""" MATCH.AI — AI Resume Screener & Feedback System Hugging Face Gradio Space Entry Point """ import os import gradio as gr try: import spaces except ImportError: class spaces: @staticmethod def GPU(fn): return fn from resume_scanner.assessor import ResumeAssessor from resume_scanner.extractor import extract_text_from_file, prepare_scanner_inputs @spaces.GPU def scan_resume_gradio(resume_file, jd_file, jd_text) -> tuple: if not resume_file: return ( "⚠️ Error: Please upload a candidate resume file (.pdf, .docx, or .txt).", 0, "No evaluation rationale.", "", "", "", ) try: resume_path = resume_file.name jd_path = jd_file.name if jd_file else resume_path if jd_file: resume_extracted, jd_extracted = prepare_scanner_inputs(resume_path, jd_path) elif jd_text and jd_text.strip(): resume_extracted = extract_text_from_file(resume_path, label="Resume") jd_extracted = jd_text.strip() else: return ( "⚠️ Error: Please provide either a Job Description file or paste Job Description text.", 0, "", "", "", "", ) assessor = ResumeAssessor() result = assessor.assess(resume_extracted, jd_extracted) score = result.match_score rationale = result.score_rationale matched = "\n".join([f"✅ {item}" for item in result.matched_requirements]) or "None" missing = "\n".join([f"⚠️ {item}" for item in result.missing_requirements]) or "None" suggestions = "\n".join([f"💡 {item}" for item in result.suggestions]) or "None" status_msg = f"🎉 Scan Complete! Match Score: {score}/100" return status_msg, score, rationale, matched, missing, suggestions except Exception as exc: return f"❌ Error during assessment: {str(exc)}", 0, str(exc), "", "", "" def run_demo_gradio() -> tuple: try: sample_resume = "data/sample_resume.txt" sample_jd = "data/sample_jd.txt" resume_extracted, jd_extracted = prepare_scanner_inputs(sample_resume, sample_jd) assessor = ResumeAssessor() result = assessor.assess(resume_extracted, jd_extracted) score = result.match_score rationale = result.score_rationale matched = "\n".join([f"✅ {item}" for item in result.matched_requirements]) or "None" missing = "\n".join([f"⚠️ {item}" for item in result.missing_requirements]) or "None" suggestions = "\n".join([f"💡 {item}" for item in result.suggestions]) or "None" return ( f"⚡ 1-Click Demo Assessment Complete! Score: {score}/100", score, rationale, matched, missing, suggestions, ) except Exception as exc: return f"❌ Demo error: {str(exc)}", 0, str(exc), "", "", "" CUSTOM_CSS = """ .gradio-container { background: radial-gradient(circle at 10% 20%, rgb(11, 15, 25) 0%, rgb(17, 24, 39) 90%) !important; color: #f3f4f6 !important; font-family: 'Inter', system-ui, -apple-system, sans-serif !important; } .hero-banner { background: linear-gradient(135deg, rgba(6, 182, 212, 0.15) 0%, rgba(139, 92, 246, 0.15) 100%); border: 1px solid rgba(6, 182, 212, 0.3); border-radius: 16px; padding: 24px; margin-bottom: 20px; box-shadow: 0 8px 32px 0 rgba(0, 0, 0, 0.37); backdrop-filter: blur(12px); } .hero-title { background: linear-gradient(135deg, #22d3ee 0%, #c084fc 100%); -webkit-background-clip: text; -webkit-text-fill-color: transparent; font-size: 2.2rem; font-weight: 800; margin-bottom: 8px; } button.primary { background: linear-gradient(135deg, #06b6d4 0%, #8b5cf6 100%) !important; border: none !important; color: white !important; font-weight: 700 !important; box-shadow: 0 0 15px rgba(6, 182, 212, 0.4) !important; transition: all 0.3s ease !important; } button.primary:hover { transform: translateY(-2px) !important; box-shadow: 0 0 25px rgba(139, 92, 246, 0.7) !important; } """ with gr.Blocks( title="MATCH.AI — Executive AI Resume Screener Studio", theme=gr.themes.Base( primary_hue="cyan", secondary_hue="purple", neutral_hue="slate", font=[gr.themes.GoogleFont("Inter"), "ui-sans-serif", "system-ui"], ), css=CUSTOM_CSS, ) as demo: gr.HTML('''
🚀 MATCH.AI — Executive AI Resume Screener Studio

Enterprise deterministic AI screening powered by Google Gemini Flash & Pydantic AI. Equipped with Hallucination Grounding Verification & Zero-Bias Structured Analysis.

''') with gr.Row(): with gr.Column(scale=5): gr.Markdown("### 📄 Candidate Documents") resume_input = gr.File( label="Upload Candidate Resume (.pdf, .docx, .doc, .txt)", file_types=[".pdf", ".docx", ".doc", ".txt"], ) jd_input = gr.File( label="Upload Job Description (.pdf, .docx, .doc, .txt) [Optional if pasting text below]", file_types=[".pdf", ".docx", ".doc", ".txt"], ) jd_text_input = gr.Textbox( label="Or Paste Job Description Text", lines=5, placeholder="Paste job requirements here...", ) with gr.Row(): scan_btn = gr.Button("⚡ Run AI Scan", variant="primary") demo_btn = gr.Button("🚀 1-Click Sample Demo", variant="secondary") with gr.Column(scale=6): gr.Markdown("### 📊 AI Executive Assessment Results") status_output = gr.Textbox(label="Status", interactive=False) score_output = gr.Number(label="Match Score (0 - 100)", interactive=False) rationale_output = gr.Textbox( label="Executive Evaluation Rationale", lines=4, interactive=False ) with gr.Row(): matched_output = gr.Textbox( label="✅ Matched Qualifications", lines=5, interactive=False ) missing_output = gr.Textbox( label="⚠️ Identified Skill Gaps", lines=5, interactive=False ) suggestions_output = gr.Textbox( label="💡 Actionable Presentation Suggestions", lines=4, interactive=False ) scan_btn.click( fn=scan_resume_gradio, inputs=[resume_input, jd_input, jd_text_input], outputs=[ status_output, score_output, rationale_output, matched_output, missing_output, suggestions_output, ], show_api=False, ) demo_btn.click( fn=run_demo_gradio, inputs=[], outputs=[ status_output, score_output, rationale_output, matched_output, missing_output, suggestions_output, ], show_api=False, ) if __name__ == "__main__": demo.launch(server_name="0.0.0.0", server_port=7860, show_api=False)