Spaces:
Runtime error
Runtime error
upgrading text embedding model
Browse files- main.py +6 -5
- match_utils.py +24 -21
- requirements.txt +2 -2
- scrape_onet.py +19 -0
- static/cohere_tSNE_dat.csv +0 -0
- static/embeddings/.DS_Store +0 -0
- static/embeddings/onet_embeddings_neural_chat.csv +0 -3
- static/embeddings/{cohere_embeddings.csv → onet_embeddings_st5.csv} +2 -2
- static/jd_embeddings.csv +2 -2
- static/res_embeddings.csv +2 -3
- static/{neural_chat_tSNE_dat.csv → st5_tSNE_dat.csv} +0 -0
main.py
CHANGED
@@ -17,7 +17,7 @@ from mangum import Mangum
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from localStoragePy import localStoragePy
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localStorage = localStoragePy('pathfinder', 'text')
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-
from scrape_onet import get_onet_code, get_onet_description, get_onet_tasks, get_onet_activities,
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from match_utils import neighborhoods, get_resume, skill_extractor, sim_result_loop, get_links, skillEmbed, sim_result_loop_jobFinder, sim_result_loop_candFinder
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from user_utils import Hash
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@@ -29,8 +29,8 @@ templates = Jinja2Templates(directory="templates/")
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# LOAD DATA
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onet = pd.read_csv('static/ONET_JobTitles.csv')
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-
coheredat = pd.read_csv('static/neural_chat_tSNE_dat.csv')
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@app.get("/register/", response_class=HTMLResponse)
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def get_register(request: Request):
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return templates.TemplateResponse('register.html', context={'request': request})
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@@ -107,11 +107,13 @@ def post_logout(request: Request):
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message = "You have been successfully logged out."
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return templates.TemplateResponse('logout.html', context={'request': request, 'message': message})
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### JOB INFORMATION CENTER ###
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# GET
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@app.get("/")
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def get_job(request: Request):
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joblist = onet['JobTitle']
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return templates.TemplateResponse('job_list.html', context={'request': request, 'joblist': joblist})
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# POST
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@@ -130,8 +132,6 @@ def post_job(request: Request, bt: BackgroundTasks, jobtitle: str = Form(enum=[x
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interests = get_onet_interests(onetCode)
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values = get_onet_values(onetCode)
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styles = get_onet_styles(onetCode)
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-
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bt.add_task(neighborhoods, coheredat)
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return templates.TemplateResponse('job_list.html', context={
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'request': request,
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@@ -154,6 +154,7 @@ def post_job(request: Request, bt: BackgroundTasks, jobtitle: str = Form(enum=[x
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async def get_job_neighborhoods(request: Request):
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return templates.TemplateResponse('job_neighborhoods.html', context={'request': request})
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### FIND-MY-MATCH ###
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# GET
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@app.get("/find-my-match/", response_class=HTMLResponse)
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from localStoragePy import localStoragePy
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localStorage = localStoragePy('pathfinder', 'text')
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+
from scrape_onet import get_onet_code, get_onet_description, get_onet_tasks, get_onet_activities, get_onet_abilities, get_onet_interests, get_onet_knowledge, get_onet_context, get_onet_skills, get_onet_styles, get_onet_values, get_job_postings
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from match_utils import neighborhoods, get_resume, skill_extractor, sim_result_loop, get_links, skillEmbed, sim_result_loop_jobFinder, sim_result_loop_candFinder
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from user_utils import Hash
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# LOAD DATA
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onet = pd.read_csv('static/ONET_JobTitles.csv')
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# REGISTER, LOGIN, LOGOUT
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@app.get("/register/", response_class=HTMLResponse)
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def get_register(request: Request):
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return templates.TemplateResponse('register.html', context={'request': request})
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message = "You have been successfully logged out."
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return templates.TemplateResponse('logout.html', context={'request': request, 'message': message})
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### JOB INFORMATION CENTER ###
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# GET
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@app.get("/")
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def get_job(request: Request, bt: BackgroundTasks):
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joblist = onet['JobTitle']
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bt.add_task(neighborhoods)
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return templates.TemplateResponse('job_list.html', context={'request': request, 'joblist': joblist})
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# POST
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interests = get_onet_interests(onetCode)
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values = get_onet_values(onetCode)
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styles = get_onet_styles(onetCode)
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return templates.TemplateResponse('job_list.html', context={
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'request': request,
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async def get_job_neighborhoods(request: Request):
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return templates.TemplateResponse('job_neighborhoods.html', context={'request': request})
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+
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### FIND-MY-MATCH ###
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# GET
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@app.get("/find-my-match/", response_class=HTMLResponse)
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match_utils.py
CHANGED
@@ -10,6 +10,7 @@ from langchain_community.llms.ollama import Ollama
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from langchain_community.embeddings import OllamaEmbeddings
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from langchain.chains import LLMChain
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from langchain.output_parsers import CommaSeparatedListOutputParser
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# SSL CERTIFICATE FIX
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try:
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@@ -19,32 +20,30 @@ except AttributeError:
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else:
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ssl._create_default_https_context = _create_unverified_https_context
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# LOAD EMBEDDINGS:
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simdat = pd.read_csv('static/embeddings/
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-
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# LOAD LLM MODELS:
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model = Ollama(model="mistral", temperature=0)
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embedding_model = OllamaEmbeddings(model="neural-chat", temperature=0)
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parser = CommaSeparatedListOutputParser()
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#
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def remove_new_line(value):
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return ''.join(value.splitlines())
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-
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async def neighborhoods(coheredat = coheredat):
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def format_title(logo, title, subtitle, title_font_size = 28, subtitle_font_size=14):
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logo = f'<a href="/" target="_self">{logo}</a>'
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subtitle = f'<span style="font-size: {subtitle_font_size}px;">{subtitle}</span>'
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title = f'<span style="font-size: {title_font_size}px;">{title}</span>'
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return f'{logo}{title}<br>{subtitle}'
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fig = px.scatter(
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title=format_title(
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fig['layout'].update(height=1000, width=1500, font=dict(family='Courier New, monospace', color='black'))
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fig.write_html('templates/job_neighborhoods.html')
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-
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def get_resume(resume):
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path = f"static/{resume.filename}"
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with open(path, 'wb') as buffer:
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@@ -56,7 +55,6 @@ def get_resume(resume):
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resume = "\n".join(text)
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return resume
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def skill_extractor(resume):
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system_prompt_template = SystemMessagePromptTemplate.from_template("""
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### [INST]
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@@ -80,18 +78,26 @@ def skill_extractor(resume):
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result = remove_new_line(result['text'])
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return parser.parse(result)
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-
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def skillEmbed(skills):
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embeddings = embedding_model.
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return embeddings
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embeds = skillEmbed(skills)
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def cosine(A, B):
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return np.dot(A,B)/(norm(A)*norm(B))
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def format_sim(sim):
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return "{:0.2f}".format(sim)
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simResults = []
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[simResults.append(cosine(np.array(embeds), np.array(simdat.iloc[i,1:]))) for i in range(len(simdat))]
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simResults = pd.DataFrame(simResults)
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@@ -110,14 +116,12 @@ async def sim_result_loop(skills):
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simResults.iloc[x,1] = format_sim(simResults.iloc[x,1])
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return simResults, embeds
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-
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def get_links(simResults):
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links = []
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titles = simResults["JobTitle"]
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[links.append("https://www.onetonline.org/link/summary/" + get_onet_code(title)) for title in titles]
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return links
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def sim_result_loop_jobFinder(skills):
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embeds = skillEmbed(skills)
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def cosine(A, B):
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@@ -139,7 +143,6 @@ def sim_result_loop_jobFinder(skills):
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simResults.iloc[x,2] = format_sim(simResults.iloc[x,2])
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return simResults
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def sim_result_loop_candFinder(skills):
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embeds = skillEmbed(skills)
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def cosine(A, B):
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from langchain_community.embeddings import OllamaEmbeddings
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from langchain.chains import LLMChain
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from langchain.output_parsers import CommaSeparatedListOutputParser
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from sentence_transformers import SentenceTransformer
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# SSL CERTIFICATE FIX
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try:
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else:
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ssl._create_default_https_context = _create_unverified_https_context
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# LOAD DATA AND EMBEDDINGS:
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simdat = pd.read_csv('static/embeddings/onet_embeddings_st5.csv')
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tsne_dat = pd.read_csv('static/st5_tSNE_dat.csv')
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parser = CommaSeparatedListOutputParser()
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# LOAD MODELS:
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model = Ollama(model="mistral")
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embedding_model = SentenceTransformer('sentence-transformers/sentence-t5-base', device='cpu')
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# UTILITY FUNCTIONS:
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def remove_new_line(value):
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return ''.join(value.splitlines())
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async def neighborhoods(jobtitle=None):
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def format_title(logo, title, subtitle, title_font_size = 28, subtitle_font_size=14):
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logo = f'<a href="/" target="_self">{logo}</a>'
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subtitle = f'<span style="font-size: {subtitle_font_size}px;">{subtitle}</span>'
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title = f'<span style="font-size: {title_font_size}px;">{title}</span>'
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return f'{logo}{title}<br>{subtitle}'
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fig = px.scatter(tsne_dat, x = 'longitude', y = 'latitude', color = 'Category', hover_data = ['Category', 'Title'],
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title=format_title("Pathfinder", " Job Neighborhoods: Explore the Map!", ""))
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fig['layout'].update(height=1000, width=1500, font=dict(family='Courier New, monospace', color='black'))
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fig.write_html('templates/job_neighborhoods.html')
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def get_resume(resume):
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path = f"static/{resume.filename}"
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with open(path, 'wb') as buffer:
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resume = "\n".join(text)
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return resume
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def skill_extractor(resume):
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system_prompt_template = SystemMessagePromptTemplate.from_template("""
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### [INST]
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result = remove_new_line(result['text'])
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return parser.parse(result)
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def skillEmbed(skills):
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embeddings = embedding_model.encode(skills)
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return embeddings
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async def sim_result_loop(skilltext):
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if type(skilltext) == str:
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skills = skilltext
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if type(skilltext) == dict:
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skills = [key for key, value in skilltext.items() if value == "Skill"]
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skills = str(skills).replace("'", "").replace(",", "")
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if type(skilltext) == list:
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skills = ', '.join(skilltext)
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embeds = skillEmbed(skills)
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def cosine(A, B):
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return np.dot(A,B)/(norm(A)*norm(B))
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def format_sim(sim):
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return "{:0.2f}".format(sim)
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simResults = []
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[simResults.append(cosine(np.array(embeds), np.array(simdat.iloc[i,1:]))) for i in range(len(simdat))]
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simResults = pd.DataFrame(simResults)
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simResults.iloc[x,1] = format_sim(simResults.iloc[x,1])
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return simResults, embeds
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def get_links(simResults):
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links = []
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titles = simResults["JobTitle"]
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[links.append("https://www.onetonline.org/link/summary/" + get_onet_code(title)) for title in titles]
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return links
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def sim_result_loop_jobFinder(skills):
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embeds = skillEmbed(skills)
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def cosine(A, B):
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simResults.iloc[x,2] = format_sim(simResults.iloc[x,2])
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return simResults
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def sim_result_loop_candFinder(skills):
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embeds = skillEmbed(skills)
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def cosine(A, B):
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requirements.txt
CHANGED
@@ -10,7 +10,6 @@ python-multipart==0.0.5
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python-docx==0.8.11
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aiofiles==22.1.0
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nltk==3.8.1
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-
unidecode==1.3.6
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transformers==4.37.2
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torch==1.13.1
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accelerate==0.26.1
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localStoragePy==0.2.3
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mangum==0.17.0
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certifi==2023.7.22
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langchain==0.1.4
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python-docx==0.8.11
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aiofiles==22.1.0
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nltk==3.8.1
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transformers==4.37.2
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torch==1.13.1
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accelerate==0.26.1
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localStoragePy==0.2.3
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mangum==0.17.0
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certifi==2023.7.22
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langchain==0.1.4
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sentence-transformers==2.3.1
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scrape_onet.py
CHANGED
@@ -88,6 +88,25 @@ def get_onet_context(onetCode):
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df2 = pd.DataFrame(num_desc, columns = ['Importance', 'Condition'])
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df2 = df2[df2['Importance'] != '']
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context = df2
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return context
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else:
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return pd.DataFrame([("We're sorry."), ("This occupation is currently undergoing updates."), ("Please try again later.")])
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df2 = pd.DataFrame(num_desc, columns = ['Importance', 'Condition'])
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df2 = df2[df2['Importance'] != '']
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context = df2
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if len(context.index) < 5:
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context_url = "https://www.onetonline.org/link/result/" + onetCode + "?c=wc&n_wc=0&c_wc=0"
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response = requests.get(context_url, headers=headers, verify=False)
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soup = BeautifulSoup(response.text, 'html.parser')
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tasks = str(soup.get_text('reportsubdesc')).replace("reportsubdesc", " ").replace("ImportanceCategoryTask ", "")
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tasks = clean(tasks)
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if len(tasks.split('show all show top 10')) > 1:
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tasks = tasks.split('show all show top 10')[1]
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tasks = tasks.split('back to top')[0]
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tasks = remove_new_line(tasks).replace("related occupations", " ").replace("importance work activity", " ")
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tasks = tasks.split("? ")
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split_data = [item.split(" -- ")[0] for item in tasks]
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num_desc = []
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for i in range(len(tasks)):
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temp = [','.join(item) for item in split_data][i].split(',')
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num_desc.append([''.join([c for c in temp if c in '0123456789']), ''.join([c for c in temp if c not in '0123456789']).replace(')context work context', '')])
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df2 = pd.DataFrame(num_desc, columns = ['Importance', 'Condition'])
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df2 = df2[df2['Importance'] != '']
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context = df2
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return context
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else:
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return pd.DataFrame([("We're sorry."), ("This occupation is currently undergoing updates."), ("Please try again later.")])
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static/cohere_tSNE_dat.csv
DELETED
The diff for this file is too large to render.
See raw diff
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static/embeddings/.DS_Store
ADDED
Binary file (6.15 kB). View file
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static/embeddings/onet_embeddings_neural_chat.csv
DELETED
@@ -1,3 +0,0 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:ee0f38319dc32fabf6b4e3d21c668f2115fe8d04f83749c7ec549321da613bc0
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-
size 44269270
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static/embeddings/{cohere_embeddings.csv → onet_embeddings_st5.csv}
RENAMED
@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:e38dcf288a3eee4ed1f18d9ccd6513693539ca5f60b7f19f6f4ac34dee55a38b
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size 8310387
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static/jd_embeddings.csv
CHANGED
@@ -1,2 +1,2 @@
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2 |
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|
static/res_embeddings.csv
CHANGED
@@ -1,3 +1,2 @@
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1 |
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|
2 |
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641f876e-acc7-11ed-8b05-8a2dec87980d,test1,$2b$12$ziIVglNbgdsKmNMeLuNCY.6D5CwJMKS9n7eiUXNtSE4w35CmO9UeC,test1@gmail.com,candidate,0.9682617,0.9609375,0.7294922,-2.0058594,-0.12298584,-0.7705078,-1.1816406,0.50439453,1.3583984,-0.21020508,1.4619141,-1.1542969,-1.4482422,-0.71533203,-1.4423828,0.08679199,1.1484375,-0.30981445,0.3310547,0.4855957,1.1083984,0.49121094,-0.38989258,0.005828857,0.32788086,3.859375,1.5410156,-2.0742188,0.19763184,1.7324219,0.49267578,1.53125,0.51220703,-0.015586853,-0.9316406,-1.15625,0.94384766,-0.6015625,-1.0126953,-0.47485352,-2.1933594,0.3852539,0.18762207,1.1689453,-1.0566406,0.9355469,-1.6308594,-0.7080078,1.8203125,0.60839844,0.91064453,0.034332275,-1.5517578,1.3427734,-0.10437012,-0.5234375,-1.9541016,-0.28930664,0.44702148,0.40527344,-0.85595703,1.8486328,-0.4958496,0.21777344,-1.1582031,-0.12792969,0.1583252,0.4428711,-1.5380859,0.04006958,1.3720703,0.9506836,0.4260254,-0.42578125,1.4541016,0.8198242,-1.3818359,-1.5048828,0.67529297,0.05130005,0.58496094,0.92285156,-1.4091797,-0.31201172,-0.19006348,-0.9272461,-2.0703125,-0.7714844,0.020629883,-0.74902344,0.76953125,-2.1855469,0.55078125,-0.4741211,-1.0634766,0.51660156,1.8027344,-1.3691406,-0.25976562,-2.0800781,0.8989258,-1.3193359,0.9345703,0.22192383,0.7680664,0.35986328,1.5126953,-1.1123047,-2.1484375,-0.43481445,0.15075684,-0.22314453,2.7011719,-0.4638672,0.22375488,2.8398438,0.31958008,-1.0498047,-0.64501953,-1.4697266,0.9057617,0.47851562,0.45825195,-0.044525146,-0.09863281,-0.8208008,0.2854004,-2.03125,-0.7167969,0.77685547,-0.8461914,-0.34716797,3.0898438,0.953125,4.0859375,0.56396484,-1.4267578,0.18444824,1.3720703,0.22998047,0.002439499,1.1826172,-0.39453125,2.8613281,1.1025391,-1.1201172,0.8041992,-0.64453125,-1.1367188,-0.8955078,-2.2910156,-1.9912109,-1.3486328,0.49658203,-1.6757812,0.98876953,0.84765625,0.5283203,0.29467773,0.77978516,-0.53125,-0.5991211,2.4003906,0.7451172,0.75390625,1.0185547,1.9931641,0.32836914,-0.4152832,-2.7558594,1.3349609,-1.0410156,0.7133789,1.0878906,0.4025879,-1.0283203,-0.64453125,-3.3496094,0.38061523,-1.6865234,-0.13000488,-1.3046875,2.6152344,0.26000977,-0.8222656,-0.71484375,0.81103516,1.0927734,-2.0058594,1.1396484,0.8129883,-1.421875,-0.53466797,-0.7788086,-0.5961914,-0.24536133,1.6044922,0.55908203,4.0664062,-0.19848633,1.3046875,-0.23669434,-1.4833984,-1.8994141,0.9057617,1.0849609,-0.3112793,-0.92578125,-0.2685547,1.6689453,-1.3339844,-2,0.47387695,0.011543274,0.8129883,1.7851562,-0.62597656,0.6767578,1.7607422,-0.1105957,0.5830078,-2.6972656,-0.47924805,-2.2089844,-1.7148438,-1.5615234,-2.125,0.091796875,0.7993164,-0.1496582,-0.4584961,-1.4326172,1.0097656,2.5898438,1.1689453,-0.63671875,-0.69677734,2.3652344,0.2355957,0.04776001,0.5517578,0.95458984,-1.4453125,-0.7705078,-0.6425781,-0.8105469,0.85302734,-0.5620117,-0.91503906,-0.90234375,1.7978516,0.9506836,-0.25976562,-1.1328125,-1.9951172,-0.7734375,-0.5834961,-0.22253418,0.26489258,-0.79541016,-1.7089844,-1.5292969,1.2998047,0.0927124,0.48632812,0.50146484,-1.8876953,-0.42822266,0.42651367,0.8144531,0.9038086,0.56396484,6.75,0.6220703,0.7939453,0.11853027,1.9326172,-0.5786133,-1.8408203,0.02192688,0.5444336,-1.7832031,0.34228516,0.8359375,1.9648438,-0.26123047,-2.0253906,-0.296875,1.2431641,-0.6982422,-2.1347656,-0.67822266,-0.40649414,-1.9326172,-0.2668457,0.57177734,-0.9379883,0.2841797,-1.4775391,-0.24157715,-1.0292969,0.48364258,-0.12451172,-0.98339844,0.121032715,1.2832031,-0.5683594,-1.7753906,-0.6166992,0.39990234,-1.71875,-1.6435547,-0.7397461,1.2861328,0.5024414,-1.3955078,-0.1541748,-1.7392578,-1.2441406,-3.2617188,-0.15698242,-1.734375,-0.9267578,-0.09893799,-0.6767578,-1.6533203,1.3076172,-2.3730469,1.9287109,0.9609375,0.25512695,1.7353516,-1.4121094,0.92089844,0.30273438,0.60839844,1.1171875,0.3227539,-1.4785156,-2.4042969,-0.4873047,1.3925781,-3.34375,-0.24243164,-1.0273438,0.5546875,-0.74902344,1.3916016,1.6738281,-0.51416016,1.3701172,-1.2246094,2.1152344,-1.1757812,1.7431641,1.9726562,2.4355469,0.4025879,-2.6386719,2.5019531,-0.3581543,-0.45117188,-4.0429688,-0.4165039,0.69091797,-0.9321289,0.15808105,-2.2910156,1.0244141,0.33154297,-1.0126953,2.328125,-0.23034668,0.107910156,0.6713867,-0.14404297,0.17163086,-1.5351562,-0.6225586,-2.4257812,1.9492188,0.34155273,-0.068115234,0.94091797,-1.9912109,0.50439453,-0.17114258,-0.12658691,-0.8339844,-0.6767578,1.1279297,-0.57128906,3.0136719,1.6484375,-0.66308594,-1.3720703,1.5595703,1.2666016,0.13024902,0.18237305,0.57714844,0.35766602,-1.2490234,1.3564453,-0.52001953,1.0117188,-1.6005859,-0.8173828,-0.97802734,-2.4707031,0.93603516,-1.8339844,1.0703125,0.8623047,0.0725708,-0.23132324,-1.0908203,-1.6308594,-1.8134766,1.0810547,1.3554688,2.0859375,0.13549805,0.41235352,0.2130127,-0.58496094,0.7597656,-0.27612305,-2.0644531,1.3984375,-1.5107422,0.33129883,1.0097656,1.5253906,-1.0429688,-2.7460938,1.3486328,1.1884766,-0.11981201,-0.54785156,0.4729004,-0.6713867,0.93847656,1.4541016,0.20739746,3.984375,1.6875,1.0683594,-0.734375,1.6337891,-0.6123047,0.7817383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static/{neural_chat_tSNE_dat.csv → st5_tSNE_dat.csv}
RENAMED
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See raw diff
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