feat: Complete interactive demo with 6 sections #370
Conversation
- Smart installation with transparent planning phase - Hardware-aware intelligence demonstrations - AI natural language understanding - Smart stacks workflow (list and install) - Installation history tracking - Rollback protection demonstration - Comparison table finale showing key differentiators - Clear screen between sections for better UX - ~5 minute interactive tutorial - Matches actual Cortex output with CX branding Users type real commands to build muscle memory. Accepts any input for learning (no validation friction). Closes cortexlinux#366
📝 WalkthroughWalkthroughReplaces the procedural demo with a new class-based interactive tutorial: adds Changes
Sequence Diagram(s)sequenceDiagram
participant U as User
participant D as CortexDemo
participant H as HardwareDetector
participant O as SimulatedCortexOutput
U->>D: start/run()
D->>H: detect_hardware()
H-->>D: hardware info (GPU/CPU,NVIDIA/AMD)
D->>U: render intro & prompts (sections)
alt user enters command
U->>D: command input (install/ask/stack)
D->>O: _simulate_cortex_output(command, packages)
O-->>D: simulated plan/output
D->>U: display plan, prompts (hint/skip)
else user interrupts
U->>D: Ctrl+C / EOF
D-->>U: handle interruption, return non-zero
end
D->>U: show finale & next steps
Estimated code review effort🎯 4 (Complex) | ⏱️ ~45 minutes Possibly related PRs
Suggested reviewers
Poem
Pre-merge checks and finishing touches❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing touches
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Actionable comments posted: 3
🧹 Nitpick comments (2)
cortex/demo.py (2)
206-217: Usegpu_infoconsistently instead of re-accessinggpu[0].After extracting
gpu_info = gpu[0], the code still accessesgpu[0].modeldirectly on lines 217, 239, and 252. Usinggpu_info.modelwould be more consistent and slightly cleaner.🔎 Proposed fix
if has_nvidia: # NVIDIA GPU - show successful CUDA install - self.console.print(f"[cyan]Detected GPU:[/cyan] {gpu[0].model}") + self.console.print(f"[cyan]Detected GPU:[/cyan] {gpu_info.model}")Apply similar changes to lines 239 and 252.
411-438: Consider using dynamic dates for the history table.The hardcoded dates (e.g., "2025-12-26 13:11:51") will appear stale as time passes. For a more polished demo, consider generating dates relative to the current time.
🔎 Example approach
from datetime import datetime, timedelta now = datetime.now() dates = [ now.strftime("%Y-%m-%d %H:%M:%S"), (now - timedelta(days=1)).strftime("%Y-%m-%d %H:%M:%S"), (now - timedelta(days=1, hours=1)).strftime("%Y-%m-%d %H:%M:%S"), (now - timedelta(days=2)).strftime("%Y-%m-%d %H:%M:%S"), ]
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Docstrings required for all public APIs
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cortex/demo.py
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cortex/demo.py (3)
cortex/branding.py (1)
show_banner(35-46)cortex/cli.py (2)
status(797-828)demo(180-184)cortex/progress_indicators.py (1)
total_steps(99-100)
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🔇 Additional comments (8)
cortex/demo.py (8)
95-105: LGTM!The method correctly handles both interactive and non-interactive modes, with proper exception handling for user interruption.
107-124: LGTM!The method correctly implements the "accept any input for learning" design goal while maintaining proper exception handling.
126-171: LGTM!The simulation logic is well-structured with clear phases (understanding, planning, execution) and proper CX branding. The optional
show_executionparameter provides flexibility for different demo scenarios.
288-333: LGTM!The AI features section effectively demonstrates the
cortex askcapability with a well-formatted response panel.
335-389: LGTM!The stacks section provides a clear demonstration of the workflow bundles feature with a well-formatted table and simulated installation.
448-476: LGTM!The rollback section effectively demonstrates the undo capability and ties back to the installation ID from earlier sections.
478-564: LGTM!The finale provides a compelling comparison table and comprehensive next steps, making for a polished conclusion to the demo.
567-573: LGTM!The entry point is clean and correctly delegates to the new class-based implementation.
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🧹 Nitpick comments (3)
cortex/demo.py (3)
13-13: Remove unused imports fromrich.progress.
BarColumn,Progress,SpinnerColumn, andTextColumnare imported but never used in this file. The code usesself.console.status()which doesn't require these explicit imports.🔎 Proposed fix
-from rich.progress import BarColumn, Progress, SpinnerColumn, TextColumn
23-27: Add type annotation forself.hwand docstring for__init__.Per coding guidelines, type hints are required in Python code and docstrings are required for all public APIs.
self.hwlacks a type annotation, and__init__lacks a docstring.🔎 Proposed fix
def __init__(self): + """Initialize the interactive demo with console and state.""" self.console = Console() - self.hw = None + self.hw: Optional[object] = None # Set by detect_hardware() self.is_interactive = sys.stdin.isatty() self.installation_id = self._generate_id()
131-133: Add return type hint for consistency.Per coding guidelines, type hints are required. This method implicitly returns
None.🔎 Proposed fix
def _simulate_cortex_output( self, command: str, packages: list[str], show_execution: bool = False - ): + ) -> None:
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cortex/demo.py (3)
cortex/branding.py (1)
show_banner(35-46)cortex/cli.py (2)
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total_steps(99-100)
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🔇 Additional comments (13)
cortex/dependency_check.py (1)
46-46: LGTM!Trivial stylistic change from single to double quotes in the join delimiter. No functional impact.
cortex/demo.py (12)
29-35: LGTM!Both
clear_screenand_generate_idare well-documented with appropriate docstrings and type hints.
37-98: LGTM!The
runmethod is well-structured with proper error handling forKeyboardInterruptandEOFError, clear section progression, and appropriate return codes.
100-110: LGTM!Clean implementation with proper non-interactive mode support for CI and robust exception handling.
112-129: LGTM!Zero-validation approach aligns with PR objectives for a learning-focused demo. Good exception handling.
187-211: LGTM!Well-structured section with clear flow: explanation → command prompt → simulation → feature highlight.
213-322: LGTM!Hardware detection logic is sound with proper null checks. The three branches (NVIDIA/AMD/no GPU) provide good coverage for different user scenarios.
324-373: LGTM!Clean AI features section with well-formatted response content and appropriate Panel display.
375-431: LGTM!Stacks section effectively demonstrates listing and installation with proper table formatting.
433-474: LGTM!History section with appropriate example data. Hardcoded dates are acceptable for demo purposes.
476-512: LGTM!Rollback section cleanly demonstrates the undo capability with proper ID generation.
514-574: LGTM!Excellent finale with a compelling comparison table and comprehensive next steps. The summary effectively reinforces what users learned.
577-583: LGTM!Clean entry point that preserves the existing API while delegating to the new class-based implementation. Matches CLI integration in
cortex/cli.py.
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🧹 Nitpick comments (5)
cortex/demo.py (5)
13-13: Remove unused imports from rich.progress.The imports
BarColumn,Progress,SpinnerColumn, andTextColumnare not used in this file. The demo only usesself.console.status(...)contexts, which don't require these imports.🔎 Proposed fix
-from rich.progress import BarColumn, Progress, SpinnerColumn, TextColumn
23-27: Consider adding a docstring and type annotation for better API documentation.The
__init__method lacks a docstring, andself.hwhas no type annotation. Per coding guidelines, docstrings are required for public APIs.🔎 Proposed enhancement
def __init__(self): + """ + Initialize the interactive demo. + + Sets up console, hardware detection placeholder, interactivity detection, + and generates a unique installation ID for demo purposes. + """ self.console = Console() - self.hw = None + self.hw: Optional[object] = None # Set by detect_hardware() self.is_interactive = sys.stdin.isatty() self.installation_id = self._generate_id()
29-31: Add return type annotation for consistency.Per coding guidelines, type hints are required. The
clear_screenmethod should explicitly declare-> None.🔎 Proposed fix
- def clear_screen(self): + def clear_screen(self) -> None: """Clears the terminal screen""" self.console.clear()
128-131: Add return type annotation.The method should declare
-> Noneper coding guidelines requiring type hints.🔎 Proposed fix
def _simulate_cortex_output( - self, command: str, packages: list[str], show_execution: bool = False + self, command: str, packages: list[str], show_execution: bool = False + ) -> None: - ): """Simulate real Cortex output with CX branding"""
184-186: Add return type annotation.Per coding guidelines, the method should declare
-> boolas its return type.🔎 Proposed fix
- def _section_ai_intelligence(self) -> bool: + def _section_ai_intelligence(self) -> bool: """Section 1: AI Intelligence - NLP, Planning, and Hardware Awareness"""(Note: The return type is actually already present in the signature. This might be a display issue.)
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**/*.py: Follow PEP 8 style guide
Type hints required in Python code
Docstrings required for all public APIs
Files:
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cortex/demo.py (3)
cortex/branding.py (1)
show_banner(35-46)cortex/cli.py (2)
status(797-828)demo(180-184)cortex/progress_indicators.py (1)
total_steps(99-100)
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[error] 577-577: W292 No newline at end of file.
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cortex/demo.py:577:22: W292 No newline at end of file
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🔇 Additional comments (6)
cortex/demo.py (6)
37-96: LGTM! Well-structured demo orchestration.The
run()method has a clear flow: initialization, hardware detection, section execution, and finale. Error handling properly catches interruptions and returns appropriate exit codes. The section loop design allows early exit on user interruption.
97-127: LGTM! Robust user interaction handling.Both methods properly handle interactive vs. non-interactive modes and gracefully catch interruptions. The design choice to accept any input in
_prompt_command(line 118) aligns with the learning-friendly objective stated in the PR.
263-278: LGTM! Robust GPU detection with multiple vendor support.The GPU detection logic safely handles the case where hardware info is unavailable (lines 263-264) and checks for both NVIDIA and AMD GPUs using case-insensitive pattern matching. The three-way branching (NVIDIA/AMD/no-GPU) provides tailored demos for each scenario.
365-424: LGTM! Clear demonstration of stacks feature.The section effectively showcases the stacks workflow with a well-formatted table of available stacks and a complete installation simulation. The tip about creating custom stacks (lines 421-422) is a nice touch.
426-506: LGTM! Excellent narrative continuity.The section effectively demonstrates history tracking and rollback features. The use of
self.installation_idfrom the earlier webdev stack installation (line 449, 478, 481) creates excellent narrative continuity throughout the demo.
508-569: LGTM! Compelling finale with clear value proposition.The comparison table (lines 519-536) effectively highlights Cortex's differentiators, and the summary panel provides actionable next steps with correct command syntax. Recommending
cortex wizardas the first step (line 553) is good UX guidance.
1 participant
Summary
Completely redesigned
cortex demofrom a 5-second hardware check into a comprehensive 5-minute interactive tutorial.Changes
Before
After
Sections
cortex askwith intelligent recommendationscortex stack)Key Features
Testing
Video demonstration:
Screencast from 26-12-25 03:45:52 PM IST.webm
Closes #366