app.py

#Fast APi Packages
from fastapi import FastAPI,File
from pydantic import BaseModel
import json

#SkillExtraction Packages
import psycopg2
import pandas as pd
import numpy as np
import spacy
from sklearn.metrics.pairwise import cosine_similarity
from spacy.matcher import PhraseMatcher
from skillNer.general_params import SKILL_DB
from skillNer.skill_extractor_class import SkillExtractor
from psycopg2.extensions import register_adapter, AsIs
register_adapter(np.int64, AsIs)
import warnings
warnings.filterwarnings('ignore')


#Custom Classes for endpoints
from DbConnection import DbConnection
from UploadFile import UploadOpenFile
from SkillExtract import SkillExtractorDetails
from ExtractContentsFromFile import ExtractContentFromFile
from RemoveSkills import RemoveSkill
from AddSkillDetails import AddSkill
import ClassModals
import os
os.environ['HF_HOME'] = '/hug/cache/'

app = FastAPI()
class FileDetails(BaseModel):
    filecontents: str
    filename: str
    fileid: str
    message: str


class SkillDetails(BaseModel):
    skillid: int 
    requiredSkills: str
    softSkills: str
    goodToHaveSkills: str  

class AddSkillDetails1(BaseModel):    
    SkillName: str = 'SkillName'
    SkillType: str = 'Soft Skill'
    SkillScore: int = 10     

class SkillData(BaseModel):
    filename: str 
         
nlp = spacy.load("en_core_web_lg")
    # init skill extractor
skill_extractor = SkillExtractor(nlp, SKILL_DB, PhraseMatcher)

@app.get("/")
async def root():
 return {"SkillAPI":"SkillAPi Version 0.05"}

db_params = DbConnection.GetDbConnection()
def parse_csv(df):
    res = df.to_json(orient="records")
    parsed = json.loads(res)
    return parsed


@app.post("/UploadJobDescription/")
def uploadJobDescription(file: bytes =  File(...), FileName: str = "sample.pdf"):   
    text= ExtractContentFromFile.ExtractDataFromFile(FileName,file)
    returnID = UploadOpenFile.uploadFile(text,FileName,db_params)
    returnSkills = SkillExtractorDetails.SkillExtract(db_params,skill_extractor,returnID)     
    details = returnSkills.split('@')
    data = {'Data':['Required Skills', 'Soft Skills', 'Good to have Skills'], 'Values':[details[0], details[1], details[2]]}
    df = pd.DataFrame(data)
    return parse_csv(df) 

@app.get("/AllProfileMatchResults") 
def AllProfileMatchResults():
   dbQuery = "select * from profilematch"
   conn = psycopg2.connect(**db_params)   
   df = pd.read_sql_query(dbQuery, conn)
   return parse_csv(df) 

@app.post("/UploadOpenText/")
def UploadOpenText(file_data: FileDetails):   
   
   returnID = UploadOpenFile.uploadFile(file_data.filecontents,file_data.filename,db_params)
   file_data.filecontents = ""
   file_data.fileid = str(returnID)
   file_data.message = "File Uploaded Successfully!"
   
   return file_data


@app.post("/ExtractSkillsByJobID/")
def ExtractSkillsByJobID(skill_data: SkillDetails):
   returnSkills = SkillExtractorDetails.SkillExtract(db_params,skill_extractor,skill_data.skillid)     
   details = returnSkills.split('@')
   skill_data.requiredSkills = details[0]
   skill_data.softSkills = details[1]
   skill_data.goodToHaveSkills = details[1]
   return skill_data

@app.post("/RemoveSkillsByName/")
def RemoveSkills(SkillName : str):    
    RemoveSkill.RemoveSkillDetails(db_params,SkillName)
    return "Skill Removed Successfully"

@app.post("/AddSkillDeails/")
def AddSkills(Skills : ClassModals.ClassModals.AddSkillDetails):    
    skilldetailsStr = Skills.SkillName + ',' + Skills.SkillType + ',' + str(Skills.SkillScore)
    AddSkill.AddSkillDetails(db_params,skilldetailsStr)
    return "Skill Removed Successfully"


#return JSONResponse(content={"message": "Here's your interdimensional portal." , "mes1":"data2"})
#https://vaibhav84-resumeapi.hf.space/docs