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"""
FastAPI routes for resume review functionality
"""
from fastapi import APIRouter, UploadFile, File, Form, HTTPException
from fastapi.responses import JSONResponse
from app.embedding import CorrectedResumeJobMatcher
from app.supabase import supabase_service
from app.config import settings
import logging
import os
logger = logging.getLogger(__name__)
router = APIRouter()
@router.post("/match", response_model=dict)
async def match_resume_job(
file: UploadFile = File(..., description="PDF resume file"),
job_description: str = Form(..., description="Job description text", min_length=50)
):
"""
Enhanced resume-job matching using the CorrectedResumeJobMatcher
Args:
file: PDF file upload
job_description: Job description as form data
Returns:
dict: Comprehensive matching results with detailed analysis
"""
try:
# Validate file type
if file.content_type not in settings.ALLOWED_FILE_TYPES:
raise HTTPException(
status_code=400,
detail=f"Invalid file type. Only PDF files are allowed."
)
# Check file size
file_content = await file.read()
if len(file_content) > settings.MAX_FILE_SIZE:
raise HTTPException(
status_code=400,
detail=f"File size exceeds maximum allowed size of {settings.MAX_FILE_SIZE/1024/1024}MB"
)
# Initialize the enhanced matcher
groq_api_key = os.getenv("GROQ_API_KEY")
cohere_api_key = os.getenv("COHERE_API_KEY")
matcher = CorrectedResumeJobMatcher(
groq_api_key=groq_api_key,
cohere_api_key=cohere_api_key,
resume_bert_model=None # Auto-select best model
)
# Perform the matching analysis
result = matcher.match(file_content, job_description)
# Print detailed analysis to terminal
print("\n" + "="*80)
print("🔍 API REQUEST ANALYSIS RESULTS")
print("="*80)
final_score = result["final_similarity_score"]
final_percentage = result["final_similarity_percentage"]
category = result["similarity_category"]
print(f"\n🎯 FINAL MATCH SCORE: {final_score:.4f} ({final_percentage:.2f}%)")
print(f"📊 CATEGORY: {category}")
print(f"🔍 CONFIDENCE: {result['confidence']:.3f}")
print(f"⚠️ ANOMALY: {result['anomaly']}")
# Component scores
print(f"\n📊 COMPONENT SCORES:")
print(f" • Semantic Similarity: {result['semantic_score']:.3f} ({result['semantic_score']*100:.1f}%)")
print(f" • Skills Matching: {result['skills_score']:.3f} ({result['skills_score']*100:.1f}%)")
print(f" • Enhanced Skills: {result['enhanced_skills_score']:.3f} ({result['enhanced_skills_score']*100:.1f}%)")
print(f" • Resume-BERT: {result['resume_bert_score']:.3f} ({result['resume_bert_score']*100:.1f}%)")
print(f" • LLM Assessment: {result['llm_score']:.1f}/100")
# LLM Details
if result.get('llm_details'):
llm_details = result['llm_details']
print(f"\n🧠 LLM ANALYSIS:")
print(f" • API Used: {llm_details.get('api_used', 'N/A')}")
print(f" • Response Time: {llm_details.get('response_time', 0):.2f}s")
print(f" • Compatibility: {llm_details.get('compatibility_score', 0)}/100")
if llm_details.get('strengths'):
print(f" • Key Strengths: {len(llm_details['strengths'])} identified")
if llm_details.get('gaps'):
print(f" • Areas for Improvement: {len(llm_details['gaps'])} identified")
if llm_details.get('recommendations'):
print(f" • Recommendations: {len(llm_details['recommendations'])} provided")
# Skills Analysis
if result.get('skills_analysis'):
skills_analysis = result['skills_analysis']
print(f"\n🔧 SKILLS ANALYSIS:")
print(f" • Coverage: {skills_analysis['coverage_percentage']*100:.1f}%")
print(f" • Direct Matches: {skills_analysis['direct_match_count']}/{skills_analysis['total_job_skills']}")
print(f" • Missing Skills: {len(skills_analysis['missing_skills'])}")
print(f" • Critical Skills Missing: {len(skills_analysis['critical_skills_missing'])}")
# Model Info
if result.get('model_info'):
model_info = result['model_info']
print(f"\n🤖 MODEL INFO:")
print(f" • Primary Model: {model_info.get('primary_semantic_model', 'N/A')}")
print(f" • Resume Model: {model_info.get('resume_specific_model', 'N/A')}")
print(f" • Total Models: {model_info.get('total_models_loaded', 0)}")
print("\n" + "="*80)
# Store results in database if Supabase is configured
try:
# Extract resume text for storage
resume_text = matcher.pdf_extractor.extract_text(file_content)
# Create a summary for storage
feedback = f"Match Score: {result['final_similarity_percentage']:.1f}% - {result['similarity_category']}"
# Store in database
await supabase_service.insert_resume_review(
resume_text=resume_text,
job_description=job_description,
match_score=result['final_similarity_percentage'],
feedback=feedback
)
except Exception as e:
logger.warning(f"Failed to store results in database: {str(e)}")
# Continue without failing the request
return result
except Exception as e:
logger.error(f"Resume matching failed: {str(e)}")
raise HTTPException(
status_code=500,
detail=f"Failed to process resume matching: {str(e)}"
)
@router.get("/reviews")
async def get_recent_reviews():
"""
Get recent resume reviews
Returns:
list: Recent resume reviews
"""
try:
reviews = await supabase_service.get_resume_reviews(limit=10)
return {"reviews": reviews}
except Exception as e:
logger.error(f"Error retrieving reviews: {str(e)}")
raise HTTPException(
status_code=500,
detail="Failed to retrieve reviews"
)
@router.get("/health")
async def health_check():
"""
Health check for the review service
Returns:
dict: Health status
"""
return {
"status": "healthy",
"embedding_model": settings.EMBEDDING_MODEL_NAME,
"llm_model": settings.LLM_MODEL_NAME,
"supabase_connected": supabase_service.client is not None
} |