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| import streamlit as st | |
| import pandas as pd | |
| import pdfplumber | |
| import spacy | |
| # Set page config at the top | |
| st.set_page_config(page_title="Skill Scoring App", layout="wide") | |
| # Load spaCy model | |
| import spacy.cli | |
| spacy.cli.download("en_core_web_sm") | |
| nlp = spacy.load("en_core_web_sm") | |
| # Load datasets | |
| skills_df = pd.read_csv("skills_dataset.csv") | |
| countries_df = pd.read_csv("countries_dataset.csv") | |
| cert_df = pd.read_csv("certifications.csv") | |
| edu_tech_df = pd.read_csv("education_technical.csv") | |
| edu_non_tech_df = pd.read_csv("education_non_technical.csv") | |
| # Helper functions | |
| def extract_text_from_pdf(file): | |
| with pdfplumber.open(file) as pdf: | |
| return "\n".join( | |
| page.extract_text() | |
| for page in pdf.pages | |
| if page.extract_text() | |
| ) | |
| def extract_entities(text): | |
| doc = nlp(text) | |
| # Identify skills present in the CV | |
| skills = [token.text for token in doc if token.text in skills_df['Skill'].values] | |
| # Determine technical vs non‑technical background | |
| technical_skills = {"Python", "Machine Learning", "Cloud Computing", "Cybersecurity", "AI", "DevOps"} | |
| background = "technical" if any(s in technical_skills for s in skills) else "non-technical" | |
| return list(set(skills)), background | |
| def score_skills(user_skills): | |
| if not skills_df.shape[0]: | |
| return 0 | |
| return int((len(user_skills) / len(skills_df)) * 100) | |
| def recommend_countries(skills): | |
| df = countries_df[countries_df['Skill'].isin(skills)] | |
| return df[["Country", "AverageSalary", "VisaPath"]].drop_duplicates().reset_index(drop=True) | |
| def recommend_certifications(skills): | |
| return cert_df[cert_df['Skill'].isin(skills)].reset_index(drop=True) | |
| def recommend_education(background): | |
| return edu_tech_df.reset_index(drop=True) if background == "technical" else edu_non_tech_df.reset_index(drop=True) | |
| # Streamlit UI | |
| st.title("📊 Personalized Skill Scoring & Career Path App") | |
| st.markdown( | |
| "Upload your CV and get a personalized career guide based on your skills and background." | |
| ) | |
| uploaded_file = st.file_uploader("📤 Upload your CV (PDF format only)", type=["pdf"]) | |
| if uploaded_file: | |
| with st.spinner("Analyzing your CV..."): | |
| text = extract_text_from_pdf(uploaded_file) | |
| skills, background = extract_entities(text) | |
| score = score_skills(skills) | |
| country_info = recommend_countries(skills) | |
| certs = recommend_certifications(skills) | |
| edu = recommend_education(background) | |
| st.subheader("✅ Identified Skills") | |
| st.write(skills or "No recognized skills found.") | |
| st.subheader("📈 Skill Score") | |
| st.metric("Your Skill Score", f"{score}/100") | |
| st.subheader("🌍 Country Recommendations") | |
| if not country_info.empty: | |
| st.dataframe(country_info) | |
| else: | |
| st.write("No country recommendations available for your skill set.") | |
| st.subheader("🎓 Recommended Certifications") | |
| if not certs.empty: | |
| st.dataframe(certs) | |
| else: | |
| st.write("No certification recommendations available for your skill set.") | |
| st.subheader("🎓 Higher Education Opportunities") | |
| if not edu.empty: | |
| st.dataframe(edu) | |
| else: | |
| st.write("No higher education opportunities available for your background.") | |
| else: | |
| st.info("Please upload your CV to begin.") | |