Enhance Supply Chain & Logistics Platform with AI Security and Data Engineering#26
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Enhance Supply Chain & Logistics Platform with AI Security and Data Engineering#26
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…n, and data engineering. - Created supply_chain_platform/security_tools.py for AI-driven anomaly detection and IoT risk assessment. - Updated supply_chain_platform/ai_logistics_engine.py with data engineering pipeline and transport optimizer. - Enhanced supply_chain_platform/incoterms_data.json with detailed trade term information. - Updated supply_chain_platform/supply_chain_main.py (CLI) to include new features. - Redesigned src/SupplyChainPlatform.jsx (Frontend) with new Security and Data Engineering tabs, and improved Incoterms UI. Co-authored-by: GYFX35 <134739293+GYFX35@users.noreply.github.com>
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| Status | Name | Latest Commit | Updated (UTC) |
|---|---|---|---|
| ❌ Deployment failed View logs |
games | d0cc76a | Feb 12 2026, 12:23 PM |
Reviewer's GuideEnhances the Supply Chain & Logistics AI platform across frontend and CLI by adding AI security analysis, data engineering pipeline simulation, expanded Incoterms metadata, and multimodal transport optimization, with new interactive UI tabs and helper components. Sequence diagram for frontend data engineering pipeline executionsequenceDiagram
actor User
participant SupplyChainPlatformUI
participant DataPipelineState
participant BlockchainLogger
User->>SupplyChainPlatformUI: Click Trigger_Pipeline_Run_button
SupplyChainPlatformUI->>DataPipelineState: runDataPipeline()
DataPipelineState->>DataPipelineState: setPipelineStep(1)
DataPipelineState->>DataPipelineState: setAiStatus(Ingesting_IoT_Telemetry)
DataPipelineState-->>User: Show_IoT_Ingestion_node_active
DataPipelineState->>DataPipelineState: setPipelineStep(2)
DataPipelineState->>DataPipelineState: setAiStatus(Cleaning_Data)
DataPipelineState-->>User: Show_Cleaning_node_active
DataPipelineState->>DataPipelineState: setPipelineStep(3)
DataPipelineState->>DataPipelineState: setAiStatus(Running_AI_Transformers)
DataPipelineState-->>User: Show_AI_Transform_node_active
DataPipelineState->>DataPipelineState: setPipelineStep(4)
DataPipelineState->>DataPipelineState: setAiStatus(Loading_to_Warehouse)
DataPipelineState-->>User: Show_Data_Warehouse_node_active
DataPipelineState->>DataPipelineState: setAiStatus(Pipeline_Complete)
DataPipelineState->>BlockchainLogger: logToBlockchain(Data_Pipeline_Executed)
BlockchainLogger-->>User: Append_blockchainLog_entry
Sequence diagram for CLI security and risk analysis flowsequenceDiagram
actor Operator
participant SupplyChainCLI as supply_chain_main
participant SecurityAnalyzer as SupplyChainSecurityAnalyzer
Operator->>SupplyChainCLI: Select_menu_option_3(Security_&_Risk_Analyzer)
SupplyChainCLI-->>Operator: Show_submenu_a_b_c
Operator->>SupplyChainCLI: Choose_a(Shipment_Anomaly_Detection)
SupplyChainCLI->>Operator: Prompt_for_route_deviation_temperature_max_temp
Operator-->>SupplyChainCLI: Enter_route_deviation_temp_max_temp
SupplyChainCLI->>SecurityAnalyzer: detect_shipment_anomaly(shipment_data_dict)
SecurityAnalyzer-->>SupplyChainCLI: Anomaly_result_string
SupplyChainCLI-->>Operator: Print_Security_Scan_result
Operator->>SupplyChainCLI: Choose_b(IoT_Cyber_Risk_Assessment)
SupplyChainCLI->>Operator: Prompt_for_protocol_and_creds
Operator-->>SupplyChainCLI: Enter_protocol_and_creds
SupplyChainCLI->>SecurityAnalyzer: assess_iot_cyber_risk(device_info_dict)
SecurityAnalyzer-->>SupplyChainCLI: Risk_report_dict
SupplyChainCLI-->>Operator: Print_Cyber_Risk_Report
Operator->>SupplyChainCLI: Choose_c(Invoice_Integrity_Check)
SupplyChainCLI->>Operator: Prompt_for_amount_and_vendor
Operator-->>SupplyChainCLI: Enter_amount_and_vendor
SupplyChainCLI->>SecurityAnalyzer: verify_invoice_integrity(invoice_id, amount, vendor_id)
SecurityAnalyzer-->>SupplyChainCLI: Fraud_analysis_string
SupplyChainCLI-->>Operator: Print_Fraud_Analysis
Updated class diagram for logistics, data engineering, and security modulesclassDiagram
class AILogisticsEngine {
+predict_delivery_delay(distance, weather)
+analyze_supply_chain_risk(inventory_level, demand_forecast)
}
class DataEngineeringPipe {
+simulate_pipeline(batch_size)
}
class TransportOptimizer {
+calculate_multimodal_cost(distance, weight, mode)
}
class SupplyChainSecurityAnalyzer {
+detect_shipment_anomaly(shipment_data)
+assess_iot_cyber_risk(device_info)
+verify_invoice_integrity(invoice_id, amount, vendor_id)
}
class SupplyChainCLI {
+load_incoterms()
+display_menu()
+main()
}
SupplyChainCLI --> AILogisticsEngine
SupplyChainCLI --> DataEngineeringPipe
SupplyChainCLI --> TransportOptimizer
SupplyChainCLI --> SupplyChainSecurityAnalyzer
File-Level Changes
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Hey - I've found 2 issues, and left some high level feedback:
- The
aiStatusstate is now used by multiple tabs (logistics, security, data pipeline), so triggering one workflow can overwrite the status message of another; consider splitting this into separate status states per feature to avoid confusing UI feedback. - In the CLI (
supply_chain_main.py), submenus for options 2 and 3 don’t handle invalid sub-options (anything other than a/b/c), which currently just fall through without feedback; adding an explicitelsebranch with an error message would make the UX more robust. - The new
SupplyChainSecurityAnalyzermethods document richershipment_data/device_infopayloads than the examples actually use (e.g.,current_location,last_patch_date), which could lead to misuses; consider aligning parameter docs with the fields you actually inspect or expanding the implementation to use the documented fields.
Prompt for AI Agents
Please address the comments from this code review:
## Overall Comments
- The `aiStatus` state is now used by multiple tabs (logistics, security, data pipeline), so triggering one workflow can overwrite the status message of another; consider splitting this into separate status states per feature to avoid confusing UI feedback.
- In the CLI (`supply_chain_main.py`), submenus for options 2 and 3 don’t handle invalid sub-options (anything other than a/b/c), which currently just fall through without feedback; adding an explicit `else` branch with an error message would make the UX more robust.
- The new `SupplyChainSecurityAnalyzer` methods document richer `shipment_data`/`device_info` payloads than the examples actually use (e.g., `current_location`, `last_patch_date`), which could lead to misuses; consider aligning parameter docs with the fields you actually inspect or expanding the implementation to use the documented fields.
## Individual Comments
### Comment 1
<location> `src/SupplyChainPlatform.jsx:131-140` </location>
<code_context>
}, 1500);
};
+ const runDataPipeline = () => {
+ setPipelineStep(1);
+ const steps = [
+ 'Ingesting IoT Telemetry...',
+ 'Cleaning Data...',
+ 'Running AI Transformers...',
+ 'Loading to Warehouse...'
+ ];
+ let i = 0;
+ const interval = setInterval(() => {
+ if (i < steps.length) {
+ setAiStatus(steps[i]);
+ setPipelineStep(i + 1);
+ i++;
+ } else {
+ clearInterval(interval);
+ setAiStatus('Pipeline Complete.');
+ logToBlockchain('Data Pipeline Executed');
+ }
+ }, 800);
+ };
+
</code_context>
<issue_to_address>
**issue (bug_risk):** Consider cleaning up the interval in `runDataPipeline` to avoid it running after unmount or multiple triggers.
The interval created here is only cleared when the loop finishes. If the component unmounts mid-run, the interval can still fire and attempt state updates, causing React warnings and a potential memory leak. Please store the interval ID (e.g., in a ref) and clear it in a `useEffect` cleanup, or switch to a cancelable recursive `setTimeout` pattern.
</issue_to_address>
### Comment 2
<location> `supply_chain_platform/supply_chain_main.py:78-87` </location>
<code_context>
+ elif choice == '3':
</code_context>
<issue_to_address>
**issue (bug_risk):** Unvalidated numeric input in the Security & Risk submenu can raise `ValueError` and terminate the program.
Sub-options 'a' and 'c' convert input to `float` with no `try/except`, so any non-numeric value for route deviation, temperature, or invoice amount will raise `ValueError` and terminate the CLI. Please add error handling consistent with the other menus to avoid crashes and keep behavior uniform.
</issue_to_address>Help me be more useful! Please click 👍 or 👎 on each comment and I'll use the feedback to improve your reviews.
Comment on lines
+131
to
+140
| const runDataPipeline = () => { | ||
| setPipelineStep(1); | ||
| const steps = [ | ||
| 'Ingesting IoT Telemetry...', | ||
| 'Cleaning Data...', | ||
| 'Running AI Transformers...', | ||
| 'Loading to Warehouse...' | ||
| ]; | ||
| let i = 0; | ||
| const interval = setInterval(() => { |
There was a problem hiding this comment.
issue (bug_risk): Consider cleaning up the interval in runDataPipeline to avoid it running after unmount or multiple triggers.
The interval created here is only cleared when the loop finishes. If the component unmounts mid-run, the interval can still fire and attempt state updates, causing React warnings and a potential memory leak. Please store the interval ID (e.g., in a ref) and clear it in a useEffect cleanup, or switch to a cancelable recursive setTimeout pattern.
Comment on lines
78
to
+87
| elif choice == '3': | ||
| try: | ||
| inv = float(input("Current Inventory Level: ")) | ||
| demand = float(input("Forecasted Demand: ")) | ||
| risk = ai_engine.analyze_supply_chain_risk(inv, demand) | ||
| print(f"\nAI Risk Assessment: {risk}") | ||
| except ValueError: | ||
| print("Invalid numbers.") | ||
| print("\n--- Security & Risk AI ---") | ||
| print("a. Shipment Anomaly Detection") | ||
| print("b. IoT Device Cyber Risk Assessment") | ||
| print("c. Invoice Integrity Check") | ||
| sub_choice = input("Select sub-option: ").lower() | ||
|
|
||
| if sub_choice == 'a': | ||
| dev = float(input("Route Deviation % (e.g. 10): ")) | ||
| temp = float(input("Current Temp: ")) |
There was a problem hiding this comment.
issue (bug_risk): Unvalidated numeric input in the Security & Risk submenu can raise ValueError and terminate the program.
Sub-options 'a' and 'c' convert input to float with no try/except, so any non-numeric value for route deviation, temperature, or invoice amount will raise ValueError and terminate the CLI. Please add error handling consistent with the other menus to avoid crashes and keep behavior uniform.
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This PR significantly enhances the Supply Chain & Digital Twin Platform by integrating advanced logistics, data engineering, and security features.
Key improvements:
PR created automatically by Jules for task 4205200497296634291 started by @GYFX35
Summary by Sourcery
Enhance the supply chain AI platform with expanded Incoterms metadata, new logistics, security, and data engineering capabilities across the CLI and React dashboard.
New Features:
Enhancements: