upgrading text embedding model

main.py

@@ -17,7 +17,7 @@ from mangum import Mangum
 from localStoragePy import localStoragePy
 localStorage = localStoragePy('pathfinder', 'text')
 
-from scrape_onet import get_onet_code, get_onet_description, get_onet_tasks, get_onet_activities, get_onet_context, get_onet_skills, get_onet_knowledge, get_onet_abilities, get_onet_interests, get_onet_styles, get_onet_values, get_job_postings
+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
 from match_utils import neighborhoods, get_resume, skill_extractor, sim_result_loop, get_links, skillEmbed, sim_result_loop_jobFinder, sim_result_loop_candFinder
 from user_utils import Hash
 
@@ -29,8 +29,8 @@ templates = Jinja2Templates(directory="templates/")
 
 # LOAD DATA
 onet = pd.read_csv('static/ONET_JobTitles.csv')
-coheredat = pd.read_csv('static/neural_chat_tSNE_dat.csv')
 
+# REGISTER, LOGIN, LOGOUT
 @app.get("/register/", response_class=HTMLResponse)
 def get_register(request: Request):
     return templates.TemplateResponse('register.html', context={'request': request})
@@ -107,11 +107,13 @@ def post_logout(request: Request):
     message = "You have been successfully logged out."
     return templates.TemplateResponse('logout.html', context={'request': request, 'message': message})
 
+
 ### JOB INFORMATION CENTER ###
 # GET
 @app.get("/")
-def get_job(request: Request):
+def get_job(request: Request, bt: BackgroundTasks):
     joblist = onet['JobTitle']
+    bt.add_task(neighborhoods)
     return templates.TemplateResponse('job_list.html', context={'request': request, 'joblist': joblist})
 
 # POST
@@ -130,8 +132,6 @@ def post_job(request: Request, bt: BackgroundTasks, jobtitle: str = Form(enum=[x
         interests = get_onet_interests(onetCode)
         values = get_onet_values(onetCode)
         styles = get_onet_styles(onetCode)
-
-        bt.add_task(neighborhoods, coheredat)
         
         return templates.TemplateResponse('job_list.html', context={
             'request': request, 
@@ -154,6 +154,7 @@ def post_job(request: Request, bt: BackgroundTasks, jobtitle: str = Form(enum=[x
 async def get_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)

match_utils.py

@@ -10,6 +10,7 @@ from langchain_community.llms.ollama import Ollama
 from langchain_community.embeddings import OllamaEmbeddings
 from langchain.chains import LLMChain
 from langchain.output_parsers import CommaSeparatedListOutputParser
+from sentence_transformers import SentenceTransformer
 
 # SSL CERTIFICATE FIX
 try:
@@ -19,32 +20,30 @@ except AttributeError:
 else:
     ssl._create_default_https_context = _create_unverified_https_context
 
-# LOAD EMBEDDINGS:
-simdat = pd.read_csv('static/embeddings/onet_embeddings_neural_chat.csv')
-coheredat = pd.read_csv('static/neural_chat_tSNE_dat.csv')
-
-# LOAD LLM MODELS:
-model = Ollama(model="mistral", temperature=0)
-embedding_model = OllamaEmbeddings(model="neural-chat", temperature=0)
+# LOAD DATA AND EMBEDDINGS:
+simdat = pd.read_csv('static/embeddings/onet_embeddings_st5.csv')
+tsne_dat = pd.read_csv('static/st5_tSNE_dat.csv')
 parser = CommaSeparatedListOutputParser()
 
-# UTILITY FUNCTIONS
+# LOAD MODELS:
+model = Ollama(model="mistral")
+embedding_model = SentenceTransformer('sentence-transformers/sentence-t5-base', device='cpu')
+
+# UTILITY FUNCTIONS:
 def remove_new_line(value):
         return ''.join(value.splitlines())
 
-
-async def neighborhoods(coheredat = coheredat):
+async def neighborhoods(jobtitle=None):
     def format_title(logo, title, subtitle, title_font_size = 28, subtitle_font_size=14):
         logo = f'<a href="/" target="_self">{logo}</a>'
         subtitle = f'<span style="font-size: {subtitle_font_size}px;">{subtitle}</span>'
         title = f'<span style="font-size: {title_font_size}px;">{title}</span>'
         return f'{logo}{title}<br>{subtitle}'
-    fig = px.scatter(coheredat, x = 'longitude', y = 'latitude', color = 'Category', hover_data = ['Category', 'Title'], 
-        title=format_title(logo="Pathfinder", title="     Job Neighborhoods: Explore the Map!", subtitle=""))
+    fig = px.scatter(tsne_dat, x = 'longitude', y = 'latitude', color = 'Category', hover_data = ['Category', 'Title'], 
+        title=format_title("Pathfinder", "     Job Neighborhoods: Explore the Map!", ""))
     fig['layout'].update(height=1000, width=1500, font=dict(family='Courier New, monospace', color='black'))
     fig.write_html('templates/job_neighborhoods.html')
 
-
 def get_resume(resume):
     path = f"static/{resume.filename}"
     with open(path, 'wb') as buffer:
@@ -56,7 +55,6 @@ def get_resume(resume):
     resume = "\n".join(text)
     return resume
 
-
 def skill_extractor(resume):
      system_prompt_template = SystemMessagePromptTemplate.from_template("""
      ### [INST] 
@@ -80,18 +78,26 @@ def skill_extractor(resume):
      result = remove_new_line(result['text'])
      return parser.parse(result)
 
-
 def skillEmbed(skills):
-    embeddings = embedding_model.embed_query(skills)
+    embeddings = embedding_model.encode(skills)
     return embeddings
 
-
-async def sim_result_loop(skills):
+async def sim_result_loop(skilltext):
+    if type(skilltext) == str:
+        skills = skilltext
+    if type(skilltext) == dict:
+        skills = [key for key, value in skilltext.items() if value == "Skill"]
+        skills = str(skills).replace("'", "").replace(",", "")
+    if type(skilltext) == list: 
+        skills = ', '.join(skilltext)
     embeds = skillEmbed(skills)
+    
     def cosine(A, B):
         return np.dot(A,B)/(norm(A)*norm(B))
+    
     def format_sim(sim):
         return "{:0.2f}".format(sim)
+    
     simResults = []
     [simResults.append(cosine(np.array(embeds), np.array(simdat.iloc[i,1:]))) for i in range(len(simdat))]
     simResults = pd.DataFrame(simResults)
@@ -110,14 +116,12 @@ async def sim_result_loop(skills):
         simResults.iloc[x,1] = format_sim(simResults.iloc[x,1])
     return simResults, embeds
 
-
 def get_links(simResults):
     links = []
     titles = simResults["JobTitle"]
     [links.append("https://www.onetonline.org/link/summary/" + get_onet_code(title)) for title in titles]
     return links
 
-
 def sim_result_loop_jobFinder(skills):
     embeds = skillEmbed(skills)
     def cosine(A, B):
@@ -139,7 +143,6 @@ def sim_result_loop_jobFinder(skills):
         simResults.iloc[x,2] = format_sim(simResults.iloc[x,2])
     return simResults
 
-
 def sim_result_loop_candFinder(skills):
     embeds = skillEmbed(skills)
     def cosine(A, B):

requirements.txt

@@ -10,7 +10,6 @@ python-multipart==0.0.5
 python-docx==0.8.11
 aiofiles==22.1.0
 nltk==3.8.1
-unidecode==1.3.6
 transformers==4.37.2
 torch==1.13.1
 accelerate==0.26.1
@@ -20,4 +19,5 @@ passlib==1.7.4
 localStoragePy==0.2.3
 mangum==0.17.0
 certifi==2023.7.22
-langchain==0.1.4
+langchain==0.1.4
+sentence-transformers==2.3.1

scrape_onet.py

@@ -88,6 +88,25 @@ def get_onet_context(onetCode):
         df2 = pd.DataFrame(num_desc, columns = ['Importance', 'Condition'])
         df2 = df2[df2['Importance'] != '']
         context = df2
+        if len(context.index) < 5:
+            context_url = "https://www.onetonline.org/link/result/" + onetCode + "?c=wc&n_wc=0&c_wc=0"
+            response = requests.get(context_url, headers=headers, verify=False)
+            soup = BeautifulSoup(response.text, 'html.parser')
+            tasks = str(soup.get_text('reportsubdesc')).replace("reportsubdesc", " ").replace("ImportanceCategoryTask ", "")
+            tasks = clean(tasks)
+            if len(tasks.split('show all show top 10')) > 1:
+                tasks = tasks.split('show all show top 10')[1]
+                tasks = tasks.split('back to top')[0]
+                tasks = remove_new_line(tasks).replace("related occupations", " ").replace("importance work activity", " ")
+                tasks = tasks.split("? ")
+                split_data = [item.split(" -- ")[0] for item in tasks]
+                num_desc = []
+                for i in range(len(tasks)):
+                    temp = [','.join(item) for item in split_data][i].split(',')
+                    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', '')])
+                df2 = pd.DataFrame(num_desc, columns = ['Importance', 'Condition'])
+                df2 = df2[df2['Importance'] != '']
+                context = df2
         return context
     else: 
         return pd.DataFrame([("We're sorry."), ("This occupation is currently undergoing updates."), ("Please try again later.")])

static/embeddings/onet_embeddings_neural_chat.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ee0f38319dc32fabf6b4e3d21c668f2115fe8d04f83749c7ec549321da613bc0
-size 44269270

static/embeddings/{cohere_embeddings.csv → onet_embeddings_st5.csv}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:86072bee6b0fe3ea47f18afd9c7959c2c672ebaf24ed6db24a6b375f63035dea
-size 38573720
+oid sha256:e38dcf288a3eee4ed1f18d9ccd6513693539ca5f60b7f19f6f4ac34dee55a38b
+size 8310387

static/jd_embeddings.csv

@@ -1,2 +1,2 @@
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static/res_embeddings.csv

@@ -1,3 +1,2 @@
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