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from fastapi import FastAPI,File, UploadFile |
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from pydantic import BaseModel |
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import json |
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from typing_extensions import Annotated |
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from fastapi.encoders import jsonable_encoder |
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from fastapi.responses import JSONResponse |
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import PyPDF2 |
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from PyPDF2 import PdfReader |
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import psycopg2 |
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from psycopg2 import sql |
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import pandas as pd |
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from datetime import date |
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import numpy as np |
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import spacy |
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import re |
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import docx2txt |
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from sentence_transformers import SentenceTransformer, util |
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from sklearn.feature_extraction.text import CountVectorizer |
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from sklearn.metrics.pairwise import cosine_similarity |
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import io |
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from spacy.matcher import PhraseMatcher |
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from skillNer.general_params import SKILL_DB |
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from skillNer.skill_extractor_class import SkillExtractor |
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from psycopg2.extensions import register_adapter, AsIs |
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register_adapter(np.int64, AsIs) |
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import warnings |
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warnings.filterwarnings('ignore') |
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from io import BytesIO |
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import requests |
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from DbConnection import DbConnection |
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from UploadFile import UploadOpenFile |
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from SkillExtract import SkillExtractorDetails |
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from ExtractContentsFromFile import ExtractContentFromFile |
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import os |
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os.environ['HF_HOME'] = '/hug/cache/' |
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app = FastAPI() |
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class FileDetails(BaseModel): |
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filecontents: str |
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filename: str |
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fileid: str |
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message: str |
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class SkillDetails(BaseModel): |
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skillid: int |
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requiredSkills: str |
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softSkills: str |
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goodToHaveSkills: str |
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class SkillData(BaseModel): |
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filename: str |
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nlp = spacy.load("en_core_web_lg") |
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skill_extractor = SkillExtractor(nlp, SKILL_DB, PhraseMatcher) |
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@app.get("/") |
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async def root(): |
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return {"SkillAPI":"SkillAPi Version 0.05"} |
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db_params = DbConnection.GetDbConnection() |
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def parse_csv(df): |
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res = df.to_json(orient="records") |
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parsed = json.loads(res) |
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return parsed |
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@app.post("/uploadJobDescription/") |
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def uploadJobDescription(file: bytes = File(...), FileName: str = "sample.pdf"): |
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text= ExtractContentFromFile.ExtractDataFromFile(FileName,file) |
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returnID = UploadOpenFile.uploadFile(text,FileName,db_params) |
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returnSkills = SkillExtractorDetails.SkillExtract(db_params,skill_extractor,returnID) |
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details = returnSkills.split('@') |
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data = {'Data':['Required Skills', 'Soft Skills', 'Good to have Skills'], 'Values':[details[0], details[1], details[2]]} |
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df = pd.DataFrame(data) |
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return parse_csv(df) |
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@app.get("/AllProfileMatchResults") |
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def AllProfileMatchResults(): |
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dbQuery = "select * from profilematch" |
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conn = psycopg2.connect(**db_params) |
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df = pd.read_sql_query(dbQuery, conn) |
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return parse_csv(df) |
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@app.post("/UploadOpenText/") |
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def UploadOpenText(file_data: FileDetails): |
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returnID = UploadOpenFile.uploadFile(file_data.filecontents,file_data.filename,db_params) |
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file_data.filecontents = "" |
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file_data.fileid = str(returnID) |
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file_data.message = "File Uploaded Successfully!" |
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return file_data |
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@app.post("/ExtractSkillsByJobID/") |
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def ExtractSkillsByJobID(skill_data: SkillDetails): |
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returnSkills = SkillExtractorDetails.SkillExtract(db_params,skill_extractor,skill_data.skillid) |
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details = returnSkills.split('@') |
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skill_data.requiredSkills = details[0] |
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skill_data.softSkills = details[1] |
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skill_data.goodToHaveSkills = details[1] |
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return skill_data |
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@app.post("/RemoveSkillsByName/") |
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def RemoveSkills(SkillName : str): |
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conn = psycopg2.connect(**db_params) |
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cursor = conn.cursor() |
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SkillName = data.split(':')[1] |
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print("Removing Skills " + SkillName) |
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query = "update skillmaster set weightage = 0 where skilldetails = (%s)" |
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params = (SkillName,) |
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cursor.execute(query, params) |
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conn.commit() |
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cursor.close() |
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conn.close() |
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