app.py

import gradio as gr
from transformers import pipeline
import re
import os
from huggingface_hub import login

# ✅ Authenticate using HF token stored in secret
login(token=os.environ.get("HUGGINGFACEHUB_API_TOKEN"))

# ✅ Use a lightweight instruction-tuned model compatible with CPU
summarizer = pipeline("text2text-generation", model="declare-lab/flan-alpaca-base")

# ✅ Highlight matching and find missing keywords
from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS

def compare_keywords(resume_text, job_desc):
    resume_words = set(re.findall(r"\b\w{3,}\b", resume_text.lower())) - ENGLISH_STOP_WORDS
    job_words = set(re.findall(r"\b\w{3,}\b", job_desc.lower())) - ENGLISH_STOP_WORDS
    matched = resume_words & job_words
    missing = job_words - resume_words
    return matched, missing

def highlight_keywords(resume_text, matched):
    highlighted = resume_text
    for word in sorted(matched, key=len, reverse=True):
        highlighted = re.sub(rf"\b({re.escape(word)})\b", r"**\1**", highlighted, flags=re.IGNORECASE)
    return highlighted

# 🔍 Use LLM to extract missing keywords contextually from JD
def extract_missing_keywords_with_llm(job_desc, resume_text):
    prompt = f"""
Given the following job description and resume, list the important skills, tools, and concepts from the job description that are missing or weakly represented in the resume.

Job Description:
{job_desc}

Resume:
{resume_text}

Only list the missing keywords as bullet points.
"""
    result = summarizer(prompt, max_new_tokens=300, do_sample=True)[0]['generated_text']
    return result.strip()

# 🔍 Prompt for dynamic section classification and feedback
def build_dynamic_prompt(job_desc, resume_text, analyze_with_jd):
    prompt = f"""
Analyze the resume below and organize it into meaningful categories (e.g., Skills, Education, Work Experience, etc.).

If a job description is provided, compare it against the resume and suggest improvements section by section.

Job Description:
{job_desc if analyze_with_jd else '[None provided]'}

Resume:
{resume_text}

Return structured Markdown with headers for each section and improvement suggestions.
"""
    return prompt

# 🧠 Function to call Hugging Face model and get structured resume feedback
def analyze_resume(job_desc, resume_text, analyze_with_jd):
    if not resume_text.strip():
        return "⚠️ Please paste your resume text."

    user_prompt = build_dynamic_prompt(job_desc, resume_text, analyze_with_jd)

    try:
        response = summarizer(user_prompt, max_new_tokens=512, do_sample=True)[0]['generated_text']
        if analyze_with_jd and job_desc:
            matched, _ = compare_keywords(resume_text, job_desc)
            highlighted_resume = highlight_keywords(resume_text, matched)
            llm_missing_keywords = extract_missing_keywords_with_llm(job_desc, resume_text)
            return f"### 🔍 Resume with Highlighted Matches\n\n{highlighted_resume}\n\n---\n**✅ Matched Keywords:** {', '.join(sorted(matched)) or 'None'}\n\n**❌ LLM-Inferred Missing Keywords:**\n{llm_missing_keywords}\n\n---\n{response.strip()}"
        return response.strip()
    except Exception as e:
        return f"❌ Error: {str(e)}"

# 🎛️ Gradio UI
def create_ui():
    with gr.Blocks() as demo:
        with gr.Row():
            with gr.Column():
                analyze_checkbox = gr.Checkbox(label="Analyze with Job Description", value=True)
                job_desc = gr.Textbox(label="Job Description", lines=6, placeholder="Paste job description here...")
                resume_text = gr.Textbox(label="Resume Text", lines=16, placeholder="Paste resume content here...")
            with gr.Column():
                output_analysis = gr.Markdown(label="Resume Analysis and Suggestions")

        resume_text.change(fn=analyze_resume, inputs=[job_desc, resume_text, analyze_checkbox], outputs=output_analysis)
        job_desc.change(fn=analyze_resume, inputs=[job_desc, resume_text, analyze_checkbox], outputs=output_analysis)

    return demo

if __name__ == '__main__':
    create_ui().launch()