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| from fastapi import FastAPI, HTTPException, UploadFile, File | |
| import pandas as pd | |
| from resume_extractor import ResumeExtractor | |
| from job_description_extractor import JobDescriptionExtractor | |
| from model_trainer import ModelTrainer | |
| from comparison_utils import ( | |
| compare_with_chatgpt_job_title, | |
| compare_with_chatgpt_education, | |
| compare_with_chatgpt_location, | |
| compare_age_range_with_description | |
| ) | |
| from synthetic_data import create_synthetic_data | |
| app = FastAPI() | |
| def main(resume_text, job_description): | |
| openai_api_key = 'sk-proj-bC6H6QrP6DUqHkn5vOkYT3BlbkFJsSyvL4Bc9c3UEbHrsPMj' | |
| ner_model_name_or_path = "NLPclass/Named-entity-recognition" | |
| skill_model_name_or_path = "GalalEwida/lm-ner-skills-recognition" | |
| resume_extractor = ResumeExtractor(ner_model_name_or_path, openai_api_key) | |
| job_description_extractor = JobDescriptionExtractor(openai_api_key) | |
| full_name, loc, age, skills, education_resume, title_job_resume = resume_extractor.extract_resume_info(resume_text, skill_model_name_or_path) | |
| job_skills, education_job, title_job, location, age_DS = job_description_extractor.extract_job_info(job_description, skill_model_name_or_path) | |
| education_match = compare_with_chatgpt_education(education_resume, education_job, openai_api_key) | |
| title_job_match = compare_with_chatgpt_job_title(title_job_resume, title_job, openai_api_key) | |
| title_loc_match = compare_with_chatgpt_location(loc, location, openai_api_key) | |
| title_age_match = compare_age_range_with_description(age, age_DS, openai_api_key) | |
| synthetic_data = create_synthetic_data(job_skills, education_job, title_job, location, age_DS) | |
| synthetic_data.to_csv('synthetic_data.csv') | |
| model_trainer = ModelTrainer(synthetic_data) | |
| best_model = model_trainer.train_models() | |
| input_data = {skill: 1 if skill in skills else 0 for skill in job_skills} | |
| input_data[education_job] = education_match | |
| input_data[title_job] = title_job_match | |
| input_data[location] = title_loc_match | |
| input_data[age_DS] = title_age_match | |
| input_df = pd.DataFrame([input_data]) | |
| input_df.to_csv('input_df.csv') | |
| predicted_target = best_model.predict(input_df) | |
| return { | |
| "full_name": full_name, | |
| "location": loc, | |
| "age": age, | |
| "age_DS": age_DS, | |
| "skills": skills, | |
| "education_resume": education_resume, | |
| "title_job_resume": title_job_resume, | |
| "job_skills": job_skills, | |
| "education_job": education_job, | |
| "title_job": title_job, | |
| "location_job": location, | |
| "predicted_target": predicted_target[0] | |
| } | |
| async def extract(resume_file: UploadFile = File(...), job_description_file: UploadFile = File(...)): | |
| try: | |
| resume_text = await resume_file.read() | |
| job_description = await job_description_file.read() | |
| # Convert bytes to string | |
| resume_text = resume_text.decode('utf-8') | |
| job_description = job_description.decode('utf-8') | |
| output = main(resume_text, job_description) | |
| return output | |
| except Exception as e: | |
| raise HTTPException(status_code=500, detail=str(e)) |