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"""
Simple FastAPI Resume-JD Scorer
Accepts PDF resumes and job description, returns scored results
"""
from fastapi import FastAPI, File, UploadFile, Form, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from typing import List
import uvicorn
import os
import tempfile
import shutil
# Import scoring utilities
from utils.scorer import ResumeScorer
from utils.pdf_processor import extract_text_from_pdf
# Initialize FastAPI app
app = FastAPI()
# Enable CORS for frontend
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Global scorer instance
scorer = None
@app.on_event("startup")
async def startup_event():
"""Load ML model on startup"""
global scorer
print("⚙️ Loading InstructorXL model (this may take 2-3 minutes)...")
scorer = ResumeScorer()
print("✅ Model loaded successfully!")
@app.get("/health")
async def health():
"""Health check endpoint"""
return {
"status": "healthy",
"model_loaded": scorer is not None
}
@app.post("/score")
async def score_resumes(
job_description: str = Form(...),
resumes: List[UploadFile] = File(...)
):
"""
Score resumes against job description
Parameters:
- job_description: Text of the job description
- resumes: List of PDF files
Returns:
- Ranked list of resumes with scores
"""
if not scorer:
raise HTTPException(status_code=503, detail="Model not loaded yet")
if not resumes:
raise HTTPException(status_code=400, detail="No resumes provided")
if len(job_description.strip()) < 50:
raise HTTPException(status_code=400, detail="Job description too short")
results = []
temp_dir = tempfile.mkdtemp()
try:
print(f"📄 Processing {len(resumes)} resumes...")
for idx, resume_file in enumerate(resumes, 1):
print(f" [{idx}/{len(resumes)}] Processing: {resume_file.filename}")
# Validate PDF
if not resume_file.filename.lower().endswith('.pdf'):
results.append({
"resume_name": resume_file.filename,
"error": "Only PDF files supported",
"skills_score": 0.0,
"projects_score": 0.0,
"experience_score": 0.0,
"final_score": 0.0
})
continue
try:
# Save and extract PDF
temp_path = os.path.join(temp_dir, resume_file.filename)
content = await resume_file.read()
with open(temp_path, 'wb') as f:
f.write(content)
resume_text = extract_text_from_pdf(temp_path)
if not resume_text or len(resume_text.strip()) < 100:
results.append({
"resume_name": resume_file.filename,
"error": "Could not extract text from PDF",
"skills_score": 0.0,
"projects_score": 0.0,
"experience_score": 0.0,
"final_score": 0.0
})
continue
# Score resume
score_result = scorer.score_resume(job_description, resume_text)
score_result["resume_name"] = resume_file.filename
results.append(score_result)
print(f" ✓ Scored: {resume_file.filename} (final_score: {score_result['final_score']:.3f})")
except Exception as e:
print(f"❌ Error processing {resume_file.filename}: {str(e)}")
results.append({
"resume_name": resume_file.filename,
"error": str(e),
"skills_score": 0.0,
"projects_score": 0.0,
"experience_score": 0.0,
"final_score": 0.0
})
# Sort by final score
results.sort(key=lambda x: x.get("final_score", 0), reverse=True)
print(f"✅ Scoring complete!")
return {
"success": True,
"total": len(resumes),
"processed": len([r for r in results if "error" not in r]),
"results": results
}
finally:
# Cleanup
shutil.rmtree(temp_dir, ignore_errors=True)
if __name__ == "__main__":
uvicorn.run(
"main:app",
host="0.0.0.0",
port=8000,
reload=True
)
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