Create careermatcher.py

careermatcher.py

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+import streamlit as st
+import plotly.express as px
+import pandas as pd
+import numpy as np
+import pickle as pkl
+import spacy
+from spacy.lang.en.stop_words import STOP_WORDS
+nlp = spacy.load('en_core_web_lg')
+import re
+import docx2txt
+from spacy.matcher import PhraseMatcher
+
+# from transformers import BertForSequenceClassification
+# from transformers import BertTokenizer
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# tokenizer = AutoTokenizer.from_pretrained("liberatoratif/BERT-resume-job-recommender")
+# model = AutoModelForSequenceClassification.from_pretrained("liberatoratif/BERT-resume-job-recommender")
+
+matcher = PhraseMatcher(nlp.vocab)
+
+import torch
+
+st.set_page_config(
+    page_title="Resume Scanner",
+    page_icon="📝",
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+
+
+# output_dir = "model_save"
+enc_dir = "target_encodings.pkl"
+matcher_dir = "linkedin_skill.txt"
+
+
+# @st.cache
+def bert():
+    # model_loaded_temp = BertForSequenceClassification.from_pretrained(output_dir)
+    model_loaded_temp = AutoModelForSequenceClassification.from_pretrained("liberatoratif/BERT-resume-job-recommender")
+    return model_loaded_temp
+
+# @st.cache
+def bert_token():
+    # tokenizer_loaded_temp = BertTokenizer.from_pretrained(output_dir)
+    tokenizer_loaded_temp = AutoTokenizer.from_pretrained("liberatoratif/BERT-resume-job-recommender")
+    return tokenizer_loaded_temp
+
+# @st.cache
+def label_enc():
+    enc = pkl.load(open(enc_dir, 'rb'))
+    return enc
+
+# @st.cache
+def ph_match():
+    with open(matcher_dir, 'r', encoding='utf-8') as file:
+        text = file.read()
+
+    return text
+
+
+label_encoder = label_enc()
+model_loaded = bert()
+tokenizer_loaded = bert_token()
+
+txt = ph_match()
+
+st.markdown(
+    """
+    <style>
+    [data-testid="stSidebar"][aria-expanded="true"] > div:first-child {
+        width: 250px;
+    }
+    [data-testid="stSidebar"][aria-expanded="false"] > div:first-child {
+        width: 150px;
+        margin-left: -500px;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+st.markdown("<h1 style='text-align: centre; color: cyan;'>RESUME/CV SCANNER</h1>",
+                unsafe_allow_html=True)
+st.markdown("<h6 style='text-align: centre; color: white;'>Know which domain fit's your resume :)</h1>",
+                unsafe_allow_html=True)
+
+stops = list(STOP_WORDS) 
+
+def extract_text_from_docx(docx_path):
+    txt = docx2txt.process(docx_path)
+    if txt:
+        return txt.replace('\t', ' ')
+    return None
+
+def cleanResume(resumeText):
+    resumeText = ' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)"," ",resumeText).split())
+    resumeText = re.sub(r'[^\x00-\x7F]+',r' ', resumeText)
+    resumeText = ''.join(resumeText.splitlines())
+    return resumeText
+
+def complete_pack(x):
+  demo = nlp(x)
+  lst = [i.text.lower() for i in demo if i.text.lower() not in stops]
+  return lst
+
+
+with st.sidebar:
+    global resume_text, upload
+    global resume_text_spacy, re_temp
+    upload = st.file_uploader("DRAG AND DROP YOUR RESUME NOW")
+    st.markdown("<h5 style='text-align: centre; color: red;'>Only .docx type files accepted</h1>",
+                unsafe_allow_html=True)
+    if upload:
+        try:
+            resume_text = extract_text_from_docx(upload)
+            resume_text = resume_text.replace('\n\n', ' ')
+            re_temp = cleanResume(resume_text)
+            resume_text_spacy = nlp(re_temp)
+        except Exception as e:
+            st.error('WRONG FILE FORMAT : Only .docx(WORD DOC) type of files are accepted')
+
+
+scan = st.button('SCAN 📝')
+if scan:
+    try:
+        emails = re.findall(r"[a-z0-9\.\-+_]+@[a-z0-9\.\-+_]+\.[a-z]+", resume_text)
+        phone = re.findall(r'[\+\(]?[1-9][0-9 .\-\(\)]{8,}[0-9]', resume_text)
+        links = re.findall(r"(?i)\b((?:https?://|www\d{0,3}[.]|[a-z0-9.\-]+[.][a-z]{2,4}/)(?:[^\s()<>]+|\(([^\s()<>]+|(\([^\s()<>]+\)))*\))+(?:\(([^\s()<>]+|(\([^\s()<>]+\)))*\)|[^\s`!()\[\]{};:'\".,<>?«»“”‘’]))", resume_text)
+
+
+        txt = txt.split('\n')
+        ev = [nlp.make_doc(i) for i in txt]
+        matcher.add("SKILLS", None, *ev)
+        get_skills = matcher(resume_text_spacy)
+
+        demo = []
+        for match_id, start, end in get_skills:
+            span = resume_text_spacy[start : end]
+            demo.append(span.text)
+
+        re_text = ' '.join(demo)  
+        my_skills_re_text = re_text
+        my_skills_clean_re_text = cleanResume(my_skills_re_text)
+
+        skills = complete_pack(my_skills_clean_re_text)
+        skills = ' '.join(skills)
+        lst = []
+        lst.append(skills)
+
+
+        model_loaded.eval()
+
+# Tokenize the input text
+        input_ids = tokenizer_loaded.encode(lst[0], add_special_tokens=True)
+        input_ids = torch.tensor(input_ids).unsqueeze(0)  # Add batch dimension
+
+        # Move the input tensor to the same device as the model
+        # input_ids = input_ids.to(device)
+        # model_loaded = model_loaded.to(device)
+
+        # Perform the forward pass to get the model's predictions
+        with torch.no_grad():
+            result = model_loaded(input_ids, token_type_ids=None, attention_mask=None, return_dict=True)
+            logits = result.logits
+
+        # Move the logits to the CPU and convert to numpy array
+        logits = logits.detach().cpu().numpy()
+
+        # Get the predicted label
+        predicted_label = np.argmax(logits)
+
+        # Print the predicted label
+        # st.write("Predicted Label:", predicted_label)
+
+        probs = logits[0]
+        # print("text:", lst[0])
+        # print("predictions:", probs)
+        pred_idx = np.argmax(probs) 
+        # kp = list(pred_idx)
+        d = {}
+        ind = 0
+
+        for i in probs:
+            d[label_encoder.inverse_transform([ind])[0]] = i
+            ind+=1
+        # st.write("Your skills are matching to : ", label_encoder.inverse_transform([pred_idx])[0])
+        domain = label_encoder.inverse_transform([pred_idx])[0]
+        data = pd.DataFrame({'Domains' : list(d.keys()), 'Probs' : list(d.values())})
+        # st.markdown(f"**Your skills are matching to:** <span style='color: cyan;'>{domain}</span>", unsafe_allow_html=True) #BF3EFF
+        st.markdown(f"<span style='color: #BF3EFF;'>**Your skills are matching to :**</span> <span style='color: #54FF9F;'>{domain}</span>", unsafe_allow_html=True)
+        datacpy = data.copy()
+        datacpy['Probs'] = datacpy['Probs']*10
+        datacpy.rename(columns={'Probs': 'Percentage Prediction of your Domain'}, inplace=True)
+
+        st.markdown("<h3 style='text-align: centre; color: blue;'>PERCENT OF YOUR DOMAIN MATCH</h3>",
+                unsafe_allow_html=True)
+
+        st.dataframe(datacpy.sort_values('Percentage Prediction of your Domain', ascending=False))
+        domains = px.bar(data, x = 'Domains', y = 'Probs',width=800, height=400) 
+        st.plotly_chart(domains)
+
+        
+        if len(list(set(emails))) > 0:
+            st.markdown("<h4 style='text-align: centre; color: blue;'>EMAIL ✔️ </h1>",
+                unsafe_allow_html=True)
+            st.success(list(set(emails)))
+        else:
+            st.markdown("<h4 style='text-align: centre; color: blue;'>EMAIL ❌ </h1>",
+                unsafe_allow_html=True)
+            st.error('Email-Id is not present try including it in your Resume')
+
+
+
+        if len(list(set(phone))) > 0:
+            st.markdown("<h4 style='text-align: centre; color: blue;'>MOBILE NO ✔️ </h1>",
+                unsafe_allow_html=True)
+            st.success(list(set(phone)))
+        else:
+            st.markdown("<h4 style='text-align: centre; color: blue;'>MOBILE NO ❌ </h1>",
+                unsafe_allow_html=True)
+            st.error('Mobile number is not present try including it in your Resume')
+
+
+        
+        if len(list(set(links))) > 0:
+            st.markdown("<h4 style='text-align: centre; color: blue;'>LINKS ✔️ </h1>",
+                unsafe_allow_html=True)
+            st.success(list(set(links)))
+        else:
+            st.markdown("<h4 style='text-align: centre; color: blue;'>LINKS ❌</h1>",
+                unsafe_allow_html=True)
+            st.error("Link's are not present try including your Github or LinkedIn Profile in your Resume")
+
+
+    except Exception as e:
+        st.write(e)
+        st.error("😲 Try uploading your file again")
+
+