Seed professional demo data; fix duplicate LUNA avatar on interview

- interview.html: reuse the placeholder avatar/bubble for the first
question instead of appending a second LUNA avatar, so only the real
question shows. Follow-up questions and talking animation unchanged.
- database.py: add idempotent seed_demo_data() (demo recruiter, demo
applicant, and 5 curated tech jobs owned by the recruiter) run on
startup, so the listing looks polished and the full demo flow works
after each ephemeral-DB restart.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

backend/models/database.py

@@ -112,12 +112,144 @@ def init_db(app):
             # ``num_questions`` column.
             pass
 
-        # Database tables are created on application start.  We intentionally do not
-        # seed any sample data here.  Previously a block of code inserted dummy
-        # job listings whenever the jobs table was empty.  In production, jobs
-        # should only be added by authenticated recruiters via the job posting
-        # interface.  Leaving the seeding logic in place would result in fake
-        # positions appearing every time the application starts, which is
-        # undesirable for a live recruitment platform.  If your environment
-        # requires initial data for testing, insert it manually via the
-        # database or through the new recruiter job posting page.
+        # Seed professional demo data (recruiter + applicant + a curated set of
+        # jobs) so the platform always looks polished and the full demo flow
+        # works after every restart.  On Hugging Face the SQLite DB lives in
+        # the ephemeral /tmp directory and is wiped on each restart, so this
+        # idempotent seeding repopulates a clean, consistent dataset every time.
+        seed_demo_data()
+
+
+# Known demo accounts used for the marketing demo.  Passwords are intentionally
+# simple here because these are throwaway demo accounts on an ephemeral DB.
+DEMO_RECRUITER_EMAIL = 'hr@codingo.ai'
+DEMO_APPLICANT_EMAIL = 'candidate@codingo.ai'
+DEMO_PASSWORD = 'codingo123'
+
+
+def seed_demo_data():
+    """Idempotently seed a demo recruiter, a demo applicant and a curated set
+    of professional job listings.
+
+    Safe to call on every startup: each entity is only created when missing,
+    and jobs are only inserted when the ``jobs`` table is empty.  Any failure
+    is rolled back and swallowed so seeding can never block app startup.
+    """
+    try:
+        # --- Demo recruiter (owns the seeded jobs so they appear in the HR
+        #     dashboard, which filters by recruiter_id) ---
+        recruiter = User.query.filter_by(email=DEMO_RECRUITER_EMAIL).first()
+        if recruiter is None:
+            recruiter = User(
+                username='Codingo HR',
+                email=DEMO_RECRUITER_EMAIL,
+                role='recruiter',
+            )
+            recruiter.set_password(DEMO_PASSWORD)
+            db.session.add(recruiter)
+            db.session.commit()
+
+        # --- Demo applicant (job-seeker role is 'unemployed') ---
+        applicant = User.query.filter_by(email=DEMO_APPLICANT_EMAIL).first()
+        if applicant is None:
+            applicant = User(
+                username='Demo Candidate',
+                email=DEMO_APPLICANT_EMAIL,
+                role='unemployed',
+            )
+            applicant.set_password(DEMO_PASSWORD)
+            db.session.add(applicant)
+            db.session.commit()
+
+        # --- Jobs: only seed when there are none, to avoid duplicates and to
+        #     never clobber jobs a recruiter may have posted at runtime ---
+        if Job.query.count() == 0:
+            demo_jobs = [
+                {
+                    'role': 'Data Scientist',
+                    'seniority': 'Mid-level',
+                    'company': 'NorthBridge Analytics',
+                    'skills': ['Python', 'SQL', 'Pandas', 'scikit-learn',
+                               'Machine Learning', 'Statistics', 'Data Visualization'],
+                    'description': (
+                        "We are looking for a Data Scientist to turn raw data into "
+                        "actionable insight. You will design and evaluate machine "
+                        "learning models, run statistical analyses, and build clear "
+                        "visualizations that guide product and business decisions. "
+                        "You will collaborate closely with engineering and product "
+                        "teams to take models from prototype to production."
+                    ),
+                },
+                {
+                    'role': 'Data Engineer',
+                    'seniority': 'Senior',
+                    'company': 'Cloudbyte Systems',
+                    'skills': ['Python', 'SQL', 'Apache Spark', 'Airflow',
+                               'ETL', 'AWS', 'Data Warehousing'],
+                    'description': (
+                        "Join us as a Senior Data Engineer to design, build and "
+                        "maintain the data pipelines that power our analytics and "
+                        "machine learning platforms. You will own scalable ETL "
+                        "workflows, optimize our data warehouse, and ensure data "
+                        "quality and reliability across the organization."
+                    ),
+                },
+                {
+                    'role': 'Machine Learning Engineer',
+                    'seniority': 'Mid-level',
+                    'company': 'Vantix AI',
+                    'skills': ['Python', 'TensorFlow', 'PyTorch', 'Machine Learning',
+                               'MLOps', 'Model Deployment', 'Docker'],
+                    'description': (
+                        "As a Machine Learning Engineer you will take models from "
+                        "research into reliable, production-grade services. You will "
+                        "train and fine-tune models, build robust deployment and "
+                        "monitoring pipelines, and work with data scientists to "
+                        "ship features that delight our users."
+                    ),
+                },
+                {
+                    'role': 'NLP Engineer',
+                    'seniority': 'Senior',
+                    'company': 'Lingua Labs',
+                    'skills': ['Python', 'NLP', 'Transformers', 'spaCy',
+                               'PyTorch', 'LLMs', 'Hugging Face'],
+                    'description': (
+                        "We are hiring a Senior NLP Engineer to build state-of-the-art "
+                        "language understanding systems. You will develop models for "
+                        "text classification, information extraction and conversational "
+                        "AI, fine-tune large language models, and deploy them at scale "
+                        "to power our products."
+                    ),
+                },
+                {
+                    'role': 'Computer Vision Engineer',
+                    'seniority': 'Mid-level',
+                    'company': 'VisionWorks AI',
+                    'skills': ['Python', 'OpenCV', 'Deep Learning', 'PyTorch',
+                               'Image Processing', 'CNNs', 'Model Optimization'],
+                    'description': (
+                        "Join our team as a Computer Vision Engineer to develop "
+                        "image and video understanding systems. You will design and "
+                        "train deep learning models for detection, segmentation and "
+                        "recognition, optimize them for real-time performance, and "
+                        "integrate them into our production pipeline."
+                    ),
+                },
+            ]
+
+            for spec in demo_jobs:
+                db.session.add(Job(
+                    role=spec['role'],
+                    description=spec['description'],
+                    seniority=spec['seniority'],
+                    skills=json.dumps(spec['skills']),
+                    company=spec['company'],
+                    recruiter_id=recruiter.id,
+                    num_questions=4,
+                ))
+            db.session.commit()
+    except Exception as exc:
+        # Never let seeding break startup.
+        db.session.rollback()
+        print(f"Demo data seeding skipped due to error: {exc}")

backend/templates/interview.html

@@ -698,22 +698,30 @@
             }
 
             displayQuestion(question, audioUrl = null) {
-                // Remove loading message
                 const loadingMsg = document.getElementById('loadingMessage');
                 if (loadingMsg) {
+                    // First question: reuse the existing avatar + bubble placeholder
+                    // so only ONE LUNA avatar shows. Swap the spinner for the text.
+                    const bubble = loadingMsg.parentElement; // .message-bubble
                     loadingMsg.remove();
+                    const p = document.createElement('p');
+                    p.textContent = question;
+                    bubble.appendChild(p);
+                } else {
+                    // Follow-up questions: append a new AI message bubble.
+                    const messageDiv = document.createElement('div');
+                    messageDiv.className = 'ai-message';
+                    const avatar = document.createElement('div');
+                    avatar.className = 'ai-avatar';
+                    const bubble = document.createElement('div');
+                    bubble.className = 'message-bubble';
+                    const p = document.createElement('p');
+                    p.textContent = question;
+                    bubble.appendChild(p);
+                    messageDiv.appendChild(avatar);
+                    messageDiv.appendChild(bubble);
+                    this.chatArea.appendChild(messageDiv);
                 }
-
-                // Create question message
-                const messageDiv = document.createElement('div');
-                messageDiv.className = 'ai-message';
-                messageDiv.innerHTML = `
-            <div class="ai-avatar">AI</div>
-            <div class="message-bubble">
-                <p>${question}</p>
-            </div>
-        `;
-                this.chatArea.appendChild(messageDiv);
                 this.chatArea.scrollTop = this.chatArea.scrollHeight;
 
                 // Update question counter