# app.py
import gradio as gr
import pdfplumber
import spacy
import os
import json
import matplotlib.pyplot as plt
import tempfile
import re
from wordcloud import WordCloud

# Load NLP model
try:
    nlp = spacy.load("en_core_web_sm")
except:
    from spacy.cli import download
    download("en_core_web_sm")
    nlp = spacy.load("en_core_web_sm")

# --- Helper functions ---
def extract_text_from_pdf(file):
    with pdfplumber.open(file) as pdf:
        text = "\n".join([page.extract_text() or "" for page in pdf.pages])
    return clean_text(text)

def clean_text(text):
    import re
    text = re.sub(r'\n+', '\n', text)
    text = re.sub(r'[^\x00-\x7F]+', ' ', text)  # remove non-ASCII
    text = re.sub(r'\s+', ' ', text)  # collapse multiple spaces
    return text.strip()

def extract_contact_info(text):
    email = re.search(r"[\w.+-]+@[\w-]+\.[\w.-]+", text)
    phone = re.search(r"\b\d{4}[-.\s]?\d{7}\b|\b\d{11}\b", text)
    return email.group(0) if email else None, phone.group(0) if phone else None

def extract_skills(text):
    words = set(re.findall(r"\b\w+\b", text.lower()))
    custom_keywords = re.findall(r"(?i)(?<=skills[:\-\n]).+?(?=education|experience|projects|certification|$)", text, re.DOTALL)
    skill_keywords = [
        "python", "sql", "ml", "ai", "keras", "tensorflow", "html", "css", "nltk",
        "pandas", "numpy", "power bi", "excel", "scikit-learn", "seaborn", "flask", "django"
    ]
    found = {k for k in skill_keywords if k in words}
    for chunk in custom_keywords:
        found.update([s.strip().lower() for s in re.split(r",|\n|\||\s{2,}", chunk) if len(s.strip()) >= 2])
    return sorted(set(found))

def extract_section(text, section_titles):
    lines = text.split("\n")
    section_data = []
    capture = False

    for line in lines:
        clean_line = line.strip()

        if not clean_line:
            continue

        # Detect section start
        if any(title.lower() in clean_line.lower() for title in section_titles):
            capture = True
            continue

        # Stop if a new main section starts
        if capture and re.match(r"^(skills|certifications|projects|summary|languages|contact|references|achievements|publications)\s*[:\-]?$", clean_line, re.IGNORECASE):
            break

        # Collect section lines
        if capture:
            section_data.append(clean_line)

    return section_data


def match_job_description(skills, job_desc):
    job_desc = job_desc.lower()
    matched_skills = [s for s in skills if s.lower() in job_desc]
    return matched_skills, len(matched_skills), len(skills)

def visualize_skills(skills):
    if not skills:
        return None
    plt.figure(figsize=(6, 4))
    freq = {s: skills.count(s) for s in skills}
    plt.bar(freq.keys(), freq.values(), color="skyblue")
    plt.xticks(rotation=45)
    plt.title("Skills Frequency")
    plt.tight_layout()
    temp_chart = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
    plt.savefig(temp_chart.name)
    return temp_chart.name

def parse_resume(file):
    text = extract_text_from_pdf(file.name)
    doc = nlp(text)
    name = next((ent.text for ent in doc.ents if ent.label_ == "PERSON"), "Not Found")
    email, phone = extract_contact_info(text)
    skills = extract_skills(text)
    education = extract_section(text, ["education", "academic background", "qualification", "university", "degree", "bachelor", "master"])
    experience = extract_section(text, ["experience", "professional experience", "work history", "employment", "internship", "projects"])

    print("=== DEBUG TEXT ===")
    print(text)


    parsed = {
        "Name": name,
        "Email": email,
        "Phone": phone,
        "Skills": skills,
        "Education": education,
        "Experience": experience,
    }

    chart = visualize_skills(skills)
    return parsed, chart

def download_json(data):
    with tempfile.NamedTemporaryFile(delete=False, suffix=".json", mode="w") as f:
        json.dump(data, f, indent=4)
        return f.name

def job_matcher(resume_json, job_desc):
    skills = resume_json.get("Skills", [])
    matched_skills, matched_count, total_skills = match_job_description(skills, job_desc)
    return f"✅ Matched Skills: {matched_skills}\n🎯 Match Score: {matched_count} / {total_skills}"

# --- Gradio Interface ---
with gr.Blocks(title="🧠 AI Resume Parser") as demo:
    gr.Markdown("""
    # 🧠 AI Resume Parser (with Skills Chart, JSON Download, and Job Match)
    Upload a resume and extract structured data with NLP
    """)

    with gr.Row():
        resume_input = gr.File(label="Upload Resume (PDF)", file_types=[".pdf"])
        parse_btn = gr.Button("📄 Parse Resume")

    parsed_output = gr.JSON(label="🧾 Parsed Resume")
    chart_output = gr.Image(label="📊 Skills Chart")
    download_btn = gr.File(label="⬇️ Download Resume JSON")

    with gr.Accordion("📌 Match with Job Description", open=False):
        job_desc_input = gr.Textbox(label="Paste Job Description", lines=5)
        match_btn = gr.Button("🔍 Match Skills")
        match_output = gr.Text(label="Match Result")

    def full_pipeline(file):
        parsed, chart_path = parse_resume(file)
        json_path = download_json(parsed)
        return parsed, chart_path, json_path

    parse_btn.click(fn=full_pipeline, inputs=[resume_input], outputs=[parsed_output, chart_output, download_btn])
    match_btn.click(fn=job_matcher, inputs=[parsed_output, job_desc_input], outputs=[match_output])

if __name__ == "__main__":
    demo.launch()