Refactor and fix Enhanced AGI Pipeline#25
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Co-authored-by: OneFineStarstuff <87420139+OneFineStarstuff@users.noreply.github.com>
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Reviewer's GuideRefactors the monolithic Enhanced AGI Pipeline into dedicated NLP, CV, and Speech processor modules, fixes YOLOv8 result handling and Whisper/TTS integration issues, and updates tests and fixtures to work with authenticated endpoints and realistic media inputs so the full test suite passes reliably. Sequence diagram for speech_to_text UploadFile handling in SpeechProcessorsequenceDiagram
actor Client
participant FastAPIApp
participant SpeechProcessor
participant TempFileSystem as TempFileSystem
participant WhisperModel
Client->>FastAPIApp: POST /speech_to_text(audio_file: UploadFile)
FastAPIApp->>SpeechProcessor: speech_to_text(audio_file)
SpeechProcessor->>TempFileSystem: create NamedTemporaryFile
SpeechProcessor->>TempFileSystem: write audio_file bytes
TempFileSystem-->>SpeechProcessor: tmp_path
SpeechProcessor->>WhisperModel: transcribe(tmp_path)
WhisperModel-->>SpeechProcessor: text
SpeechProcessor->>TempFileSystem: delete tmp_path
SpeechProcessor-->>FastAPIApp: text
FastAPIApp-->>Client: 200 OK + transcription
Sequence diagram for YOLOv8 detection in CVModulesequenceDiagram
actor Client
participant FastAPIApp
participant CVModule
participant YOLOModel
Client->>FastAPIApp: POST /detect_objects(image: Image)
FastAPIApp->>CVModule: detect_objects(image)
CVModule->>CVModule: validate image is not None
CVModule->>YOLOModel: __call__(image)
YOLOModel-->>CVModule: results_list
CVModule->>CVModule: results_list[0].to_json()
CVModule-->>FastAPIApp: json_results
FastAPIApp-->>Client: 200 OK + json_results
Class diagram for modular Enhanced AGI PipelineclassDiagram
class EnhancedAGIPipeline {
- NLPModule nlp_module
- CVModule cv_module
- SpeechProcessor speech_processor
+ EnhancedAGIPipeline()
+ handle_text(prompt: str) str
+ handle_image(image: Image) str
+ handle_speech_upload(audio_file: UploadFile) str
}
class NLPModule {
- T5Tokenizer tokenizer
- T5ForConditionalGeneration model
+ NLPModule()
+ generate_text(prompt: str) str
}
class CVModule {
- torch_device device
- YOLO model
+ CVModule()
+ detect_objects(image: Image) str
}
class SpeechProcessor {
- WhisperModel whisper_model
- TTS_Engine tts
+ SpeechProcessor()
+ speech_to_text(audio_file: UploadFile) str
+ text_to_speech(text: str) None
+ __del__() None
}
EnhancedAGIPipeline *-- NLPModule : composes
EnhancedAGIPipeline *-- CVModule : composes
EnhancedAGIPipeline *-- SpeechProcessor : composes
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Review these changes at https://app.gitnotebooks.com/OneFineStarstuff/OneFineStarstuff.github.io/pull/25 |
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Caution Review failedThe pull request is closed. 📝 WalkthroughWalkthroughThis pull request introduces a comprehensive modular architecture refactor, transitioning from monolithic design to an API-driven service with separate NLP, CV, and Speech processing modules. New components include documentation tooling, enhanced auditing with cryptographic integrity, production FastAPI server setup, and extensive testing with synthetic data generation. Changes
Sequence Diagram(s)sequenceDiagram
participant Client
participant FastAPI as FastAPI Endpoint
participant EnhancedAGIPipeline as Orchestrator
participant NLPModule
participant CVModule
participant SpeechProcessor
Client->>FastAPI: POST /process-nlp/ (prompt)
FastAPI->>FastAPI: verify_token()
FastAPI->>EnhancedAGIPipeline: process_nlp(prompt)
EnhancedAGIPipeline->>NLPModule: generate_text(prompt)
NLPModule-->>EnhancedAGIPipeline: generated text
EnhancedAGIPipeline-->>FastAPI: response
FastAPI-->>Client: JSON {summary: text}
Client->>FastAPI: POST /process-cv-detection/ (image file)
FastAPI->>FastAPI: verify_token()
FastAPI->>EnhancedAGIPipeline: process_cv(image)
EnhancedAGIPipeline->>CVModule: detect_objects(image)
CVModule-->>EnhancedAGIPipeline: detection results
EnhancedAGIPipeline-->>FastAPI: response
FastAPI-->>Client: JSON {classification: results}
Client->>FastAPI: POST /speech-to-text/ (audio file)
FastAPI->>FastAPI: verify_token()
FastAPI->>EnhancedAGIPipeline: process_stt(file)
EnhancedAGIPipeline->>SpeechProcessor: speech_to_text(audio)
SpeechProcessor-->>EnhancedAGIPipeline: transcribed text
EnhancedAGIPipeline-->>FastAPI: response
FastAPI-->>Client: JSON response
sequenceDiagram
participant RuleEngine
participant TelemetryMonitor as Monitor
participant PhaseState
participant AuditLog as AuditLogEntry
participant Logger
Monitor->>Monitor: sample(phase)
Monitor-->>RuleEngine: TelemetrySnapshot(latency, region, phase)
RuleEngine->>RuleEngine: evaluate(telemetry)
RuleEngine->>RuleEngine: apply sorting by (action, priority)
RuleEngine->>RuleEngine: _log_conflict_resolution()
RuleEngine->>AuditLog: create() + HMAC-SHA256
AuditLog-->>Logger: audit entry
Logger-->>RuleEngine: logged
RuleEngine-->>PhaseState: return (winning_rule, triggered_rules)
RuleEngine-->>Logger: triggered rules and phase transition
Estimated code review effort🎯 4 (Complex) | ⏱️ ~60 minutes Poem
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Co-authored-by: OneFineStarstuff <87420139+OneFineStarstuff@users.noreply.github.com>
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| result = self.whisper_model.transcribe(audio_data) | ||
| return result['text'] | ||
| # API Key from environment or default | ||
| VALID_API_KEY = os.getenv("AGI_API_KEY", "YvZz9Hni0hWJPh_UWW4dQYf9rhIe9nNYcC5ZQTTZz0Q") |
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security (generic-api-key): Detected a Generic API Key, potentially exposing access to various services and sensitive operations.
Source: gitleaks
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| """ | |||
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security (generic-api-key): Detected a Generic API Key, potentially exposing access to various services and sensitive operations.
Source: gitleaks
Micro-Learning Topic: External entity injection (Detected by phrase)Matched on "xxE"An XML External Entity attack is a type of attack against an application that parses XML input. This attack occurs when XML input containing a reference to an external entity is processed by a weakly configured XML parser. This attack may lead to the disclosure of confidential data, denial of service, server-side request forgery, port scanning from the perspective of the machine where the parser is located, and other system impacts. Try a challenge in Secure Code WarriorHelpful references
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User description
This pull request refactors the Enhanced AGI Pipeline into separate modules and fixes several bugs in the CV and Speech Processing components.
Key changes:
PR created automatically by Jules for task 17049544687311068943 started by @OneFineStarstuff
Summary by Sourcery
Extract AGI pipeline components into dedicated NLP, CV, and speech processor modules while hardening speech and vision handling and updating tests to use self-contained authenticated inputs.
New Features:
Bug Fixes:
Enhancements:
Tests:
Summary by CodeRabbit
Release Notes
New Features
Documentation
Tests
Style
Description
CVModulefor object detection using YOLOv8.Changes walkthrough 📝
5 files
agi-pipeline.py
Refactor AGI Pipeline into Modular Structureagi-pipeline.py
cv_module.py
Create Computer Vision Module with YOLOv8cv_module.py
CVModulefor object detection using YOLOv8.main.py
Enhance API Security and Structuremain.py
nlp_module.py
Implement NLP Module for Text Processingnlp_module.py
NLPModulefor text processing using FLAN-T5.speech_processor.py
Create Speech Processing Modulespeech_processor.py
SpeechProcessorfor STT and TTS functionalities.1 files
fix_agi.py
Add Docstrings for Documentation Clarityfix_agi.py
3 files
test_cv_module.py
Implement Unit Tests for CV Moduletest_cv_module.py
CVModuleto validate object detection.test_nlp_module.py
Implement Unit Tests for NLP Moduletest_nlp_module.py
NLPModuleto ensure text generation works.test_speech_processor.py
Implement Unit Tests for Speech Processortest_speech_processor.py
SpeechProcessorto validate STT and TTS.