+```
+
+### Testing
+Run tests with pytest:
+```bash
+pytest
+```
+
+### Database Migrations
+Create new migrations:
+```bash
+alembic revision --autogenerate -m "Description"
+```
+
+Apply migrations:
+```bash
+alembic upgrade head
+```
+
+## Contributing
+
+1. Fork the repository
+2. Create a feature branch
+3. Make your changes
+4. Run tests and linting
+5. Submit a pull request
+
+## License
+
+This project is licensed under the MIT License.
+
+## Support
+
+For support and questions, please create an issue in the repository.

alembic.ini

@@ -0,0 +1,108 @@
+# A generic, single database configuration.
+
+[alembic]
+# path to migration scripts
+script_location = alembic
+
+# template used to generate migration file names; The default value is %%(rev)s_%%(slug)s
+# Uncomment the line below if you want the files to be prepended with date and time
+# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s
+
+# sys.path path, will be prepended to sys.path if present.
+# defaults to the current working directory.
+prepend_sys_path = .
+
+# timezone to use when rendering the date within the migration file
+# as well as the filename.
+# If specified, requires the python-dateutil library that can be
+# installed by adding `alembic[tz]` to the pip requirements
+# string value is passed to dateutil.tz.gettz()
+# leave blank for localtime
+# timezone =
+
+# max length of characters to apply to the
+# "slug" field
+# truncate_slug_length = 40
+
+# set to 'true' to run the environment during
+# the 'revision' command, regardless of autogenerate
+# revision_environment = false
+
+# set to 'true' to allow .pyc and .pyo files without
+# a source .py file to be detected as revisions in the
+# versions/ directory
+# sourceless = false
+
+# version path separator; As mentioned above, this is the character used to split
+# version_locations. The default within new alembic.ini files is "os", which uses
+# os.pathsep. If this key is omitted entirely, it falls back to the legacy
+# behavior of splitting on spaces and/or commas.
+# Valid values for version_path_separator are:
+#
+# version_path_separator = :
+# version_path_separator = ;
+# version_path_separator = space
+version_path_separator = os
+
+# set to 'true' to search source files recursively
+# in each "version_locations" directory
+# new in Alembic version 1.10
+# recursive_version_locations = false
+
+# the output encoding used when revision files
+# are written from script.py.mako
+# output_encoding = utf-8
+
+sqlalchemy.url = sqlite:////app/storage/db/db.sqlite
+
+[post_write_hooks]
+# post_write_hooks defines scripts or Python functions that are run
+# on newly generated revision scripts.  See the documentation for further
+# detail and examples
+
+# format using "black" - use the console_scripts runner, against the "black" entrypoint
+# hooks = black
+# black.type = console_scripts
+# black.entrypoint = black
+# black.options = -l 79 REVISION_SCRIPT_FILENAME
+
+# lint with attempts to fix using "ruff" - use the exec runner, execute a binary
+# hooks = ruff
+# ruff.type = exec
+# ruff.executable = %(here)s/.venv/bin/ruff
+# ruff.options = --fix REVISION_SCRIPT_FILENAME
+
+# Logging configuration
+[loggers]
+keys = root,sqlalchemy,alembic
+
+[handlers]
+keys = console
+
+[formatters]
+keys = generic
+
+[logger_root]
+level = WARN
+handlers = console
+qualname =
+
+[logger_sqlalchemy]
+level = WARN
+handlers =
+qualname = sqlalchemy.engine
+
+[logger_alembic]
+level = INFO
+handlers =
+qualname = alembic
+
+[handler_console]
+class = StreamHandler
+args = (sys.stderr,)
+level = NOTSET
+formatter = generic
+
+[formatter_generic]
+format = %(levelname)-5.5s [%(name)s] %(message)s
+datefmt = %H:%M:%S

alembic/env.py

@@ -0,0 +1,58 @@
+from logging.config import fileConfig
+from sqlalchemy import engine_from_config
+from sqlalchemy import pool
+from alembic import context
+import os
+import sys
+
+# Add the project root to the Python path
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# Import your models here
+from app.db.base import Base
+
+# this is the Alembic Config object
+config = context.config
+
+# Interpret the config file for Python logging.
+if config.config_file_name is not None:
+    fileConfig(config.config_file_name)
+
+# add your model's MetaData object here
+target_metadata = Base.metadata
+
+def run_migrations_offline() -> None:
+    """Run migrations in 'offline' mode."""
+    url = config.get_main_option("sqlalchemy.url")
+    context.configure(
+        url=url,
+        target_metadata=target_metadata,
+        literal_binds=True,
+        dialect_opts={"paramstyle": "named"},
+    )
+
+    with context.begin_transaction():
+        context.run_migrations()
+
+
+def run_migrations_online() -> None:
+    """Run migrations in 'online' mode."""
+    connectable = engine_from_config(
+        config.get_section(config.config_ini_section, {}),
+        prefix="sqlalchemy.",
+        poolclass=pool.NullPool,
+    )
+
+    with connectable.connect() as connection:
+        context.configure(
+            connection=connection, target_metadata=target_metadata
+        )
+
+        with context.begin_transaction():
+            context.run_migrations()
+
+
+if context.is_offline_mode():
+    run_migrations_offline()
+else:
+    run_migrations_online()

alembic/script.py.mako

@@ -0,0 +1,24 @@
+"""${message}
+
+Revision ID: ${up_revision}
+Revises: ${down_revision | comma,n}
+Create Date: ${create_date}
+
+"""
+from alembic import op
+import sqlalchemy as sa
+${imports if imports else ""}
+
+# revision identifiers, used by Alembic.
+revision = ${repr(up_revision)}
+down_revision = ${repr(down_revision)}
+branch_labels = ${repr(branch_labels)}
+depends_on = ${repr(depends_on)}
+
+
+def upgrade() -> None:
+    ${upgrades if upgrades else "pass"}
+
+
+def downgrade() -> None:
+    ${downgrades if downgrades else "pass"}

alembic/versions/001_initial_migration.py

@@ -0,0 +1,140 @@
+"""Initial migration
+
+Revision ID: 001
+Revises: 
+Create Date: 2024-01-01 12:00:00.000000
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = '001'
+down_revision = None
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Create users table
+    op.create_table('users',
+        sa.Column('id', sa.Integer(), nullable=False),
+        sa.Column('email', sa.String(), nullable=False),
+        sa.Column('hashed_password', sa.String(), nullable=False),
+        sa.Column('full_name', sa.String(), nullable=False),
+        sa.Column('role', sa.Enum('APPLICANT', 'RECRUITER', 'ADMIN', name='userrole'), nullable=True),
+        sa.Column('is_active', sa.Boolean(), nullable=True),
+        sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=True),
+        sa.Column('updated_at', sa.DateTime(timezone=True), nullable=True),
+        sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_users_email'), 'users', ['email'], unique=True)
+    op.create_index(op.f('ix_users_id'), 'users', ['id'], unique=False)
+
+    # Create jobs table
+    op.create_table('jobs',
+        sa.Column('id', sa.Integer(), nullable=False),
+        sa.Column('recruiter_id', sa.Integer(), nullable=False),
+        sa.Column('title', sa.String(), nullable=False),
+        sa.Column('company', sa.String(), nullable=False),
+        sa.Column('description', sa.Text(), nullable=False),
+        sa.Column('requirements', sa.Text(), nullable=True),
+        sa.Column('location', sa.String(), nullable=True),
+        sa.Column('job_type', sa.String(), nullable=True),
+        sa.Column('salary_range', sa.String(), nullable=True),
+        sa.Column('required_skills', sa.JSON(), nullable=True),
+        sa.Column('preferred_skills', sa.JSON(), nullable=True),
+        sa.Column('experience_level', sa.String(), nullable=True),
+        sa.Column('education_requirement', sa.String(), nullable=True),
+        sa.Column('is_active', sa.Boolean(), nullable=True),
+        sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=True),
+        sa.Column('updated_at', sa.DateTime(timezone=True), nullable=True),
+        sa.ForeignKeyConstraint(['recruiter_id'], ['users.id'], ),
+        sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_jobs_id'), 'jobs', ['id'], unique=False)
+
+    # Create resumes table
+    op.create_table('resumes',
+        sa.Column('id', sa.Integer(), nullable=False),
+        sa.Column('user_id', sa.Integer(), nullable=False),
+        sa.Column('title', sa.String(), nullable=False),
+        sa.Column('file_path', sa.String(), nullable=True),
+        sa.Column('original_filename', sa.String(), nullable=True),
+        sa.Column('extracted_text', sa.Text(), nullable=True),
+        sa.Column('parsed_data', sa.JSON(), nullable=True),
+        sa.Column('skills', sa.JSON(), nullable=True),
+        sa.Column('experience_years', sa.Integer(), nullable=True),
+        sa.Column('education_level', sa.String(), nullable=True),
+        sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=True),
+        sa.Column('updated_at', sa.DateTime(timezone=True), nullable=True),
+        sa.ForeignKeyConstraint(['user_id'], ['users.id'], ),
+        sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_resumes_id'), 'resumes', ['id'], unique=False)
+
+    # Create analytics table
+    op.create_table('analytics',
+        sa.Column('id', sa.Integer(), nullable=False),
+        sa.Column('user_id', sa.Integer(), nullable=False),
+        sa.Column('event_type', sa.String(), nullable=False),
+        sa.Column('event_data', sa.JSON(), nullable=True),
+        sa.Column('improvement_score', sa.Float(), nullable=True),
+        sa.Column('session_id', sa.String(), nullable=True),
+        sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=True),
+        sa.ForeignKeyConstraint(['user_id'], ['users.id'], ),
+        sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_analytics_id'), 'analytics', ['id'], unique=False)
+
+    # Create matches table
+    op.create_table('matches',
+        sa.Column('id', sa.Integer(), nullable=False),
+        sa.Column('user_id', sa.Integer(), nullable=False),
+        sa.Column('resume_id', sa.Integer(), nullable=False),
+        sa.Column('job_id', sa.Integer(), nullable=False),
+        sa.Column('match_score', sa.Float(), nullable=False),
+        sa.Column('skill_match_score', sa.Float(), nullable=True),
+        sa.Column('experience_match_score', sa.Float(), nullable=True),
+        sa.Column('education_match_score', sa.Float(), nullable=True),
+        sa.Column('overall_feedback', sa.Text(), nullable=True),
+        sa.Column('resume_suggestions', sa.JSON(), nullable=True),
+        sa.Column('cover_letter', sa.Text(), nullable=True),
+        sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=True),
+        sa.Column('updated_at', sa.DateTime(timezone=True), nullable=True),
+        sa.ForeignKeyConstraint(['job_id'], ['jobs.id'], ),
+        sa.ForeignKeyConstraint(['resume_id'], ['resumes.id'], ),
+        sa.ForeignKeyConstraint(['user_id'], ['users.id'], ),
+        sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_matches_id'), 'matches', ['id'], unique=False)
+
+    # Create skill_gaps table
+    op.create_table('skill_gaps',
+        sa.Column('id', sa.Integer(), nullable=False),
+        sa.Column('match_id', sa.Integer(), nullable=False),
+        sa.Column('missing_skill', sa.String(), nullable=False),
+        sa.Column('importance', sa.String(), nullable=True),
+        sa.Column('suggestion', sa.Text(), nullable=True),
+        sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=True),
+        sa.ForeignKeyConstraint(['match_id'], ['matches.id'], ),
+        sa.PrimaryKeyConstraint('id')
+    )
+    op.create_index(op.f('ix_skill_gaps_id'), 'skill_gaps', ['id'], unique=False)
+
+
+def downgrade() -> None:
+    op.drop_index(op.f('ix_skill_gaps_id'), table_name='skill_gaps')
+    op.drop_table('skill_gaps')
+    op.drop_index(op.f('ix_matches_id'), table_name='matches')
+    op.drop_table('matches')
+    op.drop_index(op.f('ix_analytics_id'), table_name='analytics')
+    op.drop_table('analytics')
+    op.drop_index(op.f('ix_resumes_id'), table_name='resumes')
+    op.drop_table('resumes')
+    op.drop_index(op.f('ix_jobs_id'), table_name='jobs')
+    op.drop_table('jobs')
+    op.drop_index(op.f('ix_users_id'), table_name='users')
+    op.drop_index(op.f('ix_users_email'), table_name='users')
+    op.drop_table('users')

app/api/v1/analytics.py

@@ -0,0 +1,199 @@
+from typing import Dict, Any, Optional
+from datetime import datetime, timedelta
+from fastapi import APIRouter, Depends, Query
+from sqlalchemy.orm import Session
+from sqlalchemy import func, desc
+from app.core.deps import get_db, get_current_active_user
+from app.models.user import User
+from app.models.analytics import Analytics
+from app.models.match import Match
+
+router = APIRouter()
+
+
+@router.get("/user-stats")
+def get_user_analytics(
+    days: int = Query(30, ge=1, le=365),
+    current_user: User = Depends(get_current_active_user),
+    db: Session = Depends(get_db)
+):
+    """Get user analytics and improvement tracking"""
+    start_date = datetime.utcnow() - timedelta(days=days)
+    
+    # Activity stats
+    activities = db.query(
+        Analytics.event_type,
+        func.count(Analytics.id).label("count")
+    ).filter(
+        Analytics.user_id == current_user.id,
+        Analytics.created_at >= start_date
+    ).group_by(Analytics.event_type).all()
+    
+    activity_stats = {activity.event_type: activity.count for activity in activities}
+    
+    # Match score improvement over time
+    matches = db.query(Match).filter(
+        Match.user_id == current_user.id,
+        Match.created_at >= start_date
+    ).order_by(Match.created_at).all()
+    
+    improvement_trend = []
+    if matches:
+        # Group matches by week
+        weekly_scores = {}
+        for match in matches:
+            week_key = match.created_at.strftime("%Y-W%U")
+            if week_key not in weekly_scores:
+                weekly_scores[week_key] = []
+            weekly_scores[week_key].append(match.match_score)
+        
+        # Calculate average score per week
+        for week, scores in weekly_scores.items():
+            improvement_trend.append({
+                "week": week,
+                "avg_score": round(sum(scores) / len(scores), 2),
+                "match_count": len(scores)
+            })
+    
+    # Best and recent matches
+    best_matches = db.query(Match).filter(
+        Match.user_id == current_user.id
+    ).order_by(desc(Match.match_score)).limit(5).all()
+    
+    recent_matches = db.query(Match).filter(
+        Match.user_id == current_user.id
+    ).order_by(desc(Match.created_at)).limit(5).all()
+    
+    return {
+        "period_days": days,
+        "activity_stats": activity_stats,
+        "improvement_trend": improvement_trend,
+        "total_matches": len(matches),
+        "avg_match_score": round(sum(m.match_score for m in matches) / len(matches), 2) if matches else 0,
+        "best_matches": [
+            {
+                "match_id": m.id,
+                "score": m.match_score,
+                "created_at": m.created_at
+            } for m in best_matches
+        ],
+        "recent_matches": [
+            {
+                "match_id": m.id,
+                "score": m.match_score,
+                "created_at": m.created_at
+            } for m in recent_matches
+        ]
+    }
+
+
+@router.get("/skill-gaps")
+def get_skill_gap_analysis(
+    current_user: User = Depends(get_current_active_user),
+    db: Session = Depends(get_db)
+):
+    """Get skill gap analysis across all matches"""
+    from app.models.match import SkillGap
+    
+    # Get all skill gaps for user
+    skill_gaps = db.query(
+        SkillGap.missing_skill,
+        SkillGap.importance,
+        func.count(SkillGap.id).label("frequency")
+    ).join(Match).filter(
+        Match.user_id == current_user.id
+    ).group_by(
+        SkillGap.missing_skill,
+        SkillGap.importance
+    ).order_by(desc(func.count(SkillGap.id))).all()
+    
+    # Group by importance
+    skill_analysis = {
+        "required": [],
+        "preferred": [],
+        "other": []
+    }
+    
+    for gap in skill_gaps:
+        category = gap.importance if gap.importance in ["required", "preferred"] else "other"
+        skill_analysis[category].append({
+            "skill": gap.missing_skill,
+            "frequency": gap.frequency
+        })
+    
+    # Overall skill recommendations
+    top_skills = [gap.missing_skill for gap in skill_gaps[:10]]
+    
+    return {
+        "skill_gaps_by_importance": skill_analysis,
+        "top_missing_skills": top_skills,
+        "total_unique_gaps": len(skill_gaps)
+    }
+
+
+@router.get("/improvement-suggestions")
+def get_improvement_suggestions(
+    current_user: User = Depends(get_current_active_user),
+    db: Session = Depends(get_db)
+):
+    """Get personalized improvement suggestions"""
+    # Get recent matches with low scores
+    low_score_matches = db.query(Match).filter(
+        Match.user_id == current_user.id,
+        Match.match_score < 70
+    ).order_by(desc(Match.created_at)).limit(10).all()
+    
+    # Analyze common issues
+    common_suggestions = {}
+    for match in low_score_matches:
+        if match.resume_suggestions:
+            for suggestion in match.resume_suggestions:
+                if isinstance(suggestion, dict):
+                    section = suggestion.get("section", "general")
+                    if section not in common_suggestions:
+                        common_suggestions[section] = []
+                    common_suggestions[section].append(suggestion.get("suggestion", ""))
+    
+    # Get skill gap patterns
+    from app.models.match import SkillGap
+    frequent_gaps = db.query(
+        SkillGap.missing_skill,
+        func.count(SkillGap.id).label("count")
+    ).join(Match).filter(
+        Match.user_id == current_user.id
+    ).group_by(SkillGap.missing_skill).order_by(
+        desc(func.count(SkillGap.id))
+    ).limit(5).all()
+    
+    return {
+        "priority_improvements": [
+            {
+                "area": "Skills",
+                "suggestion": f"Focus on learning {gap.missing_skill}",
+                "frequency": gap.count,
+                "impact": "high" if gap.count >= 3 else "medium"
+            } for gap in frequent_gaps
+        ],
+        "resume_improvements": common_suggestions,
+        "overall_recommendation": "Focus on the most frequently missing skills to improve your match scores."
+    }
+
+
+@router.post("/track-event")
+def track_analytics_event(
+    event_type: str,
+    event_data: Optional[Dict[str, Any]] = None,
+    current_user: User = Depends(get_current_active_user),
+    db: Session = Depends(get_db)
+):
+    """Track custom analytics event"""
+    analytics = Analytics(
+        user_id=current_user.id,
+        event_type=event_type,
+        event_data=event_data or {}
+    )
+    
+    db.add(analytics)
+    db.commit()
+    
+    return {"message": "Event tracked successfully"}

app/api/v1/auth.py

@@ -0,0 +1,56 @@
+from datetime import timedelta
+from fastapi import APIRouter, Depends, HTTPException, status
+from fastapi.security import OAuth2PasswordRequestForm
+from sqlalchemy.orm import Session
+from app.core.config import settings
+from app.core.security import create_access_token, verify_password, get_password_hash
+from app.core.deps import get_db
+from app.models.user import User
+from app.schemas.user import UserCreate, UserResponse, Token
+
+router = APIRouter()
+
+
+@router.post("/register", response_model=UserResponse)
+def register(user: UserCreate, db: Session = Depends(get_db)):
+    """Register a new user"""
+    # Check if user already exists
+    existing_user = db.query(User).filter(User.email == user.email).first()
+    if existing_user:
+        raise HTTPException(
+            status_code=400,
+            detail="Email already registered"
+        )
+    
+    # Create new user
+    hashed_password = get_password_hash(user.password)
+    db_user = User(
+        email=user.email,
+        hashed_password=hashed_password,
+        full_name=user.full_name,
+        role=user.role
+    )
+    db.add(db_user)
+    db.commit()
+    db.refresh(db_user)
+    
+    return db_user
+
+
+@router.post("/login", response_model=Token)
+def login(form_data: OAuth2PasswordRequestForm = Depends(), db: Session = Depends(get_db)):
+    """Login and get access token"""
+    user = db.query(User).filter(User.email == form_data.username).first()
+    if not user or not verify_password(form_data.password, user.hashed_password):
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Incorrect email or password",
+            headers={"WWW-Authenticate": "Bearer"},
+        )
+    
+    access_token_expires = timedelta(minutes=settings.ACCESS_TOKEN_EXPIRE_MINUTES)
+    access_token = create_access_token(
+        subject=user.email, expires_delta=access_token_expires
+    )
+    
+    return {"access_token": access_token, "token_type": "bearer"}

app/api/v1/dashboard.py

@@ -0,0 +1,154 @@
+from fastapi import APIRouter, Depends, Query, HTTPException
+from sqlalchemy.orm import Session
+from sqlalchemy import func, desc
+from app.core.deps import get_db, get_current_recruiter
+from app.models.user import User
+from app.models.job import Job
+from app.models.match import Match
+from app.models.resume import Resume
+
+router = APIRouter()
+
+
+@router.get("/candidates/{job_id}")
+def get_candidates_for_job(
+    job_id: int,
+    min_score: float = Query(0, ge=0, le=100),
+    limit: int = Query(50, ge=1, le=100),
+    current_user: User = Depends(get_current_recruiter),
+    db: Session = Depends(get_db)
+):
+    """Get ranked candidates for a specific job"""
+    # Verify job belongs to recruiter
+    job = db.query(Job).filter(
+        Job.id == job_id,
+        Job.recruiter_id == current_user.id
+    ).first()
+    
+    if not job:
+        raise HTTPException(status_code=404, detail="Job not found")
+    
+    # Get matches for this job, ordered by score
+    matches = db.query(Match).join(Resume).join(User).filter(
+        Match.job_id == job_id,
+        Match.match_score >= min_score
+    ).order_by(desc(Match.match_score)).limit(limit).all()
+    
+    # Format response with candidate info
+    candidates = []
+    for match in matches:
+        resume = db.query(Resume).filter(Resume.id == match.resume_id).first()
+        user = db.query(User).filter(User.id == match.user_id).first()
+        
+        candidates.append({
+            "match_id": match.id,
+            "candidate_name": user.full_name,
+            "candidate_email": user.email,
+            "resume_title": resume.title,
+            "match_score": match.match_score,
+            "skill_match_score": match.skill_match_score,
+            "experience_match_score": match.experience_match_score,
+            "education_match_score": match.education_match_score,
+            "skills": resume.skills,
+            "experience_years": resume.experience_years,
+            "education_level": resume.education_level,
+            "created_at": match.created_at
+        })
+    
+    return {
+        "job_title": job.title,
+        "job_company": job.company,
+        "total_candidates": len(candidates),
+        "candidates": candidates
+    }
+
+
+@router.get("/jobs/stats")
+def get_job_stats(
+    current_user: User = Depends(get_current_recruiter),
+    db: Session = Depends(get_db)
+):
+    """Get statistics for recruiter's jobs"""
+    # Get job stats
+    job_stats = db.query(
+        Job.id,
+        Job.title,
+        Job.company,
+        Job.created_at,
+        func.count(Match.id).label("total_matches"),
+        func.avg(Match.match_score).label("avg_match_score"),
+        func.max(Match.match_score).label("best_match_score")
+    ).outerjoin(Match).filter(
+        Job.recruiter_id == current_user.id
+    ).group_by(Job.id).all()
+    
+    return [
+        {
+            "job_id": stat.id,
+            "job_title": stat.title,
+            "company": stat.company,
+            "created_at": stat.created_at,
+            "total_matches": stat.total_matches or 0,
+            "avg_match_score": round(stat.avg_match_score or 0, 2),
+            "best_match_score": stat.best_match_score or 0
+        }
+        for stat in job_stats
+    ]
+
+
+@router.get("/overview")
+def get_dashboard_overview(
+    current_user: User = Depends(get_current_recruiter),
+    db: Session = Depends(get_db)
+):
+    """Get overview statistics for recruiter dashboard"""
+    # Total jobs
+    total_jobs = db.query(Job).filter(Job.recruiter_id == current_user.id).count()
+    
+    # Active jobs
+    active_jobs = db.query(Job).filter(
+        Job.recruiter_id == current_user.id,
+        Job.is_active
+    ).count()
+    
+    # Total matches across all jobs
+    total_matches = db.query(Match).join(Job).filter(
+        Job.recruiter_id == current_user.id
+    ).count()
+    
+    # High-quality matches (score >= 80)
+    high_quality_matches = db.query(Match).join(Job).filter(
+        Job.recruiter_id == current_user.id,
+        Match.match_score >= 80
+    ).count()
+    
+    # Recent matches (last 7 days)
+    from datetime import datetime, timedelta
+    recent_matches = db.query(Match).join(Job).filter(
+        Job.recruiter_id == current_user.id,
+        Match.created_at >= datetime.utcnow() - timedelta(days=7)
+    ).count()
+    
+    # Top performing job
+    top_job = db.query(
+        Job.title,
+        func.count(Match.id).label("match_count"),
+        func.avg(Match.match_score).label("avg_score")
+    ).outerjoin(Match).filter(
+        Job.recruiter_id == current_user.id
+    ).group_by(Job.id).order_by(
+        desc(func.count(Match.id))
+    ).first()
+    
+    return {
+        "total_jobs": total_jobs,
+        "active_jobs": active_jobs,
+        "total_matches": total_matches,
+        "high_quality_matches": high_quality_matches,
+        "recent_matches": recent_matches,
+        "top_performing_job": {
+            "title": top_job.title if top_job else None,
+            "match_count": top_job.match_count if top_job else 0,
+            "avg_score": round(top_job.avg_score or 0, 2) if top_job else 0
+        }
+    }

app/api/v1/jobs.py

@@ -0,0 +1,141 @@
+from typing import List, Optional
+from fastapi import APIRouter, Depends, HTTPException, Query
+from sqlalchemy.orm import Session
+from app.core.deps import get_db, get_current_recruiter
+from app.models.user import User
+from app.models.job import Job
+from app.schemas.job import JobResponse, JobCreate, JobUpdate
+from app.services.ai_service import AIService
+
+router = APIRouter()
+ai_service = AIService()
+
+
+@router.post("/", response_model=JobResponse)
+async def create_job(
+    job: JobCreate,
+    current_user: User = Depends(get_current_recruiter),
+    db: Session = Depends(get_db)
+):
+    """Create a new job posting"""
+    # Analyze job description with AI
+    job_analysis = await ai_service.analyze_job_description(job.description)
+    
+    # Create job record
+    db_job = Job(
+        recruiter_id=current_user.id,
+        title=job.title,
+        company=job.company,
+        description=job.description,
+        requirements=job.requirements,
+        location=job.location,
+        job_type=job.job_type,
+        salary_range=job.salary_range,
+        required_skills=job_analysis.get("required_skills", job.required_skills),
+        preferred_skills=job_analysis.get("preferred_skills", job.preferred_skills),
+        experience_level=job_analysis.get("experience_level", job.experience_level),
+        education_requirement=job_analysis.get("education_requirement", job.education_requirement)
+    )
+    
+    db.add(db_job)
+    db.commit()
+    db.refresh(db_job)
+    
+    return db_job
+
+
+@router.get("/", response_model=List[JobResponse])
+def get_jobs(
+    skip: int = Query(0, ge=0),
+    limit: int = Query(100, ge=1, le=100),
+    search: Optional[str] = Query(None),
+    location: Optional[str] = Query(None),
+    job_type: Optional[str] = Query(None),
+    db: Session = Depends(get_db)
+):
+    """Get all active job postings with filtering"""
+    query = db.query(Job).filter(Job.is_active)
+    
+    if search:
+        query = query.filter(
+            Job.title.contains(search) |
+            Job.description.contains(search) |
+            Job.company.contains(search)
+        )
+    
+    if location:
+        query = query.filter(Job.location.contains(location))
+    
+    if job_type:
+        query = query.filter(Job.job_type == job_type)
+    
+    return query.offset(skip).limit(limit).all()
+
+
+@router.get("/my-jobs", response_model=List[JobResponse])
+def get_my_jobs(
+    current_user: User = Depends(get_current_recruiter),
+    db: Session = Depends(get_db)
+):
+    """Get jobs created by current recruiter"""
+    return db.query(Job).filter(Job.recruiter_id == current_user.id).all()
+
+
+@router.get("/{job_id}", response_model=JobResponse)
+def get_job(
+    job_id: int,
+    db: Session = Depends(get_db)
+):
+    """Get specific job"""
+    job = db.query(Job).filter(Job.id == job_id, Job.is_active).first()
+    
+    if not job:
+        raise HTTPException(status_code=404, detail="Job not found")
+    
+    return job
+
+
+@router.put("/{job_id}", response_model=JobResponse)
+def update_job(
+    job_id: int,
+    job_update: JobUpdate,
+    current_user: User = Depends(get_current_recruiter),
+    db: Session = Depends(get_db)
+):
+    """Update job posting"""
+    job = db.query(Job).filter(
+        Job.id == job_id,
+        Job.recruiter_id == current_user.id
+    ).first()
+    
+    if not job:
+        raise HTTPException(status_code=404, detail="Job not found")
+    
+    for field, value in job_update.dict(exclude_unset=True).items():
+        setattr(job, field, value)
+    
+    db.commit()
+    db.refresh(job)
+    
+    return job
+
+
+@router.delete("/{job_id}")
+def delete_job(
+    job_id: int,
+    current_user: User = Depends(get_current_recruiter),
+    db: Session = Depends(get_db)
+):
+    """Delete job posting"""
+    job = db.query(Job).filter(
+        Job.id == job_id,
+        Job.recruiter_id == current_user.id
+    ).first()
+    
+    if not job:
+        raise HTTPException(status_code=404, detail="Job not found")
+    
+    db.delete(job)
+    db.commit()
+    
+    return {"message": "Job deleted successfully"}

app/api/v1/matching.py

@@ -0,0 +1,208 @@
+from typing import List
+from fastapi import APIRouter, Depends, HTTPException
+from sqlalchemy.orm import Session
+from app.core.deps import get_db, get_current_active_user
+from app.models.user import User
+from app.models.resume import Resume
+from app.models.job import Job
+from app.models.match import Match, SkillGap
+from app.schemas.match import MatchResponse, MatchRequest
+from app.services.ai_service import AIService
+from app.models.analytics import Analytics
+
+router = APIRouter()
+ai_service = AIService()
+
+
+@router.post("/analyze", response_model=MatchResponse)
+async def analyze_match(
+    match_request: MatchRequest,
+    current_user: User = Depends(get_current_active_user),
+    db: Session = Depends(get_db)
+):
+    """Analyze match between resume and job description"""
+    # Verify resume belongs to user
+    resume = db.query(Resume).filter(
+        Resume.id == match_request.resume_id,
+        Resume.user_id == current_user.id
+    ).first()
+    
+    if not resume:
+        raise HTTPException(status_code=404, detail="Resume not found")
+    
+    # Get job
+    job = db.query(Job).filter(
+        Job.id == match_request.job_id,
+        Job.is_active
+    ).first()
+    
+    if not job:
+        raise HTTPException(status_code=404, detail="Job not found")
+    
+    # Check if match already exists
+    existing_match = db.query(Match).filter(
+        Match.user_id == current_user.id,
+        Match.resume_id == match_request.resume_id,
+        Match.job_id == match_request.job_id
+    ).first()
+    
+    if existing_match:
+        return existing_match
+    
+    # Prepare data for AI analysis
+    resume_data = {
+        "skills": resume.skills or [],
+        "experience_years": resume.experience_years,
+        "education_level": resume.education_level,
+        "parsed_data": resume.parsed_data or {}
+    }
+    
+    job_data = {
+        "required_skills": job.required_skills or [],
+        "preferred_skills": job.preferred_skills or [],
+        "experience_level": job.experience_level,
+        "education_requirement": job.education_requirement,
+        "description": job.description
+    }
+    
+    # Calculate match score using AI
+    match_analysis = await ai_service.calculate_match_score(resume_data, job_data)
+    
+    # Generate resume suggestions
+    suggestions = await ai_service.generate_resume_suggestions(
+        resume_data, job_data, match_analysis
+    )
+    
+    # Create match record
+    match = Match(
+        user_id=current_user.id,
+        resume_id=match_request.resume_id,
+        job_id=match_request.job_id,
+        match_score=match_analysis.get("overall_score", 0),
+        skill_match_score=match_analysis.get("skill_match_score", 0),
+        experience_match_score=match_analysis.get("experience_match_score", 0),
+        education_match_score=match_analysis.get("education_match_score", 0),
+        overall_feedback=match_analysis.get("overall_feedback", ""),
+        resume_suggestions=suggestions
+    )
+    
+    db.add(match)
+    db.commit()
+    db.refresh(match)
+    
+    # Create skill gap records
+    missing_skills = match_analysis.get("missing_skills", [])
+    for skill_data in missing_skills:
+        skill_gap = SkillGap(
+            match_id=match.id,
+            missing_skill=skill_data.get("skill", ""),
+            importance=skill_data.get("importance", ""),
+            suggestion=skill_data.get("suggestion", "")
+        )
+        db.add(skill_gap)
+    
+    db.commit()
+    
+    # Log analytics
+    analytics = Analytics(
+        user_id=current_user.id,
+        event_type="job_match",
+        event_data={
+            "resume_id": match_request.resume_id,
+            "job_id": match_request.job_id,
+            "match_score": match.match_score
+        },
+        improvement_score=match.match_score
+    )
+    db.add(analytics)
+    db.commit()
+    
+    # Refresh to get skill gaps
+    db.refresh(match)
+    
+    return match
+
+
+@router.get("/", response_model=List[MatchResponse])
+def get_matches(
+    current_user: User = Depends(get_current_active_user),
+    db: Session = Depends(get_db)
+):
+    """Get all matches for current user"""
+    return db.query(Match).filter(Match.user_id == current_user.id).all()
+
+
+@router.get("/{match_id}", response_model=MatchResponse)
+def get_match(
+    match_id: int,
+    current_user: User = Depends(get_current_active_user),
+    db: Session = Depends(get_db)
+):
+    """Get specific match"""
+    match = db.query(Match).filter(
+        Match.id == match_id,
+        Match.user_id == current_user.id
+    ).first()
+    
+    if not match:
+        raise HTTPException(status_code=404, detail="Match not found")
+    
+    return match
+
+
+@router.post("/{match_id}/cover-letter")
+async def generate_cover_letter(
+    match_id: int,
+    current_user: User = Depends(get_current_active_user),
+    db: Session = Depends(get_db)
+):
+    """Generate cover letter for specific match"""
+    match = db.query(Match).filter(
+        Match.id == match_id,
+        Match.user_id == current_user.id
+    ).first()
+    
+    if not match:
+        raise HTTPException(status_code=404, detail="Match not found")
+    
+    # Get resume and job data
+    resume = db.query(Resume).filter(Resume.id == match.resume_id).first()
+    job = db.query(Job).filter(Job.id == match.job_id).first()
+    
+    resume_data = {
+        "skills": resume.skills or [],
+        "experience_years": resume.experience_years,
+        "education_level": resume.education_level,
+        "parsed_data": resume.parsed_data or {}
+    }
+    
+    job_data = {
+        "title": job.title,
+        "company": job.company,
+        "description": job.description,
+        "required_skills": job.required_skills or [],
+        "preferred_skills": job.preferred_skills or []
+    }
+    
+    # Generate cover letter
+    cover_letter = await ai_service.generate_cover_letter(
+        resume_data, job_data, current_user.full_name
+    )
+    
+    # Update match with cover letter
+    match.cover_letter = cover_letter
+    db.commit()
+    
+    # Log analytics
+    analytics = Analytics(
+        user_id=current_user.id,
+        event_type="cover_letter_generate",
+        event_data={
+            "match_id": match_id,
+            "job_id": match.job_id
+        }
+    )
+    db.add(analytics)
+    db.commit()
+    
+    return {"cover_letter": cover_letter}

app/api/v1/resumes.py

@@ -0,0 +1,136 @@
+from typing import List
+from fastapi import APIRouter, Depends, HTTPException, UploadFile, File
+from sqlalchemy.orm import Session
+from app.core.deps import get_db, get_current_active_user
+from app.models.user import User
+from app.models.resume import Resume
+from app.schemas.resume import ResumeResponse, ResumeUpdate
+from app.services.file_service import FileService
+from app.services.resume_parser import ResumeParser
+from app.services.ai_service import AIService
+
+router = APIRouter()
+file_service = FileService()
+resume_parser = ResumeParser()
+ai_service = AIService()
+
+
+@router.post("/upload", response_model=ResumeResponse)
+async def upload_resume(
+    title: str,
+    file: UploadFile = File(...),
+    current_user: User = Depends(get_current_active_user),
+    db: Session = Depends(get_db)
+):
+    """Upload and parse resume file"""
+    # Save file
+    file_path, original_filename = await file_service.save_file(file, current_user.id)
+    
+    # Extract text from file
+    extracted_text = resume_parser.extract_text(file_path)
+    if not extracted_text:
+        # Clean up file if parsing failed
+        file_service.delete_file(file_path)
+        raise HTTPException(
+            status_code=400,
+            detail="Failed to extract text from file"
+        )
+    
+    # Analyze resume with AI
+    parsed_data = await ai_service.analyze_resume(extracted_text)
+    
+    # Create resume record
+    resume = Resume(
+        user_id=current_user.id,
+        title=title,
+        file_path=file_path,
+        original_filename=original_filename,
+        extracted_text=extracted_text,
+        parsed_data=parsed_data,
+        skills=parsed_data.get("skills", []),
+        experience_years=parsed_data.get("experience_years"),
+        education_level=parsed_data.get("education_level")
+    )
+    
+    db.add(resume)
+    db.commit()
+    db.refresh(resume)
+    
+    return resume
+
+
+@router.get("/", response_model=List[ResumeResponse])
+def get_resumes(
+    current_user: User = Depends(get_current_active_user),
+    db: Session = Depends(get_db)
+):
+    """Get all resumes for current user"""
+    return db.query(Resume).filter(Resume.user_id == current_user.id).all()
+
+
+@router.get("/{resume_id}", response_model=ResumeResponse)
+def get_resume(
+    resume_id: int,
+    current_user: User = Depends(get_current_active_user),
+    db: Session = Depends(get_db)
+):
+    """Get specific resume"""
+    resume = db.query(Resume).filter(
+        Resume.id == resume_id,
+        Resume.user_id == current_user.id
+    ).first()
+    
+    if not resume:
+        raise HTTPException(status_code=404, detail="Resume not found")
+    
+    return resume
+
+
+@router.put("/{resume_id}", response_model=ResumeResponse)
+def update_resume(
+    resume_id: int,
+    resume_update: ResumeUpdate,
+    current_user: User = Depends(get_current_active_user),
+    db: Session = Depends(get_db)
+):
+    """Update resume"""
+    resume = db.query(Resume).filter(
+        Resume.id == resume_id,
+        Resume.user_id == current_user.id
+    ).first()
+    
+    if not resume:
+        raise HTTPException(status_code=404, detail="Resume not found")
+    
+    for field, value in resume_update.dict(exclude_unset=True).items():
+        setattr(resume, field, value)
+    
+    db.commit()
+    db.refresh(resume)
+    
+    return resume
+
+
+@router.delete("/{resume_id}")
+def delete_resume(
+    resume_id: int,
+    current_user: User = Depends(get_current_active_user),
+    db: Session = Depends(get_db)
+):
+    """Delete resume"""
+    resume = db.query(Resume).filter(
+        Resume.id == resume_id,
+        Resume.user_id == current_user.id
+    ).first()
+    
+    if not resume:
+        raise HTTPException(status_code=404, detail="Resume not found")
+    
+    # Delete file
+    if resume.file_path:
+        file_service.delete_file(resume.file_path)
+    
+    db.delete(resume)
+    db.commit()
+    
+    return {"message": "Resume deleted successfully"}

app/api/v1/router.py

@@ -0,0 +1,11 @@
+from fastapi import APIRouter
+from app.api.v1 import auth, resumes, jobs, matching, dashboard, analytics
+
+api_router = APIRouter()
+
+api_router.include_router(auth.router, prefix="/auth", tags=["Authentication"])
+api_router.include_router(resumes.router, prefix="/resumes", tags=["Resumes"])
+api_router.include_router(jobs.router, prefix="/jobs", tags=["Jobs"])
+api_router.include_router(matching.router, prefix="/matching", tags=["Matching"])
+api_router.include_router(dashboard.router, prefix="/dashboard", tags=["Dashboard"])
+api_router.include_router(analytics.router, prefix="/analytics", tags=["Analytics"])

app/core/config.py

@@ -0,0 +1,26 @@
+import os
+from pydantic_settings import BaseSettings
+
+
+class Settings(BaseSettings):
+    APP_NAME: str = "SkillSync - AI Resume & Job Match Hub"
+    APP_VERSION: str = "1.0.0"
+    SECRET_KEY: str = os.getenv("SECRET_KEY", "your-secret-key-change-in-production")
+    ACCESS_TOKEN_EXPIRE_MINUTES: int = 30
+    ALGORITHM: str = "HS256"
+    
+    # OpenAI Configuration
+    OPENAI_API_KEY: str = os.getenv("OPENAI_API_KEY", "")
+    
+    # CORS settings
+    CORS_ORIGINS: list = ["*"]
+    
+    # File upload settings
+    MAX_FILE_SIZE: int = 10 * 1024 * 1024  # 10MB
+    ALLOWED_FILE_EXTENSIONS: list = [".pdf", ".docx", ".txt"]
+    
+    class Config:
+        env_file = ".env"
+
+
+settings = Settings()

app/core/deps.py

@@ -0,0 +1,52 @@
+from fastapi import Depends, HTTPException, status
+from fastapi.security import HTTPBearer
+from jose import jwt, JWTError
+from sqlalchemy.orm import Session
+from app.core.config import settings
+from app.db.session import get_db
+from app.models.user import User
+from app.schemas.user import TokenData
+
+security = HTTPBearer()
+
+
+def get_current_user(
+    db: Session = Depends(get_db),
+    token: str = Depends(security)
+) -> User:
+    credentials_exception = HTTPException(
+        status_code=status.HTTP_401_UNAUTHORIZED,
+        detail="Could not validate credentials",
+        headers={"WWW-Authenticate": "Bearer"},
+    )
+    try:
+        payload = jwt.decode(
+            token.credentials, settings.SECRET_KEY, algorithms=[settings.ALGORITHM]
+        )
+        email: str = payload.get("sub")
+        if email is None:
+            raise credentials_exception
+        token_data = TokenData(email=email)
+    except JWTError:
+        raise credentials_exception
+    
+    user = db.query(User).filter(User.email == token_data.email).first()
+    if user is None:
+        raise credentials_exception
+    return user
+
+
+def get_current_active_user(current_user: User = Depends(get_current_user)) -> User:
+    if not current_user.is_active:
+        raise HTTPException(status_code=400, detail="Inactive user")
+    return current_user
+
+
+def get_current_recruiter(current_user: User = Depends(get_current_active_user)) -> User:
+    from app.models.user import UserRole
+    if current_user.role != UserRole.RECRUITER and current_user.role != UserRole.ADMIN:
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
+            detail="Not enough permissions"
+        )
+    return current_user

app/core/security.py

@@ -0,0 +1,29 @@
+from datetime import datetime, timedelta
+from typing import Any, Union
+from jose import jwt
+from passlib.context import CryptContext
+from app.core.config import settings
+
+pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
+
+
+def create_access_token(
+    subject: Union[str, Any], expires_delta: timedelta = None
+) -> str:
+    if expires_delta:
+        expire = datetime.utcnow() + expires_delta
+    else:
+        expire = datetime.utcnow() + timedelta(
+            minutes=settings.ACCESS_TOKEN_EXPIRE_MINUTES
+        )
+    to_encode = {"exp": expire, "sub": str(subject)}
+    encoded_jwt = jwt.encode(to_encode, settings.SECRET_KEY, algorithm=settings.ALGORITHM)
+    return encoded_jwt
+
+
+def verify_password(plain_password: str, hashed_password: str) -> bool:
+    return pwd_context.verify(plain_password, hashed_password)
+
+
+def get_password_hash(password: str) -> str:
+    return pwd_context.hash(password)

app/db/base.py

@@ -0,0 +1,3 @@
+from sqlalchemy.ext.declarative import declarative_base
+
+Base = declarative_base()

app/db/session.py

@@ -0,0 +1,23 @@
+from pathlib import Path
+from sqlalchemy import create_engine
+from sqlalchemy.orm import sessionmaker
+
+DB_DIR = Path("/app") / "storage" / "db"
+DB_DIR.mkdir(parents=True, exist_ok=True)
+
+SQLALCHEMY_DATABASE_URL = f"sqlite:///{DB_DIR}/db.sqlite"
+
+engine = create_engine(
+    SQLALCHEMY_DATABASE_URL,
+    connect_args={"check_same_thread": False}
+)
+
+SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+
+
+def get_db():
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()

app/models/__init__.py

@@ -0,0 +1,5 @@
+from .user import User
+from .resume import Resume
+from .job import Job
+from .match import Match, SkillGap
+from .analytics import Analytics

app/models/analytics.py

@@ -0,0 +1,19 @@
+from sqlalchemy import Column, Integer, String, DateTime, ForeignKey, JSON, Float
+from sqlalchemy.sql import func
+from sqlalchemy.orm import relationship
+from app.db.base import Base
+
+
+class Analytics(Base):
+    __tablename__ = "analytics"
+
+    id = Column(Integer, primary_key=True, index=True)
+    user_id = Column(Integer, ForeignKey("users.id"), nullable=False)
+    event_type = Column(String, nullable=False)  # resume_upload, job_match, cover_letter_generate
+    event_data = Column(JSON, nullable=True)  # Additional event details
+    improvement_score = Column(Float, nullable=True)  # Track improvement over time
+    session_id = Column(String, nullable=True)  # Track user sessions
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+
+    # Relationships
+    user = relationship("User", back_populates="analytics")

app/models/job.py

@@ -0,0 +1,29 @@
+from sqlalchemy import Column, Integer, String, Text, DateTime, ForeignKey, JSON, Boolean
+from sqlalchemy.sql import func
+from sqlalchemy.orm import relationship
+from app.db.base import Base
+
+
+class Job(Base):
+    __tablename__ = "jobs"
+
+    id = Column(Integer, primary_key=True, index=True)
+    recruiter_id = Column(Integer, ForeignKey("users.id"), nullable=False)
+    title = Column(String, nullable=False)
+    company = Column(String, nullable=False)
+    description = Column(Text, nullable=False)
+    requirements = Column(Text, nullable=True)
+    location = Column(String, nullable=True)
+    job_type = Column(String, nullable=True)  # full-time, part-time, contract
+    salary_range = Column(String, nullable=True)
+    required_skills = Column(JSON, nullable=True)  # Extracted required skills
+    preferred_skills = Column(JSON, nullable=True)  # Extracted preferred skills
+    experience_level = Column(String, nullable=True)  # entry, mid, senior
+    education_requirement = Column(String, nullable=True)
+    is_active = Column(Boolean, default=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), onupdate=func.now())
+
+    # Relationships
+    recruiter = relationship("User", back_populates="jobs")
+    matches = relationship("Match", back_populates="job")

app/models/match.py

@@ -0,0 +1,42 @@
+from sqlalchemy import Column, Integer, String, Text, DateTime, ForeignKey, JSON, Float
+from sqlalchemy.sql import func
+from sqlalchemy.orm import relationship
+from app.db.base import Base
+
+
+class Match(Base):
+    __tablename__ = "matches"
+
+    id = Column(Integer, primary_key=True, index=True)
+    user_id = Column(Integer, ForeignKey("users.id"), nullable=False)
+    resume_id = Column(Integer, ForeignKey("resumes.id"), nullable=False)
+    job_id = Column(Integer, ForeignKey("jobs.id"), nullable=False)
+    match_score = Column(Float, nullable=False)  # 0-100 percentage
+    skill_match_score = Column(Float, nullable=True)
+    experience_match_score = Column(Float, nullable=True)
+    education_match_score = Column(Float, nullable=True)
+    overall_feedback = Column(Text, nullable=True)
+    resume_suggestions = Column(JSON, nullable=True)  # AI suggestions for resume improvement
+    cover_letter = Column(Text, nullable=True)  # Generated cover letter
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), onupdate=func.now())
+
+    # Relationships
+    user = relationship("User", back_populates="matches")
+    resume = relationship("Resume", back_populates="matches")
+    job = relationship("Job", back_populates="matches")
+    skill_gaps = relationship("SkillGap", back_populates="match")
+
+
+class SkillGap(Base):
+    __tablename__ = "skill_gaps"
+
+    id = Column(Integer, primary_key=True, index=True)
+    match_id = Column(Integer, ForeignKey("matches.id"), nullable=False)
+    missing_skill = Column(String, nullable=False)
+    importance = Column(String, nullable=True)  # required, preferred, nice-to-have
+    suggestion = Column(Text, nullable=True)  # How to acquire this skill
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+
+    # Relationships
+    match = relationship("Match", back_populates="skill_gaps")

app/models/resume.py

@@ -0,0 +1,25 @@
+from sqlalchemy import Column, Integer, String, Text, DateTime, ForeignKey, JSON
+from sqlalchemy.sql import func
+from sqlalchemy.orm import relationship
+from app.db.base import Base
+
+
+class Resume(Base):
+    __tablename__ = "resumes"
+
+    id = Column(Integer, primary_key=True, index=True)
+    user_id = Column(Integer, ForeignKey("users.id"), nullable=False)
+    title = Column(String, nullable=False)
+    file_path = Column(String, nullable=True)
+    original_filename = Column(String, nullable=True)
+    extracted_text = Column(Text, nullable=True)
+    parsed_data = Column(JSON, nullable=True)  # Structured resume data
+    skills = Column(JSON, nullable=True)  # Extracted skills
+    experience_years = Column(Integer, nullable=True)
+    education_level = Column(String, nullable=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), onupdate=func.now())
+
+    # Relationships
+    user = relationship("User", back_populates="resumes")
+    matches = relationship("Match", back_populates="resume")

app/models/user.py

@@ -0,0 +1,30 @@
+from sqlalchemy import Column, Integer, String, Boolean, DateTime, Enum
+from sqlalchemy.sql import func
+from sqlalchemy.orm import relationship
+from app.db.base import Base
+import enum
+
+
+class UserRole(enum.Enum):
+    APPLICANT = "applicant"
+    RECRUITER = "recruiter"
+    ADMIN = "admin"
+
+
+class User(Base):
+    __tablename__ = "users"
+
+    id = Column(Integer, primary_key=True, index=True)
+    email = Column(String, unique=True, index=True, nullable=False)
+    hashed_password = Column(String, nullable=False)
+    full_name = Column(String, nullable=False)
+    role = Column(Enum(UserRole), default=UserRole.APPLICANT)
+    is_active = Column(Boolean, default=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), onupdate=func.now())
+
+    # Relationships
+    resumes = relationship("Resume", back_populates="user")
+    jobs = relationship("Job", back_populates="recruiter")
+    matches = relationship("Match", back_populates="user")
+    analytics = relationship("Analytics", back_populates="user")

app/schemas/__init__.py

@@ -0,0 +1,5 @@
+from .user import User, UserCreate, UserUpdate, UserResponse, Token
+from .resume import Resume, ResumeCreate, ResumeUpdate, ResumeResponse
+from .job import Job, JobCreate, JobUpdate, JobResponse
+from .match import Match, MatchResponse, SkillGap, SkillGapResponse
+from .analytics import Analytics, AnalyticsCreate, AnalyticsResponse

app/schemas/analytics.py

@@ -0,0 +1,27 @@
+from typing import Optional, Dict, Any
+from pydantic import BaseModel
+from datetime import datetime
+
+
+class AnalyticsBase(BaseModel):
+    event_type: str
+    event_data: Optional[Dict[str, Any]] = None
+    improvement_score: Optional[float] = None
+    session_id: Optional[str] = None
+
+
+class AnalyticsCreate(AnalyticsBase):
+    pass
+
+
+class AnalyticsResponse(AnalyticsBase):
+    id: int
+    user_id: int
+    created_at: datetime
+
+    class Config:
+        from_attributes = True
+
+
+class Analytics(AnalyticsResponse):
+    pass

app/schemas/job.py

@@ -0,0 +1,51 @@
+from typing import Optional, List
+from pydantic import BaseModel
+from datetime import datetime
+
+
+class JobBase(BaseModel):
+    title: str
+    company: str
+    description: str
+    requirements: Optional[str] = None
+    location: Optional[str] = None
+    job_type: Optional[str] = None
+    salary_range: Optional[str] = None
+    required_skills: Optional[List[str]] = None
+    preferred_skills: Optional[List[str]] = None
+    experience_level: Optional[str] = None
+    education_requirement: Optional[str] = None
+
+
+class JobCreate(JobBase):
+    pass
+
+
+class JobUpdate(BaseModel):
+    title: Optional[str] = None
+    company: Optional[str] = None
+    description: Optional[str] = None
+    requirements: Optional[str] = None
+    location: Optional[str] = None
+    job_type: Optional[str] = None
+    salary_range: Optional[str] = None
+    required_skills: Optional[List[str]] = None
+    preferred_skills: Optional[List[str]] = None
+    experience_level: Optional[str] = None
+    education_requirement: Optional[str] = None
+    is_active: Optional[bool] = None
+
+
+class JobResponse(JobBase):
+    id: int
+    recruiter_id: int
+    is_active: bool
+    created_at: datetime
+    updated_at: Optional[datetime] = None
+
+    class Config:
+        from_attributes = True
+
+
+class Job(JobResponse):
+    pass

app/schemas/match.py

@@ -0,0 +1,54 @@
+from typing import Optional, List, Dict, Any
+from pydantic import BaseModel
+from datetime import datetime
+
+
+class SkillGapBase(BaseModel):
+    missing_skill: str
+    importance: Optional[str] = None
+    suggestion: Optional[str] = None
+
+
+class SkillGapResponse(SkillGapBase):
+    id: int
+    match_id: int
+    created_at: datetime
+
+    class Config:
+        from_attributes = True
+
+
+class SkillGap(SkillGapResponse):
+    pass
+
+
+class MatchBase(BaseModel):
+    match_score: float
+    skill_match_score: Optional[float] = None
+    experience_match_score: Optional[float] = None
+    education_match_score: Optional[float] = None
+    overall_feedback: Optional[str] = None
+    resume_suggestions: Optional[List[Dict[str, Any]]] = None
+    cover_letter: Optional[str] = None
+
+
+class MatchResponse(MatchBase):
+    id: int
+    user_id: int
+    resume_id: int
+    job_id: int
+    skill_gaps: Optional[List[SkillGapResponse]] = None
+    created_at: datetime
+    updated_at: Optional[datetime] = None
+
+    class Config:
+        from_attributes = True
+
+
+class Match(MatchResponse):
+    pass
+
+
+class MatchRequest(BaseModel):
+    resume_id: int
+    job_id: int

app/schemas/resume.py

@@ -0,0 +1,41 @@
+from typing import Optional, List, Dict, Any
+from pydantic import BaseModel
+from datetime import datetime
+
+
+class ResumeBase(BaseModel):
+    title: str
+    extracted_text: Optional[str] = None
+    parsed_data: Optional[Dict[str, Any]] = None
+    skills: Optional[List[str]] = None
+    experience_years: Optional[int] = None
+    education_level: Optional[str] = None
+
+
+class ResumeCreate(ResumeBase):
+    pass
+
+
+class ResumeUpdate(BaseModel):
+    title: Optional[str] = None
+    extracted_text: Optional[str] = None
+    parsed_data: Optional[Dict[str, Any]] = None
+    skills: Optional[List[str]] = None
+    experience_years: Optional[int] = None
+    education_level: Optional[str] = None
+
+
+class ResumeResponse(ResumeBase):
+    id: int
+    user_id: int
+    file_path: Optional[str] = None
+    original_filename: Optional[str] = None
+    created_at: datetime
+    updated_at: Optional[datetime] = None
+
+    class Config:
+        from_attributes = True
+
+
+class Resume(ResumeResponse):
+    pass

app/schemas/user.py

@@ -0,0 +1,43 @@
+from typing import Optional
+from pydantic import BaseModel, EmailStr
+from datetime import datetime
+from app.models.user import UserRole
+
+
+class UserBase(BaseModel):
+    email: EmailStr
+    full_name: str
+    role: UserRole = UserRole.APPLICANT
+
+
+class UserCreate(UserBase):
+    password: str
+
+
+class UserUpdate(BaseModel):
+    email: Optional[EmailStr] = None
+    full_name: Optional[str] = None
+    password: Optional[str] = None
+
+
+class UserResponse(UserBase):
+    id: int
+    is_active: bool
+    created_at: datetime
+    updated_at: Optional[datetime] = None
+
+    class Config:
+        from_attributes = True
+
+
+class User(UserResponse):
+    pass
+
+
+class Token(BaseModel):
+    access_token: str
+    token_type: str
+
+
+class TokenData(BaseModel):
+    email: Optional[str] = None

app/services/__init__.py

@@ -0,0 +1,3 @@
+from .ai_service import AIService
+from .resume_parser import ResumeParser
+from .file_service import FileService

app/services/ai_service.py

@@ -0,0 +1,218 @@
+import openai
+from typing import Dict, List, Any
+from app.core.config import settings
+import json
+
+openai.api_key = settings.OPENAI_API_KEY
+
+
+class AIService:
+    def __init__(self):
+        self.client = openai.OpenAI(api_key=settings.OPENAI_API_KEY)
+
+    async def analyze_resume(self, resume_text: str) -> Dict[str, Any]:
+        """Extract structured data from resume text using AI"""
+        prompt = f"""
+        Analyze the following resume text and extract structured information:
+        
+        {resume_text}
+        
+        Please return a JSON object with the following structure:
+        {{
+            "skills": ["skill1", "skill2", ...],
+            "experience_years": number,
+            "education_level": "string",
+            "work_experience": [
+                {{
+                    "company": "string",
+                    "position": "string",
+                    "duration": "string",
+                    "description": "string"
+                }}
+            ],
+            "education": [
+                {{
+                    "institution": "string",
+                    "degree": "string",
+                    "field": "string",
+                    "year": "string"
+                }}
+            ],
+            "contact_info": {{
+                "email": "string",
+                "phone": "string",
+                "location": "string"
+            }}
+        }}
+        """
+        
+        try:
+            response = self.client.chat.completions.create(
+                model="gpt-3.5-turbo",
+                messages=[
+                    {"role": "system", "content": "You are an expert resume analyzer. Return only valid JSON."},
+                    {"role": "user", "content": prompt}
+                ],
+                temperature=0.1
+            )
+            
+            result = response.choices[0].message.content
+            return json.loads(result)
+        except Exception as e:
+            print(f"Error analyzing resume: {e}")
+            return {}
+
+    async def analyze_job_description(self, job_description: str) -> Dict[str, Any]:
+        """Extract structured data from job description using AI"""
+        prompt = f"""
+        Analyze the following job description and extract structured information:
+        
+        {job_description}
+        
+        Please return a JSON object with the following structure:
+        {{
+            "required_skills": ["skill1", "skill2", ...],
+            "preferred_skills": ["skill1", "skill2", ...],
+            "experience_level": "entry/mid/senior",
+            "education_requirement": "string",
+            "key_responsibilities": ["resp1", "resp2", ...],
+            "company_benefits": ["benefit1", "benefit2", ...],
+            "job_type": "full-time/part-time/contract",
+            "remote_option": "yes/no/hybrid"
+        }}
+        """
+        
+        try:
+            response = self.client.chat.completions.create(
+                model="gpt-3.5-turbo",
+                messages=[
+                    {"role": "system", "content": "You are an expert job description analyzer. Return only valid JSON."},
+                    {"role": "user", "content": prompt}
+                ],
+                temperature=0.1
+            )
+            
+            result = response.choices[0].message.content
+            return json.loads(result)
+        except Exception as e:
+            print(f"Error analyzing job description: {e}")
+            return {}
+
+    async def calculate_match_score(
+        self, resume_data: Dict[str, Any], job_data: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Calculate match score between resume and job description"""
+        prompt = f"""
+        Calculate a match score between this resume and job description:
+        
+        RESUME DATA:
+        {json.dumps(resume_data, indent=2)}
+        
+        JOB DATA:
+        {json.dumps(job_data, indent=2)}
+        
+        Please return a JSON object with the following structure:
+        {{
+            "overall_score": number (0-100),
+            "skill_match_score": number (0-100),
+            "experience_match_score": number (0-100),
+            "education_match_score": number (0-100),
+            "missing_skills": [
+                {{
+                    "skill": "string",
+                    "importance": "required/preferred",
+                    "suggestion": "string"
+                }}
+            ],
+            "strengths": ["strength1", "strength2", ...],
+            "weaknesses": ["weakness1", "weakness2", ...],
+            "overall_feedback": "detailed feedback string"
+        }}
+        """
+        
+        try:
+            response = self.client.chat.completions.create(
+                model="gpt-3.5-turbo",
+                messages=[
+                    {"role": "system", "content": "You are an expert HR analyst. Provide accurate match scoring."},
+                    {"role": "user", "content": prompt}
+                ],
+                temperature=0.2
+            )
+            
+            result = response.choices[0].message.content
+            return json.loads(result)
+        except Exception as e:
+            print(f"Error calculating match score: {e}")
+            return {"overall_score": 0, "skill_match_score": 0, "experience_match_score": 0, "education_match_score": 0}
+
+    async def generate_resume_suggestions(
+        self, resume_data: Dict[str, Any], job_data: Dict[str, Any], match_analysis: Dict[str, Any]
+    ) -> List[Dict[str, str]]:
+        """Generate suggestions for improving resume based on job requirements"""
+        prompt = f"""
+        Based on this resume and job analysis, provide specific suggestions for improving the resume:
+        
+        RESUME: {json.dumps(resume_data, indent=2)}
+        JOB: {json.dumps(job_data, indent=2)}
+        MATCH ANALYSIS: {json.dumps(match_analysis, indent=2)}
+        
+        Please return a JSON array of suggestions with this structure:
+        [
+            {{
+                "section": "skills/experience/education/summary",
+                "suggestion": "specific improvement suggestion",
+                "priority": "high/medium/low",
+                "impact": "explanation of how this helps"
+            }}
+        ]
+        """
+        
+        try:
+            response = self.client.chat.completions.create(
+                model="gpt-3.5-turbo",
+                messages=[
+                    {"role": "system", "content": "You are an expert resume coach. Provide actionable suggestions."},
+                    {"role": "user", "content": prompt}
+                ],
+                temperature=0.3
+            )
+            
+            result = response.choices[0].message.content
+            return json.loads(result)
+        except Exception as e:
+            print(f"Error generating resume suggestions: {e}")
+            return []
+
+    async def generate_cover_letter(
+        self, resume_data: Dict[str, Any], job_data: Dict[str, Any], user_name: str
+    ) -> str:
+        """Generate a personalized cover letter"""
+        prompt = f"""
+        Generate a professional cover letter for {user_name} based on their resume and the job description:
+        
+        RESUME: {json.dumps(resume_data, indent=2)}
+        JOB: {json.dumps(job_data, indent=2)}
+        
+        The cover letter should:
+        - Be professional and engaging
+        - Highlight relevant skills and experiences
+        - Show enthusiasm for the role
+        - Be 3-4 paragraphs long
+        - Include a proper greeting and closing
+        """
+        
+        try:
+            response = self.client.chat.completions.create(
+                model="gpt-3.5-turbo",
+                messages=[
+                    {"role": "system", "content": "You are an expert cover letter writer. Write compelling, professional cover letters."},
+                    {"role": "user", "content": prompt}
+                ],
+                temperature=0.4
+            )
+            
+            return response.choices[0].message.content
+        except Exception as e:
+            print(f"Error generating cover letter: {e}")
+            return "Unable to generate cover letter at this time."

app/services/file_service.py

@@ -0,0 +1,63 @@
+import os
+import uuid
+from pathlib import Path
+from fastapi import UploadFile, HTTPException
+from app.core.config import settings
+
+
+class FileService:
+    def __init__(self):
+        self.upload_dir = Path("/app/storage/uploads")
+        self.upload_dir.mkdir(parents=True, exist_ok=True)
+
+    def validate_file(self, file: UploadFile) -> bool:
+        """Validate uploaded file"""
+        # Check file size
+        if file.size and file.size > settings.MAX_FILE_SIZE:
+            raise HTTPException(
+                status_code=413,
+                detail=f"File too large. Maximum size is {settings.MAX_FILE_SIZE / (1024*1024)}MB"
+            )
+        
+        # Check file extension
+        if file.filename:
+            file_extension = Path(file.filename).suffix.lower()
+            if file_extension not in settings.ALLOWED_FILE_EXTENSIONS:
+                raise HTTPException(
+                    status_code=400,
+                    detail=f"File type not allowed. Allowed types: {', '.join(settings.ALLOWED_FILE_EXTENSIONS)}"
+                )
+        
+        return True
+
+    async def save_file(self, file: UploadFile, user_id: int) -> tuple[str, str]:
+        """Save uploaded file and return file path and filename"""
+        self.validate_file(file)
+        
+        # Generate unique filename
+        file_extension = Path(file.filename).suffix.lower() if file.filename else ""
+        unique_filename = f"{user_id}_{uuid.uuid4().hex}{file_extension}"
+        file_path = self.upload_dir / unique_filename
+        
+        # Save file
+        try:
+            content = await file.read()
+            with open(file_path, "wb") as f:
+                f.write(content)
+            
+            return str(file_path), file.filename or unique_filename
+        except Exception as e:
+            raise HTTPException(
+                status_code=500,
+                detail=f"Failed to save file: {str(e)}"
+            )
+
+    def delete_file(self, file_path: str) -> bool:
+        """Delete file from storage"""
+        try:
+            if os.path.exists(file_path):
+                os.remove(file_path)
+                return True
+            return False
+        except Exception:
+            return False

app/services/resume_parser.py

@@ -0,0 +1,58 @@
+import PyPDF2
+import docx
+from typing import Optional
+from pathlib import Path
+
+
+class ResumeParser:
+    @staticmethod
+    def extract_text_from_pdf(file_path: str) -> Optional[str]:
+        """Extract text from PDF file"""
+        try:
+            with open(file_path, 'rb') as file:
+                pdf_reader = PyPDF2.PdfReader(file)
+                text = ""
+                for page in pdf_reader.pages:
+                    text += page.extract_text() + "\n"
+                return text.strip()
+        except Exception as e:
+            print(f"Error extracting PDF text: {e}")
+            return None
+
+    @staticmethod
+    def extract_text_from_docx(file_path: str) -> Optional[str]:
+        """Extract text from DOCX file"""
+        try:
+            doc = docx.Document(file_path)
+            text = ""
+            for paragraph in doc.paragraphs:
+                text += paragraph.text + "\n"
+            return text.strip()
+        except Exception as e:
+            print(f"Error extracting DOCX text: {e}")
+            return None
+
+    @staticmethod
+    def extract_text_from_txt(file_path: str) -> Optional[str]:
+        """Extract text from TXT file"""
+        try:
+            with open(file_path, 'r', encoding='utf-8') as file:
+                return file.read().strip()
+        except Exception as e:
+            print(f"Error extracting TXT text: {e}")
+            return None
+
+    @classmethod
+    def extract_text(cls, file_path: str) -> Optional[str]:
+        """Extract text from file based on extension"""
+        file_extension = Path(file_path).suffix.lower()
+        
+        if file_extension == '.pdf':
+            return cls.extract_text_from_pdf(file_path)
+        elif file_extension == '.docx':
+            return cls.extract_text_from_docx(file_path)
+        elif file_extension == '.txt':
+            return cls.extract_text_from_txt(file_path)
+        else:
+            print(f"Unsupported file format: {file_extension}")
+            return None

main.py

@@ -0,0 +1,50 @@
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from app.core.config import settings
+from app.api.v1.router import api_router
+from app.db.session import engine
+from app.db.base import Base
+
+# Create database tables
+Base.metadata.create_all(bind=engine)
+
+app = FastAPI(
+    title=settings.APP_NAME,
+    version=settings.APP_VERSION,
+    description="AI-Powered Resume & Job Match Hub - Helping job seekers find the perfect match",
+    openapi_url="/openapi.json"
+)
+
+# Configure CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=settings.CORS_ORIGINS,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Include API routes
+app.include_router(api_router, prefix="/api/v1")
+
+
+@app.get("/")
+async def root():
+    """Root endpoint providing service information"""
+    return {
+        "service": settings.APP_NAME,
+        "version": settings.APP_VERSION,
+        "description": "AI-Powered Resume & Job Match Hub",
+        "documentation": "/docs",
+        "health_check": "/health"
+    }
+
+
+@app.get("/health")
+async def health_check():
+    """Health check endpoint"""
+    return {
+        "status": "healthy",
+        "service": settings.APP_NAME,
+        "version": settings.APP_VERSION
+    }

requirements.txt

@@ -0,0 +1,18 @@
+fastapi==0.104.1
+uvicorn[standard]==0.24.0
+sqlalchemy==2.0.23
+alembic==1.12.1
+pydantic==2.5.0
+pydantic-settings==2.1.0
+python-multipart==0.0.6
+python-jose[cryptography]==3.3.0
+passlib[bcrypt]==1.7.4
+httpx==0.25.2
+openai==1.3.5
+langchain==0.0.350
+langchain-openai==0.0.2
+PyPDF2==3.0.1
+python-docx==1.1.0
+ruff==0.1.6
+pytest==7.4.3
+pytest-asyncio==0.21.1