import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
from convert import ExtractPDFText
from ATS_score import calculateATSscore, skill_gap_analysis
from model import modelFeedback
import time

# Streamlit app title
st.title("Resume Screening Assistance")

# Define required skills for the job
required_skills = ["Python", "Machine Learning", "Cloud (AWS/Azure)", "Data Analysis", "React", "Docker"]

# Job Description Input
job_description = st.text_area("Paste the job description below:")

# Upload Resumes
uploaded_files = st.file_uploader("Upload your resumes (PDF only):", type="pdf", accept_multiple_files=True)

if uploaded_files and job_description:
    resumes_data = []
    for file in uploaded_files:
        text = ExtractPDFText(file)
        ats_score = calculateATSscore(text, job_description)
        skill_analysis = skill_gap_analysis(text, required_skills)
        feedback = modelFeedback(ats_score, text, skill_analysis['missing'])
        resumes_data.append({
            "name": file.name,
            "ATS Score": ats_score,
            "Skills Present": skill_analysis['present'],
            "Missing Skills": skill_analysis['missing'],
            "Feedback": feedback
        })

    # Sort resumes by ATS score
    sorted_resumes = sorted(resumes_data, key=lambda x: x["ATS Score"], reverse=True)
    
    # Display results
    st.subheader("Top Matches:")
    for idx, resume in enumerate(sorted_resumes, 1):
        st.write(f"### {idx}. {resume['name']}")
        st.write(f"**ATS Score:** {resume['ATS Score']*100:.0f}%")
        st.write("**Missing Skills:**", ", ".join(resume["Missing Skills"]) if resume["Missing Skills"] else "None")
        st.write("**Feedback:**", resume["Feedback"])
        st.write("---")

    # Skill Gap Analysis Table
    st.subheader("Skill Gap Analysis")
    for resume in sorted_resumes:
        st.write(f"**{resume['name']}**")
        skill_data = {
            "Required Skills": required_skills,
            "Present in Resume": ["✅" if skill in resume["Skills Present"] else "❌" for skill in required_skills],
            "Missing": [skill if skill in resume["Missing Skills"] else "—" for skill in required_skills]
        }
        df = pd.DataFrame(skill_data)
        st.table(df)

    # Visualize ATS Scores
    st.subheader("ATS Score Distribution")
    names = [r["name"] for r in sorted_resumes]
    scores = [r["ATS Score"]*100 for r in sorted_resumes]
    plt.figure(figsize=(8, 5))
    plt.bar(names, scores)
    plt.title("ATS Scores by Resume")
    plt.ylabel("ATS Score (%)")
    plt.xticks(rotation=45)
    st.pyplot(plt)