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import streamlit as st
from utils.parser import parse_cv, extract_education_level, identify_cv_type
from utils.cv_scoring import calculate_cv_score
from utils.suggestions import (
get_certification_suggestions,
get_higher_education_suggestions,
get_visa_recommendations,
get_career_advice,
get_job_listings,
)
from utils.visualizer import generate_timeline
from PIL import Image
from fpdf import FPDF
import matplotlib.pyplot as plt
import tempfile
import io
# ------------------ PDF Generation Function ------------------
def generate_pdf_report(cv_type, education_level, score, score_breakdown, certs, edu, visa, advice, jobs, fig):
pdf = FPDF()
pdf.add_page()
pdf.set_font("Arial", size=12)
pdf.cell(200, 10, txt="CV Analysis Report", ln=True, align="C")
pdf.ln(10)
pdf.cell(200, 10, txt=f"CV Type: {cv_type}", ln=True)
pdf.cell(200, 10, txt=f"Education Level: {education_level}", ln=True)
pdf.cell(200, 10, txt=f"CV Score: {score}/100", ln=True)
pdf.ln(5)
pdf.set_font("Arial", style="B", size=12)
pdf.cell(200, 10, txt="Score Breakdown:", ln=True)
pdf.set_font("Arial", size=12)
for k, v in score_breakdown.items():
pdf.cell(200, 10, txt=f"- {k}: {v}", ln=True)
pdf.ln(5)
def add_list_to_pdf(title, items):
pdf.set_font("Arial", style="B", size=12)
pdf.cell(200, 10, txt=title, ln=True)
pdf.set_font("Arial", size=12)
if isinstance(items, list):
for item in items:
pdf.multi_cell(0, 10, f"- {item}")
else:
pdf.multi_cell(0, 10, str(items))
pdf.ln(5)
add_list_to_pdf("Certification Suggestions:", certs)
add_list_to_pdf("Higher Education Suggestions:", edu)
add_list_to_pdf("Visa Recommendations:", visa)
add_list_to_pdf("Career Advice:", advice)
add_list_to_pdf("Job Listings:", jobs)
# Save the figure as an image and insert into PDF
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmpfile:
fig.savefig(tmpfile.name, bbox_inches='tight')
pdf.add_page()
pdf.image(tmpfile.name, x=10, y=20, w=pdf.w - 20)
return pdf
# ------------------ Streamlit App ------------------
st.title("Smart CV Analyzer & Career Roadmap")
uploaded_file = st.file_uploader("Upload your CV in PDF format", type=["pdf"])
if uploaded_file is not None:
with open("temp_cv.pdf", "wb") as f:
f.write(uploaded_file.getbuffer())
parsed_text = parse_cv("temp_cv.pdf")
education_level = extract_education_level(parsed_text)
cv_type = identify_cv_type(parsed_text)
score, score_breakdown = calculate_cv_score(parsed_text, education_level)
st.header("CV Analysis Results")
st.write(f"**CV Type:** {cv_type}")
st.write(f"**Education Level:** {education_level}")
st.write(f"### Overall CV Score: {score} / 100")
st.write("### Score Breakdown:")
for k, v in score_breakdown.items():
st.write(f"- {k}: {v}")
with st.expander("Certification Suggestions"):
certs = get_certification_suggestions(parsed_text)
st.write(certs)
with st.expander("Higher Education Suggestions"):
edu = get_higher_education_suggestions(parsed_text)
st.write(edu)
with st.expander("Visa Recommendations"):
visa = get_visa_recommendations(parsed_text)
st.write(visa)
with st.expander("Career Advice"):
advice = get_career_advice(parsed_text)
st.write(advice)
with st.expander("Job Listings"):
jobs = get_job_listings(parsed_text)
for job in jobs:
st.write(f"- {job}")
st.header("📈 Career Roadmap Timeline")
fig = generate_timeline(cv_type, education_level, score)
st.pyplot(fig)
# --- PDF Download Button ---
# --- PDF Download Button ---
st.subheader("Download Report")
# Generate the PDF as a string
pdf_bytes = generate_pdf_report(cv_type, education_level, score, score_breakdown, certs, edu, visa, advice, jobs, fig).output(dest='S').encode('latin1')
# Display a single download button
st.download_button(
label="📄 Download CV Analysis Report (PDF)",
data=pdf_bytes,
file_name="cv_analysis_report.pdf",
mime="application/pdf"
)
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