app.py

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

#SkillExtraction Packages
import PyPDF2
from PyPDF2 import PdfReader
import psycopg2
from psycopg2 import sql
import pandas as pd
from datetime import date
import numpy as np
import spacy
import re
import docx2txt 
from sentence_transformers import SentenceTransformer, util
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import io  
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')

from io import BytesIO
import requests
#Custom Classes for endpoints
from DbConnection import DbConnection
from UploadFile import UploadOpenFile
from SkillExtract import SkillExtractorDetails
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 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"}
#https://vaibhav84-resumeapi.hf.space/docs
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"):
    # Save file locally for processing
    text =''
    print(text)
    if FileName.endswith("pdf"):
        reserve_pdf_on_memory = io.BytesIO(file)
        load_pdf = PyPDF2.PdfReader(reserve_pdf_on_memory)
        for page in load_pdf.pages:
            text += page.extract_text()
       
    elif FileName.endswith("doc") or FileName.endswith("docx"):
        text = docx2txt.process(file)
        text = text.read()
    
    else:
        text = file.decode('utf-8')

    returnID = UploadOpenFile.uploadFile(text,FileName,db_params)
    returnSkills = SkillExtractorDetails.SkillExtract(db_params,skill_extractor,returnID)     
    details = returnSkills.split('@')
    responseData = 'Required Skills : ' + details[0] +'<br><br>'
    responseData = responseData + 'Soft Skills : ' + details[1] +'<br><br>'
    responseData = responseData + 'Good to have Skills : ' + details[2]
    return {"SkillDetails": responseData}  
@app.get("/ProfileMatch") 
def ProfileMatchResults():
   dbQuery = "select * from profilematch"
   conn = psycopg2.connect(**db_params)   
   df = pd.read_sql_query(dbQuery, conn)
   return parse_csv(df) 

@app.post("/UploadFile/")
def UploadFileDetails(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("/ExtractSkills/")
def ExtractSkills(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