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| import re | |
| from datetime import datetime | |
| import psycopg2 | |
| import pandas as pd | |
| class SkillExtractorDetailsV1: | |
| def GetSkillData(skill_extractor, inputData, db_params): | |
| getdbskills = SkillExtractorDetailsV1.GetSkillDatafromDB(db_params) | |
| skills_list = [] | |
| annotations = skill_extractor.annotate(inputData) | |
| matches = annotations['results']['full_matches']+annotations['results']['ngram_scored'] | |
| skill_Typelst = [] | |
| skill_Scorelst = [] | |
| reuiredSkilllst = [] | |
| softskillslst = [] | |
| goodtohavelst = [] | |
| for result in matches: | |
| skill_id = result['skill_id'] | |
| skill_name1 = skill_extractor.skills_db[skill_id]['skill_name'] | |
| skill_name = skill_name1.split("(")[0].strip() | |
| skill_type = skill_extractor.skills_db[skill_id]['skill_type'] | |
| skill_score = round(result['score'],2) | |
| result = getdbskills[getdbskills['skill'] == skill_name.upper()] | |
| if(result.empty): | |
| if( skill_name in skills_list): | |
| continue | |
| if(skill_score > 99 and skill_type != 'Soft Skill'): | |
| reuiredSkilllst.append(skill_name) | |
| elif(skill_score > 50 and skill_type == 'Soft Skill'): | |
| softskillslst.append(skill_name) | |
| elif(skill_score <= 50 and skill_type == 'Soft Skill'): | |
| goodtohavelst.append(skill_name) | |
| elif(skill_score <= 99 and skill_type != 'Soft Skill'): | |
| goodtohavelst.append(skill_name) | |
| # skills_list.append(skill_name) | |
| # skill_Typelst.append(skill_type) | |
| # skill_Scorelst.append(skill_score) | |
| else: | |
| print('----'+ result) | |
| # list_of_tuples = list(zip(skills_list, skill_Typelst,skill_Scorelst)) | |
| # df = pd.DataFrame(list_of_tuples, | |
| # columns=['Skill_Name', 'Skill_Type','Skill_Score']) | |
| data = {'Data':['Required Skills', 'Soft Skills', 'Good to have Skills'], 'Values':[reuiredSkilllst, softskillslst, goodtohavelst]} | |
| df = pd.DataFrame(data) | |
| return df | |
| def GetSkillDatafromDB(db_params): | |
| conn = psycopg2.connect(**db_params) | |
| query = "select upper(skilldetails) skill from skillmaster where weightage = 0" | |
| df = pd.read_sql_query(query, conn) | |
| return df | |