main.py

from fastapi import FastAPI, Request, Form, File, UploadFile
from fastapi.templating import Jinja2Templates
from fastapi.staticfiles import StaticFiles
from fastapi.responses import HTMLResponse
import pandas as pd
import requests
from bs4 import BeautifulSoup
from cleantext import clean
from docx import Document
import numpy as np
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
import utils
from utils import coSkillEmbed, cosine, clean_my_text

app = FastAPI()
app.mount("/static", StaticFiles(directory='static'), name="static")
templates = Jinja2Templates(directory="templates/")

onet = pd.read_csv('static/ONET_JobTitles.csv')
simdat = pd.read_csv('static/cohere_embeddings.csv')

model = AutoModelForSequenceClassification.from_pretrained('static/model_shards', low_cpu_mem_usage=True)
tokenizer = AutoTokenizer.from_pretrained('static/tokenizer_shards', low_cpu_mem_usage=True)

### job information center ###
# get
@app.get("/")
def render_job_list(request: Request):
    joblist = onet['JobTitle']
    return templates.TemplateResponse('job_list.html', context={'request': request, 'joblist': joblist})

# post
@app.post("/")
def render_job_info(request: Request, jobtitle: str = Form(enum=[x for x in onet['JobTitle']])):
    
    def remove_new_line(value):
        return ''.join(value.splitlines())

    joblist = onet['JobTitle']

    if jobtitle: 
        onetCode = onet.loc[onet['JobTitle'] == jobtitle, 'onetCode']
        onetCode = onetCode.reindex().tolist()[0]
        headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.1.2 Safari/605.1.15'}
        url = "https://www.onetonline.org/link/summary/" + onetCode
        response = requests.get(url, headers=headers)
        soup = BeautifulSoup(response.text, 'html.parser')
        jobdescription = soup.p.get_text()
                
        url = "https://www.onetonline.org/link/result/" + onetCode + "?c=tk&n_tk=0&s_tk=IM&c_tk=0"
        response = requests.get(url, headers=headers)
        soup = BeautifulSoup(response.text, 'html.parser')
        tasks = str(soup.get_text('reportsubdesc')).replace("reportsubdesc", " ").replace("ImportanceCategoryTask ", "")
        tasks = clean(tasks)
        tasks = tasks.split('show all show top 10')[1]
        tasks = tasks.split('occupations related to multiple tasks')[0]
        tasks = remove_new_line(tasks).replace("related occupations", " ").replace("core", " - ").replace(" )importance category task", "").replace(" find ", "")
        tasks = tasks.split(". ")
        tasks = [''.join(map(lambda c: '' if c in '0123456789-' else c, task)) for task in tasks]
        return templates.TemplateResponse('job_list.html', context={
            'request': request, 
            'joblist': joblist, 
            'jobtitle': jobtitle, 
            'jobdescription': jobdescription, 
            'tasks': tasks})

### job neighborhoods ###
@app.get("/explore-job-neighborhoods/", response_class=HTMLResponse)
async def render_job_neighborhoods(request: Request):
    return templates.TemplateResponse('job_neighborhoods.html', context={'request': request})

### find my match ###
# get
@app.get("/find-my-match", response_class=HTMLResponse)
async def match_page(request: Request):
    return templates.TemplateResponse('find_my_match.html', context={'request': request})

# post
@app.post('/find-my-match', response_class=HTMLResponse)
def get_resume(request: Request, resume: UploadFile = File(...)):

    classifier = pipeline('text-classification', model = model, tokenizer = tokenizer)

    path = f"static/{resume.filename}"
    with open(path, 'wb') as buffer:
        buffer.write(resume.file.read())
    file = Document(path)
    text = []
    for para in file.paragraphs:
        text.append(para.text)
    resume = "\n".join(text)

    embeds = coSkillEmbed(resume)
    simResults = []

    for i in range(len(simdat)):
        simResults.append(cosine(np.array(embeds), np.array(simdat.iloc[i,1:])))
    simResults = pd.DataFrame(simResults)
    simResults['JobTitle'] = simdat['Title']

    simResults = simResults.iloc[:,[1,0]]
    simResults.columns = ['JobTitle', 'Similarity']
    simResults = simResults.sort_values(by = "Similarity", ascending = False)
    simResults = simResults.iloc[:13,:]
    simResults = simResults.iloc[1:,:]
    simResults.reset_index(drop=True, inplace=True)
    for x in range(len(simResults)):
        simResults.iloc[x,1] = "{:0.2f}".format(simResults.iloc[x,1])
    
    cleantext = clean_my_text(resume)
    labels = []
    for i in range(len(cleantext)):
        classification = classifier(cleantext[i])[0]['label']
        if classification == 'LABEL_1':
            labels.append("Skill")
        else:
            labels.append("Not Skill")
        skills = dict(zip(cleantext, labels))

    return templates.TemplateResponse('find_my_match.html', context={'request': request, 'resume': resume, 'skills': skills, 'simResults': simResults})