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Runtime error
| from fastapi import APIRouter, UploadFile, File, Form, HTTPException, Header, Depends, BackgroundTasks | |
| from sqlalchemy.orm import Session | |
| from ...services import document_parser, scoring_engine, notification_service | |
| from ...services import vector_store as vs | |
| from ...schemas.models import AnalysisResponse, AnalysisResultCreate | |
| from ...database import crud | |
| from ...database.database import get_db | |
| import uuid | |
| import logging | |
| import asyncio | |
| router = APIRouter() | |
| # def perform_analysis_and_save( | |
| # db: Session, | |
| # jd_text: str, | |
| # resume_bytes: bytes, | |
| # resume_filename: str, | |
| # api_key: str, | |
| # llm_model: str, | |
| # jd_id: int | |
| # ): | |
| # try: | |
| # resume_text = document_parser.parse_document(resume_filename, resume_bytes) | |
| # if not resume_text.strip(): | |
| # logging.error(f"Could not extract text from resume: {resume_filename}") | |
| # return | |
| # hard_score = scoring_engine.hard_match_score(resume_text, jd_text) | |
| # llm_results = scoring_engine.llm_analysis(resume_text, jd_text, api_key, llm_model) | |
| # final_score, verdict = scoring_engine.calculate_final_score_and_verdict( | |
| # hard_score, llm_results["semantic_score"] | |
| # ) | |
| # analysis_data = AnalysisResultCreate( | |
| # relevance_score=final_score, | |
| # verdict=verdict, | |
| # missing_elements=llm_results["missing_elements"], | |
| # improvement_suggestions=llm_results["improvement_suggestions"], | |
| # filename=resume_filename | |
| # ) | |
| # crud.create_analysis_result(db=db, result=analysis_data, jd_id=jd_id) | |
| # doc_id = str(uuid.uuid4()) | |
| # metadata = {"filename": resume_filename, "score": final_score, "verdict": verdict, "jd_id": jd_id} | |
| # vs.vector_store.add_document(doc_id, resume_text, metadata) | |
| # # Send feedback webhook | |
| # student_email = scoring_engine.extract_email_from_text(resume_text) | |
| # if student_email: | |
| # # notification_service.send_feedback_webhook(student_email, analysis_data.dict()) | |
| # asyncio.run(notification_service.send_feedback_webhook(student_email, analysis_data.model_dump())) | |
| # except Exception as e: | |
| # logging.error(f"Background analysis failed for {resume_filename}: {e}", exc_info=True) | |
| # backend/app/api/endpoints/analysis.py | |
| # ... (imports) | |
| def perform_analysis_and_save( | |
| db: Session, | |
| jd_text: str, | |
| resume_bytes: bytes, | |
| resume_filename: str, | |
| api_key: str, | |
| llm_model: str, | |
| jd_id: int | |
| ): | |
| try: | |
| resume_text = document_parser.parse_document(resume_filename, resume_bytes) | |
| if not resume_text.strip(): | |
| logging.error(f"Could not extract text from resume: {resume_filename}") | |
| return | |
| # --- NEW: Extract and store the student's email --- | |
| student_email = scoring_engine.extract_email_from_text(resume_text) | |
| hard_score = scoring_engine.hard_match_score(resume_text, jd_text) | |
| llm_results = scoring_engine.llm_analysis(resume_text, jd_text, api_key, llm_model) | |
| final_score, verdict = scoring_engine.calculate_final_score_and_verdict( | |
| hard_score, llm_results["semantic_score"] | |
| ) | |
| analysis_data = AnalysisResultCreate( | |
| relevance_score=final_score, | |
| verdict=verdict, | |
| missing_elements=llm_results["missing_elements"], | |
| improvement_suggestions=llm_results["improvement_suggestions"], | |
| filename=resume_filename, | |
| # --- NEW: Add the email to the data to be saved --- | |
| student_email=student_email | |
| ) | |
| crud.create_analysis_result(db=db, result=analysis_data, jd_id=jd_id) | |
| doc_id = str(uuid.uuid4()) | |
| metadata = {"filename": resume_filename, "score": final_score, "verdict": verdict, "jd_id": jd_id} | |
| vs.vector_store.add_document(doc_id, resume_text, metadata) | |
| # --- THIS IS THE LINE TO REMOVE --- | |
| # if student_email: | |
| # asyncio.run(notification_service.send_feedback_webhook(student_email, analysis_data.model_dump())) | |
| # ------------------------------------ | |
| except Exception as e: | |
| logging.error(f"Background analysis failed for {resume_filename}: {e}", exc_info=True) | |
| async def analyze_resume( | |
| background_tasks: BackgroundTasks, | |
| jd_id: int = Form(...), | |
| resume: UploadFile = File(...), | |
| api_key: str = Header(..., alias="X-API-Key"), | |
| llm_model: str = Header(..., alias="X-LLM-Model"), | |
| db: Session = Depends(get_db) | |
| ): | |
| if not api_key: | |
| raise HTTPException(status_code=400, detail="Gemini API Key is required.") | |
| jd_model = crud.get_job_description(db=db, jd_id=jd_id) | |
| if not jd_model: | |
| raise HTTPException(status_code=404, detail="Job Description not found.") | |
| if not resume.filename or not resume.filename.lower().endswith(('.pdf', '.docx')): | |
| raise HTTPException(status_code=400, detail="Invalid file type.") | |
| resume_bytes = await resume.read() | |
| background_tasks.add_task( | |
| perform_analysis_and_save, | |
| db, jd_model.description, resume_bytes, resume.filename, api_key, llm_model, jd_id | |
| ) | |
| return {"message": "Analysis started in the background for " + resume.filename} |