app.py

import os
import json
import pandas as pd
import gradio as gr
from langgraph_agent import LangGraphResumeAnalyzer
import utils

# Instantiate LangGraph Agent
analyzer = LangGraphResumeAnalyzer()

# FIXED CSS preventing vibrating / trembling layout bug by locking vertical scrollbar gutter permanently
CUSTOM_CSS = """
html {
    overflow-y: scroll !important;
    scroll-behavior: smooth !important;
}

body {
    background-color: #090d16 !important;
    font-family: 'Inter', system-ui, -apple-system, sans-serif !important;
    color: #e2e8f0 !important;
    margin: 0 !important;
    padding: 0 !important;
    min-height: 100vh !important;
}

.gradio-container {
    background-color: #090d16 !important;
    max-width: 1400px !important;
    margin: 0 auto !important;
    padding: 20px !important;
    width: 100% !important;
    box-sizing: border-box !important;
}

.card-panel {
    background: linear-gradient(145deg, #131b2e, #0f172a);
    border: 1px solid rgba(56, 189, 248, 0.2);
    border-radius: 16px;
    padding: 20px;
    box-shadow: 0 8px 32px rgba(0,0,0,0.4);
    height: auto !important;
    contain: content;
}

.btn-primary-audit {
    background: linear-gradient(135deg, #0284c7, #4f46e5) !important;
    color: white !important;
    font-weight: 800 !important;
    border-radius: 12px !important;
    font-size: 1.1rem !important;
    box-shadow: 0 4px 14px rgba(2, 132, 199, 0.4) !important;
}
"""

def handle_run_audit(file_obj, text_resume, job_desc):
    """
    Executes LangGraph pipeline for PDF documents or images via NVIDIA Nemotron OCR & PyPDF.
    """
    res = analyzer.run_langgraph_pipeline(
        file_input=file_obj,
        text_input=text_resume,
        job_description=job_desc
    )
    
    analysis = res["analysis"]
    ocr_info = res["ocr_result"]
    
    overall_score = analysis.get("overall_ats_score_pct", 85)
    ats_gauge_html = utils.generate_ats_score_html(overall_score)
    
    subscores_html = utils.generate_subscores_html(
        keyword_pct=analysis.get("keyword_match_pct", 82),
        skills_pct=analysis.get("skills_match_pct", 88),
        experience_pct=analysis.get("experience_fit_pct", 85),
        format_pct=analysis.get("format_quality_pct", 90)
    )
    
    skill_badges_html = utils.format_skill_badges(
        analysis.get("matched_skills", []),
        analysis.get("missing_skills", [])
    )
    
    contact = analysis.get("contact_info", {})
    summary_md = f"""
### 👤 Candidate Profile & Key Info
- **Full Name**: `{analysis.get('candidate_name', 'Alex Chen')}`
- **Estimated Experience**: `{analysis.get('estimated_years_experience', '6+ Years')}`
- **Email**: `{contact.get('email', 'N/A')}` | **Location**: `{contact.get('location', 'N/A')}`

#### 📝 Executive Recruiter Assessment
{analysis.get('executive_summary', 'No summary available.')}

#### 🎯 Key Candidate Strengths
"""
    for strg in analysis.get("key_strengths", []):
        summary_md += f"- **{strg}**\n"

    summary_md += "\n#### 💡 Actionable Recommendations to Boost ATS Score to 98%+\n"
    for tip in analysis.get("improvement_tips", []):
        summary_md += f"- {tip}\n"

    # AI Tailored Resume Rewriter Bullets
    bullets_md = "### ✍️ AI Tailored Resume Bullet Point Rewriter\n*Copy & paste these optimized bullets into your resume to maximize ATS callback rates:*\n\n"
    for idx, bullet in enumerate(analysis.get("optimized_resume_bullets", [])):
        bullets_md += f"**{idx+1}.** `{bullet}`\n\n"

    # OCR Info
    ocr_meta_md = f"""
### 👁️ NVIDIA Nemotron OCR & PDF Detection Engine
- **Engine Used**: `{ocr_info.get('model_used', 'NVIDIA Nemotron OCR v2')}`
- **Total Lines Extracted**: `{ocr_info.get('line_count', 0)}`
- **Extraction Status**: `{ocr_info.get('status', 'SUCCESS')}`
"""
    
    detections_list = ocr_info.get("detections", [])
    df_det = pd.DataFrame(detections_list) if detections_list else pd.DataFrame(columns=["text", "confidence"])

    full_report_json = json.dumps({
        "candidate": analysis.get('candidate_name'),
        "overall_ats_score_pct": overall_score,
        "subscores": {
            "keywords": analysis.get("keyword_match_pct"),
            "skills": analysis.get("skills_match_pct"),
            "experience": analysis.get("experience_fit_pct"),
            "formatting": analysis.get("format_quality_pct")
        },
        "ocr_engine": ocr_info.get('model_used'),
        "analysis": analysis
    }, indent=2)

    return (
        ats_gauge_html,
        subscores_html,
        skill_badges_html,
        summary_md,
        bullets_md,
        res["timeline"],
        ocr_meta_md,
        df_det,
        res["resume_text"],
        full_report_json
    )

def handle_rag_chat(user_question: str):
    return analyzer.answer_rag_question(user_question)

with gr.Blocks(title="AI Resume Analyzer (RAG + LangGraph)") as demo:
    gr.Markdown(
        """
        # 📄 Advanced AI Resume Analyzer (RAG + LangGraph)
        ### Multimodal PDF & Image OCR via NVIDIA Nemotron OCR v2/v1 & Groq LLM ATS Auditor
        **Engineer / Creator:** `abersabil` (`@abersbail`) | **User ID:** `69b2ede7cec72416131a3260`
        """
    )

    with gr.Tabs():
        # TAB 1: LangGraph ATS Audit & Matching
        with gr.TabItem("📊 LangGraph ATS Audit & Matching"):
            with gr.Row():
                with gr.Column(scale=1, elem_classes=["card-panel"]):
                    gr.Markdown("### 📥 Upload PDF / Image Resume & Job Description")
                    
                    resume_file_input = gr.File(
                        label="Upload Resume File (.pdf, .png, .jpg, .jpeg, .webp)",
                        file_types=[".pdf", ".png", ".jpg", ".jpeg", ".webp"]
                    )
                    
                    resume_text_area = gr.Textbox(
                        value=utils.DEFAULT_SAMPLE_RESUME,
                        label="OR Paste Resume Text directly",
                        lines=7
                    )
                    
                    job_desc_area = gr.Textbox(
                        value=utils.DEFAULT_JOB_DESCRIPTION,
                        label="Target Job Description (JD)",
                        lines=5
                    )
                    
                    btn_audit = gr.Button("🚀 Run LangGraph RAG Audit", elem_classes=["btn-primary-audit"])
                    langgraph_timeline = gr.Textbox(label="LangGraph State Machine Stream", lines=6, interactive=False)

                with gr.Column(scale=1):
                    ats_score_gauge = gr.HTML(utils.generate_ats_score_html(88))
                    ats_subscores_box = gr.HTML(utils.generate_subscores_html(85, 90, 85, 95))
                    skill_badges_box = gr.HTML("Click 'Run LangGraph RAG Audit' to analyze candidate skills.")
                    executive_summary_box = gr.Markdown("Candidate evaluation summary will appear here.")
                    tailored_bullets_box = gr.Markdown("Optimized resume bullets will appear here.")

        # TAB 2: NVIDIA Nemotron OCR Scanner View
        with gr.TabItem("👁️ NVIDIA Nemotron OCR Inspector"):
            with gr.Row():
                with gr.Column(scale=1):
                    ocr_metadata_box = gr.Markdown("Run audit to view NVIDIA Nemotron OCR v2/v1 detection metrics.")
                    ocr_detections_df = gr.Dataframe(label="Detected Lines & Confidence Scores")
                with gr.Column(scale=1):
                    gr.Markdown("### 📄 Extracted Resume Raw Text")
                    ocr_raw_text_box = gr.Textbox(lines=18, interactive=False)

        # TAB 3: LangGraph RAG Candidate Chatbot
        with gr.TabItem("💬 Candidate RAG Chatbot"):
            gr.Markdown("### 🤖 Ask RAG Questions About Candidate Qualifications")
            with gr.Row():
                with gr.Column(scale=1):
                    rag_question_input = gr.Textbox(
                        label="Type Question about Candidate",
                        placeholder="e.g., What PyTorch & RAG experience does the candidate have?",
                        lines=2
                    )
                    btn_ask_rag = gr.Button("🔍 Query RAG Vector Index", variant="primary")
                with gr.Column(scale=1):
                    rag_answer_output = gr.Textbox(label="Grounded RAG Answer", lines=8, interactive=False)

        # TAB 4: Full Audit Report Export
        with gr.TabItem("📝 Candidate Report Export"):
            gr.Markdown("### 📑 Downloadable Candidate Evaluation JSON Report")
            full_report_code = gr.Code(language="json", label="JSON Candidate Audit Report")

    # Event Bindings
    btn_audit.click(
        fn=handle_run_audit,
        inputs=[resume_file_input, resume_text_area, job_desc_area],
        outputs=[
            ats_score_gauge, ats_subscores_box, skill_badges_box,
            executive_summary_box, tailored_bullets_box, langgraph_timeline,
            ocr_metadata_box, ocr_detections_df, ocr_raw_text_box, full_report_code
        ]
    )

    btn_ask_rag.click(
        fn=handle_rag_chat,
        inputs=[rag_question_input],
        outputs=[rag_answer_output]
    )

if __name__ == "__main__":
    demo.queue()
    demo.launch(server_name="0.0.0.0", server_port=7860, css=CUSTOM_CSS)