import streamlit as st
from PyPDF2 import PdfReader
from docx import Document
from sentence_transformers import SentenceTransformer, util
from fpdf import FPDF
import tempfile
import os

# Load model
embedder = SentenceTransformer("all-MiniLM-L6-v2")

# File readers
def read_pdf(file):
    reader = PdfReader(file)
    return "\n".join(page.extract_text() for page in reader.pages if page.extract_text())

def read_docx(file):
    doc = Document(file)
    return "\n".join([para.text for para in doc.paragraphs])

def read_txt(file):
    return file.read().decode("utf-8")

# PDF generator
def generate_pdf(text, template):
    pdf = FPDF()
    pdf.add_page()

    if template == "Modern":
        pdf.set_font("Arial", "B", 14)
        pdf.set_text_color(50, 50, 50)
    elif template == "Classic":
        pdf.set_font("Times", "", 12)
        pdf.set_text_color(0, 0, 0)
    elif template == "Creative":
        pdf.set_font("Courier", "I", 12)
        pdf.set_text_color(80, 40, 120)

    lines = text.split("\n")
    for line in lines:
        try:
            pdf.cell(0, 10, txt=line.encode("latin-1", "replace").decode("latin-1"), ln=True)
        except:
            pdf.cell(0, 10, txt="...", ln=True)

    tmp_path = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
    pdf.output(tmp_path.name)
    return tmp_path.name

# Skill extraction (simple keyword matching for demo)
def extract_skills(text):
    keywords = ["Python", "Machine Learning", "Deep Learning", "Research", "Data Analysis", "Communication", "Teamwork"]
    return [kw for kw in keywords if kw.lower() in text.lower()]

# App layout
st.title("Smart Resume Optimizer & Generator")

st.markdown("Upload your **Resume** and paste your **Job or Scholarship Description** to get a customized version!")

resume_file = st.file_uploader("Upload Resume (PDF, DOCX, TXT)", type=["pdf", "docx", "txt"])
job_description = st.text_area("Paste Job or Scholarship Description Here", height=200)

if st.button("Submit"):
    if resume_file and job_description:
        # Extract resume text
        ext = resume_file.name.split(".")[-1]
        if ext == "pdf":
            resume_text = read_pdf(resume_file)
        elif ext == "docx":
            resume_text = read_docx(resume_file)
        elif ext == "txt":
            resume_text = read_txt(resume_file)
        else:
            st.error("Unsupported file format")
            st.stop()

        # Embeddings and Fit Score
        resume_emb = embedder.encode(resume_text, convert_to_tensor=True)
        job_emb = embedder.encode(job_description, convert_to_tensor=True)
        fit_score = util.cos_sim(resume_emb, job_emb).item()
        fit_percent = round(fit_score * 100, 2)

        # Skill Suggestions
        resume_skills = extract_skills(resume_text)
        job_skills = extract_skills(job_description)
        missing_skills = list(set(job_skills) - set(resume_skills))

        # Optimized Resume Text
        optimized_text = resume_text + "\n\n---\nAdditional Section:\n"
        optimized_text += f"This resume was optimized based on the job description. Fit Score: {fit_percent}%.\n"
        if missing_skills:
            optimized_text += "To improve fit, consider highlighting these skills: " + ", ".join(missing_skills)
        else:
            optimized_text += "Your resume already includes all the key skills for this role!"

        st.success("✅ Your updated resume is ready!")

        st.session_state.optimized_text = optimized_text  # Store in session
        st.session_state.show_preview = True
        st.session_state.fit_score = fit_percent
        st.session_state.missing_skills = missing_skills
    else:
        st.warning("Please upload a resume and enter job/scholarship details.")

# Preview and download
if st.session_state.get("show_preview"):
    st.subheader("Preview Your Resume")
    template = st.selectbox("Choose Template", ["Modern", "Classic", "Creative"])
    if st.button("Preview"):
        pdf_path = generate_pdf(st.session_state.optimized_text, template)
        with open(pdf_path, "rb") as f:
            st.download_button("📄 Download Resume", f, file_name="optimized_resume.pdf")