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ae:T13143d, Sports Card Enhancer

CARDENHANCEAI

Sports Card Restoration Studio

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noticing a critical discrepancy: the README describes a DCPT pipeline with specific files and structure, but when I look at what's actually in the repository, that pipeline doesn't exist. Instead, there's a minimal backend setup with just a few utility modules and a separate sports card enhancement application built with FastAPI and Vite. The documentation talks about DCPT as the core system, but the actual codebase is missing the entire pipeline implementation—this is a substantial gap that would require building out the backend infrastructure from scratch according to the spec, which conflicts with the instruction to work with the existing repository. I need to present this audit finding to the user and clarify the scope before proceeding. I'll organize this into a structured task list using TodoManager: audit the repo first, then build the dcpt_pipeline core with config and model components, add training and inference logic, write comprehensive tests, set up the FastAPI service with the pipeline runner, and finally containerize everything with CI gates. Let me start by examining the existing backend files to understand what's already there.ba:T472,The audit reveals the DCPT pipeline specified in the requirements is completely absent from the repository. What's actually present is fragmented: the backend has partial adaptive preprocessing using OpenCV and sklearn with a naming bug in the audit logging and a broken batch processor that tries to use unpicklable closures with ProcessPoolExecutor, while there's also a separate sports-card-enhancer component that appears incomplete. Given the explicit spec to build DCPT to production, I'm setting aside further questions and moving into execution mode. I'll tackle this systematically: first setting up the Python environment with CPU-based PyTorch and dependencies, then building out the complete dcpt_pipeline package with its configuration, schemas, graph structure, TDA components, model with node-conditioned attention, training infrastructure, and inference logic. After that comes comprehensive testing, integrating a FastAPI service with the pipeline runner, fixing the existing backend defects, containerizing everything with Docker, and ensuring the frontend builds properly. Let me start by checking Python availability.bb:Te05,I've got the environment set up with the necessary libraries, so now I'm organizing the backend package structure according to the spec—setting up the pyproject.toml configuration and the run_pipeline.py entry point at the root level, with the test suite and type checking configured to run against the backend directory. The dcpt_pipeline package itself will contain the core modules: a typed configuration system using Pydantic that respects the precedence chain from defaults through environment variables to CLI arguments, schema definitions for all the data structures flowing through the pipeline, utilities for dataset handling and synthetic data generation with provenance tracking, graph construction logic that transforms images into patch-based graphs with edge indices, TDA components including the T* function with pole handling and GPU acceleration support, and the main model architecture featuring node-conditioned attention with low-rank adapters and a TDA gating mechanism. For T*, I need to validate that λ > 0 and γ ≥ 0, then handle the pole at ℓ = -λ by raising an error when values approach it since the graph Laplacian eigenvalues stay non-negative. For per-node ℓ values, I'll compute them using the normalized Laplacian diagonal or a local connectivity measure like heat signature energy. Since there's no standard implementation, I'm going with a pragmatic approach: computing per-node persistence lifetimes from 0-dimensional persistent homology on the graph using a sublevel-set filtration with union-find to track when components are born and merge, which gives me genuine topological data analysis in roughly 60 lines. Each node gets a lifetime ℓ_j based on when its component appears and disappears in the filtration, ensuring ℓ ≥ 0. Now I'm setting up the attention mechanism with shared weight matrices and low-rank per-node adapters, where each node j gets its own factorized update ΔW = U_j V_j^T. The attention scores are computed between query and key projections restricted to the graph's edges, then gated by a sigmoid function applied to the node's persistence lifetime scaled by α=5, which multiplicatively modulates the softmax attention weights according to the documented formula. The model outputs value predictions, defect logits for multiclass classification, node embeddings, and TDA-derived features along with attention weights. I'm using a multi-task loss combining Huber/MSE for regression, cross-entropy for the mutually exclusive defect classes, and L2 regularization on the adapter weights. For BPDA, I'll implement a custom autograd function that treats quantization as non-differentiable in the forward pass but uses identity as the backward surrogate. The TDA cache uses a SHA256 hash of the input fingerprint, graph configuration, TDA settings, and persistence parameters to detect corruption via stored checksums. Now I'm setting up checkpoint saving with torch.save, ensuring compatibility with weights_only mode by storing state dicts and metadata together—metadata as JSON primitives that pass the weights_only validation in torch 2.13. 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