app.py

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

#SkillExtraction Packages
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
from sentence_transformers import SentenceTransformer, util
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from io import StringIO 
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
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 FileResponse(BaseModel):
    fileid: int
    message: 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.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

@app.post("/uploadJobDescription/")
def create_upload_file(file: bytes = File(...)):
    content = file.decode('utf-8')
    lines = content.split('\n')
    return {"content": lines}

@app.post("/uploadJobDescriptionPDF/")
def upload_PDF(file: UploadFile = File(...)):
    try:
        contents = file.file.read()
        with open(file.filename, 'wb') as f:
            f.write(contents)
    except Exception:
        return {"message": "There was an error uploading the file"}
    finally:
        file.file.close()

    return {"message": f"Successfully uploaded {contents}"}

@app.post("/uploadJobDescriptionPDF2/")
def upload_PDF2(file: UploadFile = File(...)):
    text =''
    
    if file.filename.endswith("pdf"):
        pdf_reader = PdfReader(file)
            
        for page in pdf_reader.pages:
            text += page.extract_text()
        
   

    return {"message": f"Successfully uploaded {text}"}