Candidate: Refactor app.py into modular services
Priority: Strong
Files Affected: app.py
→
Proposed:
inference_pipeline.py,
model_loader.py,
preprocessor.py,
postprocessor.py
The Problem
The main application file (app.py) is acting as a "god object". It manages web framework setup, model loading, inference pipelines, preprocessing, postprocessing, utilities, and API endpoints simultaneously. This creates **low architectural depth** (the interface is nearly as complex as its implementation), **poor testability** (isolated pipeline units cannot be targeted), and **low locality** (unrelated logic must be parsed to fix individual features).
Proposed Solution
Split operational concerns into highly focused, cleanly encapsulated modules:
ModelLoader: Handles asynchronous loading and persistent caching of PyTorch weights (angle, inflammation, segmentation models).Preprocessor: Isolates structural image transformations and CLAHE enhancement parameters.InferencePipeline: Orchestrates execution flows: angle classification → inflammation detection → conditional segmentation → sizing/severity metrics.Postprocessor: Houses structural thickness evaluations, severity metrics calculations, and visual overlay generators.APIHandler: A thin, declarative FastAPI endpoint wrapper delegating exclusively to the primaryInferencePipeline.
Expected Architecture Benefits
-
Architectural Depth High complexity is hidden beneath simple interfaces (e.g.,
analyze_image(img) -> Result). - High Code Locality Bugs in sizing algorithms map cleanly to the postprocessor without risking model caching logic.
- Simplified Testing Individual pipeline states and weights can be unit-tested seamlessly using mock behaviors.
- AI and Dev Navigation Highly categorized file trees accelerate context gathering for language models and onboarding engineers.
Before (Shallow Architecture)
graph TD
A[FastAPI Endpoint] --> B[load_angle_model]
A --> C[load_inflammation_model]
A --> D[load_segmentation_model_sup]
A --> E[load_segmentation_model_post]
A --> F[predict_angle]
A --> G[predict_inflammation]
A --> H[segment_image]
A --> I[measure_thickness_new]
A --> J[analyze_inflammation_severity]
A --> K[create_segmentation_overlay]
A --> L[apply_clahe]
A --> M[save_patient_data]
style A fill:#f87171,stroke:#ef4444,stroke-width:2px,color:#fff
style B fill:#fee2e2,stroke:#f87171
style C fill:#fee2e2,stroke:#f87171
style D fill:#fee2e2,stroke:#f87171
style E fill:#fee2e2,stroke:#f87171
style F fill:#fee2e2,stroke:#f87171
style G fill:#fee2e2,stroke:#f87171
style H fill:#fee2e2,stroke:#f87171
style I fill:#fee2e2,stroke:#f87171
style J fill:#fee2e2,stroke:#f87171
style K fill:#fee2e2,stroke:#f87171
style L fill:#fee2e2,stroke:#f87171
style M fill:#fee2e2,stroke:#f87171
After (Deepened Architecture)
graph TD
subgraph API [API Gateway Router]
A[FastAPI Endpoint] --> B[InferencePipeline.analyze]
end
subgraph InferencePipeline [Core Workflow Orchestrator]
B --> C[ModelLoader.get_angle_model]
B --> D[Preprocess]
B --> E[predict_angle]
E --> F{Inflammation?}
F -->|Yes| G[ModelLoader.get_inflammation_model]
G --> H[predict_inflammation]
H --> I{Inflammation Detected?}
I -->|Yes| J[ModelLoader.get_seg_model_sup/post]
J --> K[segment_image]
K --> L[measure_thickness]
K --> M[analyze_severity]
L --> N[Result: Measurement]
M --> O[Result: Severity]
K --> P[Result: Segmentation Masks]
E -->|No| Q[Result: Angle Only]
F -->|No| Q
end
subgraph ModelLoader [Model Assets Lifecycle]
C --> C1[Load Angle Model]
G --> G1[Load Inflammation Model]
J --> J1[Load Segmentation Model]
end
subgraph Preprocessor [Image Engineering]
D --> D1[Transforms]
D --> D2[CLAHE]
end
subgraph Postprocessor [Metrics & Artifact Generation]
L --> L1[Thickness Calc]
M --> M1[Severity Scoring]
end
style A fill:#3b82f6,stroke:#2563eb,stroke-width:2px,color:#fff
style B fill:#dbeafe,stroke:#60a5fa
style C fill:#dbeafe,stroke:#60a5fa
style D fill:#dbeafe,stroke:#60a5fa
style E fill:#dbeafe,stroke:#60a5fa
style F fill:#dbeafe,stroke:#60a5fa
style G fill:#dbeafe,stroke:#60a5fa
style H fill:#dbeafe,stroke:#60a5fa
style I fill:#dbeafe,stroke:#60a5fa
style J fill:#dbeafe,stroke:#60a5fa
style K fill:#dbeafe,stroke:#60a5fa
style L fill:#dbeafe,stroke:#60a5fa
style M fill:#dbeafe,stroke:#60a5fa
style N fill:#e0f2fe,stroke:#0284c7
style O fill:#e0f2fe,stroke:#0284c7
style P fill:#e0f2fe,stroke:#0284c7
style Q fill:#e0f2fe,stroke:#0284c7
style C1 fill:#f3e8ff,stroke:#a855f7
style G1 fill:#f3e8ff,stroke:#a855f7
style J1 fill:#f3e8ff,stroke:#a855f7
style D1 fill:#fef3c7,stroke:#d97706
style D2 fill:#fef3c7,stroke:#d97706
style L1 fill:#ecfccb,stroke:#65a30d
style M1 fill:#ecfccb,stroke:#65a30d