# Directory structure:
# - app.py
# - utils.py
# - job_api.py
# - requirements.txt
# - assets/ (for optional icons/images)

# app.py
import streamlit as st
from utils import parse_resume, get_recommendations, load_models, generate_career_insights
from job_api import fetch_jobs

st.set_page_config(page_title="Universal CV Analyzer", layout="wide")

st.title("📄 Universal Smart CV Analyzer & Career Roadmap")

uploaded_file = st.file_uploader("Upload your CV (PDF)", type="pdf")

if uploaded_file:
    with st.spinner("Analyzing your CV. Please wait..."):
        nlp, llm = load_models()
        text, parsed = parse_resume(uploaded_file, nlp)
        st.success("✅ Resume parsed successfully")

        st.header("🔍 CV Summary & Score")
        st.json(parsed)
        score, feedback = get_recommendations(parsed)
        st.metric(label="CV Score (out of 100)", value=score)
        st.write("**Suggestions to improve:**")
        st.write(feedback)

        st.header("💼 Live Job Listings")
        jobs = fetch_jobs(parsed)
        for job in jobs:
            st.markdown(f"**{job['title']}** at {job['company']}\n\n{job['location']} - {job['salary']}\n\n[Apply]({job['url']})")

        st.header("🎓 Certification & Higher Education Suggestions")
        certs = generate_career_insights(parsed, llm, suggestion_type="certifications")
        degrees = generate_career_insights(parsed, llm, suggestion_type="degrees")
        st.write("**Recommended Certifications:**")
        st.write(certs)
        st.write("**Higher Education Paths:**")
        st.write(degrees)

        st.header("🧭 Personalized Career Roadmap & Visa Advice")
        roadmap = generate_career_insights(parsed, llm, suggestion_type="roadmap")
        st.write(roadmap)

        st.header("🧠 Career Counselor Advice")
        advice = generate_career_insights(parsed, llm, suggestion_type="counselor")
        st.write(advice)