| """ |
| Angkit Sarma — Living CV (Gradio edition) |
| |
| An interactive resume with a self-querying "Ask the CV" panel. The retrieval |
| engine is a real TF-IDF + cosine-similarity ranker over the resume's own |
| content (scikit-learn) — the same underlying idea as the semantic-search |
| optimization work described in the Experience section below, just visible |
| and playable instead of buried in a bullet point. |
| """ |
|
|
| import gradio as gr |
| import numpy as np |
| import pandas as pd |
| import spaces |
| from sklearn.feature_extraction.text import TfidfVectorizer |
| from sklearn.metrics.pairwise import cosine_similarity |
|
|
| CORPUS = [ |
| {"tag": "Experience · Flexday AI", "text": "Built and pitched INFERA, an AI-powered agentic solution that maps sales projects to real-world opportunities, winning 1st place at the Flexday AI Hackathon."}, |
| {"tag": "Experience · Flexday AI", "text": "Optimized a semantic search system using LLM and embedding based techniques, improving retrieval performance by 50 percent and enhancing user experience."}, |
| {"tag": "Experience · Flexday AI", "text": "Designed and implemented automated AI assisted workflows across departments, cutting process cycle time by 5 percent and freeing up 10 or more hours per month."}, |
| {"tag": "Experience · Flexday AI", "text": "Implemented OCR based data extraction pipelines across diverse image collections, improving data extraction accuracy and processing efficiency."}, |
| {"tag": "Experience · Flexday AI", "text": "Identified and remediated critical application security vulnerabilities, mitigating million dollar risk exposure and strengthening system security."}, |
| {"tag": "Experience · Flexday AI", "text": "Streamlined CI/CD build pipelines by reducing redundant steps, accelerating deployment times, using Git, GitHub, Jira and Azure DevOps."}, |
| {"tag": "Experience · Leokraft", "text": "Designed, trained and deployed end to end machine learning models into production, improving model accuracy by 8 percent while cutting infrastructure costs by 20 percent."}, |
| {"tag": "Experience · Leokraft", "text": "Engineered a key target variable feature that improved model accuracy by 5 percent."}, |
| {"tag": "Experience · Leokraft", "text": "Developed an end to end application for managing model scores and usage statistics, driving higher stakeholder engagement."}, |
| {"tag": "Experience · CodingZen", "text": "Taught over 100 students full stack web development with Node.js, from foundational HTML and CSS to advanced backend engineering, while supervising a team of teaching staff."}, |
| {"tag": "Project · INFERA", "text": "INFERA is an agentic AI system built with large language models that autonomously analyzes sales pipeline data and maps projects to real world business opportunities. Won first place at the Flexday AI Hackathon."}, |
| {"tag": "Project · Analytica", "text": "Analytica is a full stack data analytics platform built with React, Python, Azure and SQL, showing machine learning model performance and usage statistics."}, |
| {"tag": "Project · Predictive Allocation", "text": "Predictive Allocation re-engineered an end to end model training and deployment pipeline on Azure SQL and Azure Blob, hardened with Snyk and Wiz security scanning, and modernized legacy code."}, |
| {"tag": "Skills · AI/ML", "text": "Core AI and machine learning skills include large language models, generative AI, agentic AI and AI agents, prompt engineering, semantic search, classification, regression, decision trees, and SMOTE."}, |
| {"tag": "Skills · Cloud & MLOps", "text": "Cloud and MLOps skills include Microsoft Azure, Azure SQL, Azure Blob Storage, serverless architecture, virtual machines, and CI/CD pipelines."}, |
| {"tag": "Skills · Programming", "text": "Programming languages and runtimes include Python, JavaScript and Node.js."}, |
| {"tag": "Certifications", "text": "Certifications include Building with the Claude API, Generative AI professional certificate, AI Fluency Framework and Foundations, Machine Learning for Leaders, and Analyze Box Office Data with Seaborn and Python."}, |
| {"tag": "Education", "text": "M.Tech in Information Technology from Tezpur University, graduated with distinction at 8.69 CGPA, with a full time AICTE scholarship. B.Tech in Computer Science and Engineering from KIET, first division."}, |
| ] |
|
|
| _texts = [c["text"] for c in CORPUS] |
| _vectorizer = TfidfVectorizer(stop_words="english") |
| _doc_matrix = _vectorizer.fit_transform(_texts) |
|
|
|
|
| def retrieve(query: str, top_k: int = 3): |
| if not query or not query.strip(): |
| return [] |
| q_vec = _vectorizer.transform([query]) |
| sims = cosine_similarity(q_vec, _doc_matrix)[0] |
| ranked_idx = np.argsort(sims)[::-1] |
| results = [] |
| for i in ranked_idx[:top_k]: |
| if sims[i] <= 0: |
| continue |
| results.append({**CORPUS[i], "score": float(sims[i])}) |
| return results |
|
|
|
|
| @spaces.GPU |
| def ask_the_cv(query, history): |
| history = history or [] |
| results = retrieve(query) |
|
|
| if not results: |
| answer = ("No strong match in the résumé index for that — try asking about " |
| "experience, projects, skills, or certifications.") |
| else: |
| top = results[0] |
| answer = f"**{top['tag']}** — {top['text']}" |
| if len(results) > 1: |
| answer += "\n\n**Also relevant:**\n" |
| for r in results[1:]: |
| answer += f"\n- _{r['tag']}_ ({r['score']*100:.0f}% match): {r['text']}" |
|
|
| history.append({"role": "user", "content": query}) |
| history.append({"role": "assistant", "content": answer}) |
| return history, "" |
|
|
|
|
| EXAMPLE_QUERIES = [ |
| "What is your experience with LLMs and generative AI?", |
| "Tell me about INFERA", |
| "What are your cloud and deployment skills?", |
| "Have you done any teaching or mentoring?", |
| "What certifications do you have?", |
| ] |
|
|
| SKILLS_DF = pd.DataFrame([ |
| {"Category": "AI/ML & GenAI", "Skills": "LLMs, Generative AI, Agentic AI, Prompt Engineering, Semantic Search, Classification, Regression, Decision Trees, SMOTE, OCR/NLP"}, |
| {"Category": "Programming", "Skills": "Python, JavaScript, Node.js"}, |
| {"Category": "Cloud & MLOps", "Skills": "Microsoft Azure, Azure SQL, Azure Blob, Serverless, Virtual Machines, CI/CD"}, |
| {"Category": "Tools & Practices", "Skills": "Git, GitHub, Jira, Azure DevOps, Snyk, Wiz, Agile"}, |
| ]) |
|
|
| IMPACT_DF = pd.DataFrame([ |
| {"Metric": "Hackathon placement", "Company": "Flexday AI (INFERA)", "Result": "🏆 1st"}, |
| {"Metric": "Semantic search retrieval improvement", "Company": "Flexday AI", "Result": "+50%"}, |
| {"Metric": "Infrastructure cost reduction", "Company": "Leokraft", "Result": "-20%"}, |
| {"Metric": "ML model accuracy improvement", "Company": "Leokraft", "Result": "+8%"}, |
| {"Metric": "Feature-engineering accuracy gain", "Company": "Leokraft", "Result": "+5%"}, |
| {"Metric": "Process cycle-time reduction", "Company": "Flexday AI", "Result": "-5%"}, |
| {"Metric": "Students taught (full-stack web dev)", "Company": "CodingZen", "Result": "100+"}, |
| ]) |
|
|
| CERTS_DF = pd.DataFrame([ |
| {"Certification": "Building with the Claude API", "Focus": "LLM application development"}, |
| {"Certification": "Generative AI (Professional Certificate)", "Focus": "Generative AI fundamentals"}, |
| {"Certification": "AI Fluency: Framework & Foundations", "Focus": "Applied AI literacy"}, |
| {"Certification": "Machine Learning for Leaders", "Focus": "ML strategy"}, |
| {"Certification": "Analyze Box Office Data with Seaborn and Python", "Focus": "Data analysis & visualization"}, |
| ]) |
|
|
| EDUCATION_DF = pd.DataFrame([ |
| {"Degree": "M.Tech, Information Technology", "Institution": "Tezpur University", "Years": "2019–2021", "Highlight": "Distinction, 8.69 CGPA, full AICTE scholarship"}, |
| {"Degree": "B.Tech, Computer Science & Engineering", "Institution": "KIET Group of Institutions", "Years": "2012–2016", "Highlight": "First Division"}, |
| ]) |
|
|
| |
| |
| |
| |
| CUSTOM_CSS = """ |
| @import url('https://fonts.googleapis.com/css2?family=Space+Grotesk:wght@500;600;700&family=Inter:wght@400;500;600&family=JetBrains+Mono:wght@400;500;600;700&display=swap'); |
| |
| :root { |
| --blue: #2563eb; |
| --blue-deep: #0ea5e9; |
| --cyan: #06b6d4; |
| --bg-0: #f7fafc; |
| --card: #ffffff; |
| --card-border: #e2e8f0; |
| --text-heading: #0f172a; |
| --text-dim: #475569; |
| --text-faint: #94a3b8; |
| } |
| |
| .gradio-container { |
| background: |
| radial-gradient(ellipse 900px 520px at 50% -8%, rgba(37,99,235,0.07) 0%, transparent 62%), |
| radial-gradient(ellipse 700px 600px at 100% 20%, rgba(6,182,212,0.06) 0%, transparent 55%), |
| radial-gradient(ellipse 1400px 900px at 50% 0%, #eef4ff 0%, var(--bg-0) 55%) !important; |
| background-attachment: fixed !important; |
| font-family: 'Inter', sans-serif !important; |
| position: relative; |
| } |
| .gradio-container::before { |
| content: ""; |
| position: fixed; inset: 0; pointer-events: none; z-index: 0; |
| background-image: |
| linear-gradient(rgba(15,23,42,0.03) 1px, transparent 1px), |
| linear-gradient(90deg, rgba(15,23,42,0.03) 1px, transparent 1px); |
| background-size: 48px 48px; |
| mask-image: radial-gradient(ellipse 1000px 600px at 50% 0%, black 0%, transparent 70%); |
| } |
| ::-webkit-scrollbar { width: 10px; height: 10px; } |
| ::-webkit-scrollbar-track { background: var(--bg-0); } |
| ::-webkit-scrollbar-thumb { background: #cbd5e1; border-radius: 8px; } |
| ::-webkit-scrollbar-thumb:hover { background: var(--blue); } |
| |
| @keyframes revealUp { |
| from { opacity: 0; transform: translateY(22px); } |
| to { opacity: 1; transform: translateY(0); } |
| } |
| #hero-wrap { |
| text-align: center; |
| max-width: 760px; |
| margin: 0 auto; |
| display: flex; |
| flex-direction: column; |
| align-items: center; |
| } |
| .status-badge, #hero-name, #hero-headline, #hero-bio, #hero-contact { |
| opacity: 0; |
| animation: revealUp 0.7s cubic-bezier(.16,1,.3,1) forwards; |
| } |
| .status-badge { animation-delay: 0.05s; } |
| #hero-name { animation-delay: 0.15s; } |
| #hero-headline { animation-delay: 0.28s; } |
| #hero-bio { animation-delay: 0.40s; } |
| #hero-contact { animation-delay: 0.52s; } |
| |
| #hero-name { |
| font-family: 'Space Grotesk', sans-serif !important; |
| font-weight: 700 !important; |
| font-size: 60px !important; |
| letter-spacing: -0.025em !important; |
| line-height: 1.05 !important; |
| margin-bottom: 6px !important; |
| background: linear-gradient(135deg, #0f172a 0%, #1d4ed8 60%, #06b6d4 100%); |
| -webkit-background-clip: text; |
| background-clip: text; |
| -webkit-text-fill-color: transparent; |
| } |
| #hero-headline { |
| display: inline-block; |
| color: #1d4ed8 !important; |
| font-family: 'Space Grotesk', sans-serif !important; |
| font-weight: 600 !important; |
| font-size: 18px !important; |
| margin-top: 10px !important; |
| padding: 6px 18px !important; |
| background: linear-gradient(90deg, rgba(37,99,235,0.08), rgba(6,182,212,0.08)); |
| border: 1px solid rgba(37,99,235,0.25); |
| border-radius: 999px; |
| } |
| .status-badge { |
| display: inline-flex; align-items: center; gap: 8px; |
| font-family: 'JetBrains Mono', monospace; font-size: 12px; letter-spacing: 0.12em; |
| color: var(--text-dim); text-transform: uppercase; margin-bottom: 14px; |
| } |
| .dot { |
| width: 8px; height: 8px; border-radius: 50%; |
| background: var(--blue); |
| box-shadow: 0 0 10px 2px rgba(37,99,235,0.55); |
| display: inline-block; animation: pulse 2.2s infinite; |
| } |
| @keyframes pulse { |
| 0% { box-shadow: 0 0 0 0 rgba(37,99,235,.45), 0 0 10px 2px rgba(37,99,235,0.55); } |
| 70% { box-shadow: 0 0 0 9px rgba(37,99,235,0), 0 0 10px 2px rgba(37,99,235,0.55); } |
| 100% { box-shadow: 0 0 0 0 rgba(37,99,235,0), 0 0 10px 2px rgba(37,99,235,0.55); } |
| } |
| #hero-contact { |
| display: flex !important; flex-wrap: wrap; justify-content: center; gap: 8px; align-items: center; |
| } |
| #hero-contact a { |
| color: var(--text-dim); text-decoration: none; |
| padding: 4px 12px; border-radius: 999px; |
| border: 1px solid var(--card-border); background: var(--card); |
| transition: all .18s ease; |
| } |
| #hero-contact a:hover { |
| color: #ffffff !important; |
| background: linear-gradient(90deg, var(--blue), var(--cyan)); |
| border-color: transparent; |
| } |
| |
| .section-label { |
| font-family: 'JetBrains Mono', monospace !important; color: var(--blue) !important; |
| letter-spacing: 0.12em !important; text-transform: uppercase; font-size: 12.5px !important; |
| font-weight: 600 !important; |
| } |
| |
| .ask-card { |
| background: var(--card) !important; |
| border: 1px solid var(--card-border) !important; |
| border-radius: 18px !important; |
| box-shadow: 0 20px 50px -24px rgba(15,23,42,0.18), 0 4px 12px -6px rgba(15,23,42,0.06) !important; |
| padding: 22px !important; |
| position: relative; |
| z-index: 1; |
| } |
| .ask-titlebar { |
| display: flex; align-items: center; gap: 8px; margin-bottom: 14px; |
| font-family: 'JetBrains Mono', monospace; font-size: 11.5px; color: var(--text-faint); |
| letter-spacing: 0.08em; |
| } |
| .ask-titlebar .tl-dot { width: 9px; height: 9px; border-radius: 50%; display: inline-block; } |
| |
| .gradio-container button { |
| transition: transform .16s ease, border-color .16s ease, box-shadow .16s ease, background .16s ease !important; |
| } |
| .gradio-container button:hover { transform: translateY(-2px); } |
| button.primary, button[class*="primary"] { |
| background: linear-gradient(135deg, var(--blue) 0%, var(--cyan) 100%) !important; |
| border: none !important; |
| box-shadow: 0 8px 20px -8px rgba(37,99,235,0.45) !important; |
| color: #ffffff !important; |
| font-weight: 700 !important; |
| } |
| button.primary:hover, button[class*="primary"]:hover { |
| box-shadow: 0 12px 28px -8px rgba(6,182,212,0.5) !important; |
| } |
| |
| .tabs > .tab-nav { |
| border-bottom: none !important; |
| background: var(--card); |
| border: 1px solid var(--card-border); |
| border-radius: 999px !important; |
| padding: 5px !important; |
| display: inline-flex !important; |
| gap: 2px !important; |
| margin-bottom: 18px; |
| box-shadow: 0 2px 8px -4px rgba(15,23,42,0.08); |
| } |
| .tabs > .tab-nav button { |
| font-family: 'JetBrains Mono', monospace !important; |
| font-size: 12.5px !important; |
| color: var(--text-dim) !important; |
| border: none !important; |
| background: transparent !important; |
| border-radius: 999px !important; |
| padding: 9px 18px !important; |
| margin: 0 !important; |
| } |
| .tabs > .tab-nav button.selected { |
| background: linear-gradient(135deg, var(--blue) 0%, var(--cyan) 100%) !important; |
| color: #ffffff !important; |
| font-weight: 700 !important; |
| box-shadow: 0 6px 16px -6px rgba(37,99,235,0.5); |
| } |
| |
| .proj-card { |
| background: var(--card) !important; |
| border: 1px solid var(--card-border) !important; |
| border-radius: 14px !important; |
| padding: 18px !important; |
| box-shadow: 0 2px 8px -4px rgba(15,23,42,0.06); |
| transition: transform .18s ease, border-color .18s ease, box-shadow .18s ease !important; |
| } |
| .proj-card:hover { |
| transform: translateY(-4px); |
| border-color: rgba(37,99,235,0.35) !important; |
| box-shadow: 0 16px 36px -18px rgba(37,99,235,0.28) !important; |
| } |
| |
| .gr-accordion, [class*="accordion"] { |
| border-radius: 14px !important; |
| } |
| |
| .cv-table { |
| border-radius: 14px !important; |
| overflow: hidden !important; |
| border: 1px solid var(--card-border) !important; |
| box-shadow: 0 12px 28px -20px rgba(15,23,42,0.2); |
| } |
| .cv-table table { font-size: 13.5px !important; } |
| .cv-table thead th { |
| font-family: 'JetBrains Mono', monospace !important; |
| text-transform: uppercase !important; |
| font-size: 10.5px !important; |
| letter-spacing: 0.08em !important; |
| color: var(--blue) !important; |
| background: rgba(37,99,235,0.06) !important; |
| } |
| .cv-table tbody tr:nth-child(even) { background: rgba(15,23,42,0.015) !important; } |
| .cv-table tbody tr:hover { background: rgba(37,99,235,0.05) !important; } |
| """ |
|
|
| with gr.Blocks(title="Angkit Sarma — Living CV") as demo: |
|
|
| gr.HTML( |
| """ |
| <div id="hero-wrap"> |
| <div class="status-badge"><span class="dot"></span> resume · live · self-querying</div> |
| <div id="hero-name">Angkit Sarma</div> |
| <div><span id="hero-headline">AI/ML Engineer — Agentic Systems & LLM Applications</span></div> |
| <p id="hero-bio" style="color:#475569; max-width:680px; margin-top:16px; line-height:1.6;"> |
| 4+ years building and shipping ML and generative AI systems — from a 50%-faster semantic |
| search pipeline to <b style="color:#0f172a">INFERA</b>, an agentic AI tool that won 1st place |
| at the Flexday AI Hackathon. Ask the panel below anything about my experience — it's a live |
| TF-IDF retrieval engine running over this résumé's own content. |
| </p> |
| <p id="hero-contact" style="font-family:'JetBrains Mono',monospace; font-size:12.5px; margin-top:16px;"> |
| <a href="tel:+919990797061">+91 9990797061</a> |
| <a href="mailto:angkit93@gmail.com">angkit93@gmail.com</a> |
| <a href="https://www.linkedin.com/in/angkit-s-81b7131b0/" target="_blank">LinkedIn</a> |
| <a href="https://github.com/angkit-hash" target="_blank">GitHub</a> |
| <a href="#" style="pointer-events:none;">Hyderabad, India</a> |
| </p> |
| </div> |
| """ |
| ) |
|
|
| gr.HTML('<p class="section-label" style="margin-top:8px;">// ask the cv</p>') |
| with gr.Group(elem_classes=["ask-card"]): |
| gr.HTML( |
| '<div class="ask-titlebar">' |
| '<span class="tl-dot" style="background:#ff5f57;"></span>' |
| '<span class="tl-dot" style="background:#febc2e;"></span>' |
| '<span class="tl-dot" style="background:#28c840;"></span>' |
| ' ask_the_cv.py — live TF-IDF retrieval, runs in this Space' |
| '</div>' |
| ) |
| chatbot = gr.Chatbot(label=None, height=320, show_label=False) |
| with gr.Row(): |
| query_box = gr.Textbox(placeholder="e.g. What's your experience with LLMs?", scale=5, show_label=False, container=False) |
| ask_btn = gr.Button("Ask", variant="primary", scale=1) |
|
|
| with gr.Row(): |
| for q in EXAMPLE_QUERIES: |
| gr.Button(q, size="sm").click(fn=ask_the_cv, inputs=[gr.State(q), chatbot], outputs=[chatbot, query_box]) |
|
|
| ask_btn.click(fn=ask_the_cv, inputs=[query_box, chatbot], outputs=[chatbot, query_box]) |
| query_box.submit(fn=ask_the_cv, inputs=[query_box, chatbot], outputs=[chatbot, query_box]) |
|
|
| with gr.Tabs(): |
| with gr.Tab("Overview"): |
| gr.Markdown( |
| "Results-driven AI/ML Engineer with 4+ years designing, training, and deploying machine " |
| "learning and generative AI systems that solve real business problems. Proven track record " |
| "building agentic AI and LLM-powered applications, optimizing semantic search and NLP " |
| "pipelines, and automating end-to-end ML workflows from data processing to production " |
| "deployment. Combines strong ML engineering fundamentals with cloud deployment, MLOps, and " |
| "application security expertise to ship secure, scalable, high-impact AI solutions." |
| ) |
| gr.HTML('<p class="section-label" style="margin-top:22px;">// impact metrics</p>') |
| gr.Dataframe( |
| value=IMPACT_DF, interactive=False, wrap=True, |
| column_widths=["55%", "25%", "20%"], |
| row_count=(len(IMPACT_DF), "fixed"), |
| elem_classes=["cv-table"], |
| ) |
|
|
| with gr.Tab("Experience"): |
| with gr.Accordion("Software Developer (AI/ML Focus) — Flexday AI, Hyderabad · Nov 2022 – Present", open=True): |
| gr.Markdown( |
| "- Built and pitched **INFERA**, an AI-powered agentic solution that maps sales projects " |
| "to real-world opportunities — 1st place at the Flexday AI Hackathon\n" |
| "- Optimized a semantic search system using LLM/embedding-based techniques, improving " |
| "retrieval performance by **50%**\n" |
| "- Designed automated, AI-assisted workflows across departments, cutting process cycle " |
| "time by 5% and freeing 10+ hours/month\n" |
| "- Implemented OCR-based data extraction pipelines across diverse image collections\n" |
| "- Identified and remediated critical security vulnerabilities, mitigating million-dollar " |
| "risk exposure\n" |
| "- Streamlined CI/CD build pipelines using Git, GitHub, Jira, and Azure DevOps" |
| ) |
| with gr.Accordion("Machine Learning Engineer — Leokraft Technologies, Bangalore · Dec 2021 – Dec 2022", open=False): |
| gr.Markdown( |
| "- Designed, trained, and deployed end-to-end ML models into production, improving " |
| "accuracy by **8%** while cutting infrastructure costs by **20%**\n" |
| "- Engineered a key target-variable feature that improved model accuracy by 5%\n" |
| "- Built an end-to-end application for managing model scores and usage statistics" |
| ) |
| with gr.Accordion("Senior Faculty — CodingZen, Delhi · Jul 2018 – Jul 2019", open=False): |
| gr.Markdown( |
| "- Taught 100+ students full-stack web development with Node.js\n" |
| "- Supervised and mentored a team of teaching staff" |
| ) |
|
|
| with gr.Tab("Projects"): |
| with gr.Row(): |
| with gr.Column(elem_classes=["proj-card"]): |
| gr.Markdown("**INFERA** — Agentic AI Sales-Opportunity Mapper\n\n*LLMs · AI Agents · Python*\n\nAn AI agent that analyzes sales pipeline data and autonomously maps projects to real-world business opportunities.\n\n🏆 1st Place — Flexday AI Hackathon") |
| with gr.Column(elem_classes=["proj-card"]): |
| gr.Markdown("**Analytica** — End-to-End ML Analytics Platform\n\n*React · Python · Azure · SQL*\n\nFull-stack analytics tool surfacing ML model performance metrics and usage statistics.") |
| with gr.Column(elem_classes=["proj-card"]): |
| gr.Markdown("**Predictive Allocation** — ML Deployment Pipeline\n\n*Python · Azure SQL · Azure Blob · DevOps*\n\nRe-engineered end-to-end training/deployment pipeline; hardened security with Snyk and Wiz.") |
| gr.HTML( |
| '<p style="font-family:\'JetBrains Mono\',monospace; font-size:13px; margin-top:14px;">' |
| '<a href="https://github.com/angkit-hash" target="_blank" style="color:#0ea5e9; text-decoration:none; border-bottom:1px solid #0ea5e9;">' |
| '→ View source & more projects on GitHub</a></p>' |
| ) |
|
|
| with gr.Tab("Skills"): |
| gr.Dataframe( |
| value=SKILLS_DF, interactive=False, wrap=True, |
| column_widths=["25%", "75%"], |
| row_count=(len(SKILLS_DF), "fixed"), |
| elem_classes=["cv-table"], |
| ) |
|
|
| with gr.Tab("Certifications & Education"): |
| gr.HTML('<p class="section-label">Certifications</p>') |
| gr.Dataframe( |
| value=CERTS_DF, interactive=False, wrap=True, |
| column_widths=["55%", "45%"], |
| row_count=(len(CERTS_DF), "fixed"), |
| elem_classes=["cv-table"], |
| ) |
| gr.HTML('<p class="section-label" style="margin-top:22px;">Education</p>') |
| gr.Dataframe( |
| value=EDUCATION_DF, interactive=False, wrap=True, |
| column_widths=["30%", "28%", "14%", "28%"], |
| row_count=(len(EDUCATION_DF), "fixed"), |
| elem_classes=["cv-table"], |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch(css=CUSTOM_CSS, theme=gr.themes.Base(primary_hue="blue", neutral_hue="slate")) |
|
|
