added_main.py

main.py

@@ -0,0 +1,154 @@
+"""
+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
+    )