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
from sentence_transformers import SentenceTransformer
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
from SkillMatcher import SkillMatch
from OpenAIResponse import OpenAIText
from SkillExtractV1 import SkillExtractorDetailsV1
import ClassModals
import os
os.environ['HF_HOME'] = '/hug/cache/'

app = FastAPI()
         
nlp = spacy.load("en_core_web_lg")
    # init skill extractor
skill_extractor = SkillExtractor(nlp, SKILL_DB, PhraseMatcher)
model = SentenceTransformer('all-MiniLM-L6-v2')

@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,True)
    returnSkills = SkillExtractorDetails.SkillExtract(db_params,skill_extractor,returnID,True)     
    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.post("/UploadJobDescriptionV1/")
def UploadJobDescriptionV1(file: bytes =  File(...), FileName: str = "sample.pdf"):   
    text= ExtractContentFromFile.ExtractDataFromFile(FileName,file)
    #returnID = UploadOpenFile.uploadFile(text,FileName,db_params,True)
    returnSkills = SkillExtractorDetailsV1.GetSkillData(skill_extractor,text,db_params)     
    #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(returnSkills) 

@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.get("/UploadJobDescriptionOpenText/")
def UploadOpenText(text : str, filename : str):   
   
   returnID = UploadOpenFile.uploadFile(text,filename,db_params,True)
   returnSkills = SkillExtractorDetails.SkillExtract(db_params,skill_extractor,returnID,True)
   data = SkillMatch.SkillMatcher(model,db_params,returnID).split(';')
   dataJson = {'Data':['Best Resume Fit', 'Score', 'ProfileID'], 'Values':[data[0], data[1], data[2]]}
   df = pd.DataFrame(dataJson)
   return parse_csv(df) 


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

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


@app.post("/UploadProfileFromFile/")
def UploadProfileFromFile(file: bytes =  File(...), FileName: str = "sample.pdf"):    
    text= ExtractContentFromFile.ExtractDataFromFile(FileName,file)
    returnID = UploadOpenFile.uploadFile(text,FileName,db_params,False) 
    returnSkills = SkillExtractorDetails.SkillExtract(db_params,skill_extractor,returnID,False)     
    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) 

class FileText(BaseModel):
    text: int 
    fname: str
    
@app.get("/UploadProfileOpenText/")
def UploadProfileOpenText(text : str, filename : str):    

    #text= ExtractContentFromFile.ExtractDataFromFile(FileName,file)
    returnID = UploadOpenFile.uploadFile(text,filename,db_params,False) 
    returnSkills = SkillExtractorDetails.SkillExtract(db_params,skill_extractor,returnID,False)     
    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("/GetMatchProfileByJobId/")
def GetMatchProfileByJobId(JobId : int):    
    data = SkillMatch.SkillMatcher(model,db_params,JobId).split(';')
    dataJson = {'Data':['Best Resume Fit', 'Score', 'ProfileID'], 'Values':[data[0], data[1], data[2]]}
    df = pd.DataFrame(dataJson)
    return parse_csv(df) 


@app.get("/GetOpenAPIResponse/")
def GetOpenAPIResponse(query:str, content:str):
    return OpenAIText.OpenAITextResponse(query,content)
    
#return JSONResponse(content={"message": "Here's your interdimensional portal." , "mes1":"data2"})
#https://vaibhav84-resumeapi.hf.space/docs