forked from 0xWheatyz/SPARC
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1 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| 44a162056d |
+76
-118
@@ -7,131 +7,86 @@ Semiconductor Patent & Analytics Report Core -- development priorities.
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SPARC is a patent analysis platform with a working end-to-end pipeline:
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Python/FastAPI backend, React/TypeScript frontend, PostgreSQL for persistence
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and caching, Docker Compose for local development, and Gitea Actions CI/CD for
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image builds and testing. Core features include patent retrieval via SerpAPI,
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PDF parsing, LLM analysis via OpenRouter (multi-model: Claude, GPT-4o, Gemini,
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Llama), batch processing, JWT authentication, analytics dashboard with patent
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trend charts, scheduled recurring analysis with alerting, webhook notifications
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(Slack/Discord), CSV and PDF export, S3/MinIO storage, side-by-side company
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comparison, and dark mode.
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---
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## Completed
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Items that have been implemented and merged into main.
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### Security hardening
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- ~~Rotate default JWT secret.~~ Startup check refuses to start with the
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default secret in non-development environments.
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- ~~CORS allow-origins are hardcoded.~~ Allowed origins are now configurable
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via environment variable.
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- ~~Database credentials in docker-compose.yml.~~ Compose references `.env`
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for sensitive values.
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### Error handling and resilience
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- ~~`get_db_client()` creates a new `DatabaseClient` on every call.~~ Refactored
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to a shared pooled singleton initialized at startup.
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- ~~No rate limiting on auth endpoints.~~ Rate limiting middleware added to
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`/auth/login` and `/auth/register`.
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### Test coverage
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- ~~API tests bypass authentication.~~ JWT auth integration tests added (33
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cases covering registration, login, protected routes, token refresh, and
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admin-only endpoints).
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- ~~No test stage in CI.~~ Gitea Actions workflow now runs `pytest` and gates
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the build.
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- ~~No linting or type checking in CI.~~ `ruff` (Python) and `tsc --noEmit`
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(TypeScript) added to CI pipeline.
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### Backend
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- ~~Add structured logging.~~ Python `logging` module used throughout.
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- ~~Make LLM model configurable.~~ `MODEL` environment variable accepted;
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multi-model support with per-analysis selection (GPT-4o, Gemini, Claude,
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Llama).
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- ~~SERP cache TTL hardcoded.~~ `SERP_CACHE_TTL_HOURS` exposed as env var.
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- ~~Patent PDF storage.~~ S3/MinIO object storage backend added alongside
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local filesystem. Volume mount requirement documented.
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- ~~`analyze_single_patent` assumes local file.~~ Auto-download from cached
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metadata link integrated.
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- ~~`Patent.patent_id` typed as `int`.~~ Fixed to `str`.
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### Frontend
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- ~~No loading/error states.~~ Skeleton loaders and error states added to
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Batch and Analytics pages.
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- ~~No dark mode.~~ Full dark mode support with theme-aware chart colors.
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- ~~Missing lockfile.~~ `package-lock.json` committed.
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### Features (formerly P3)
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- ~~Export analysis reports.~~ CSV and PDF export endpoints implemented.
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- ~~Comparison view.~~ Side-by-side company patent portfolio comparison added.
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- ~~Scheduled/recurring analysis.~~ APScheduler-based periodic re-analysis
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with configurable interval and change-threshold alerting.
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- ~~Webhook/notification support.~~ Slack, Discord, and generic HTTP POST
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webhooks with retry logic.
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- ~~Multi-model support.~~ Model picker in Analysis and Batch pages; backend
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allow-list validation.
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- ~~Patent trend charts.~~ Filing frequency and category distribution
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visualizations added to Analytics page.
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- ~~OpenAPI client generation.~~ TypeScript API client auto-generated from
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FastAPI spec with CI freshness check.
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image builds. Core features (patent retrieval via SerpAPI, PDF parsing, LLM
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analysis via OpenRouter/Claude, batch processing, JWT authentication, analytics
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dashboard) are all implemented and functional.
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---
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## P1 -- High Priority
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These items address correctness, reliability, and coverage gaps that should be
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These items address correctness, security, and reliability gaps that should be
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resolved before broader production use.
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### Resilience
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### Security hardening
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- **`_jobs` dict is in-memory only.** Job state is lost on API restart.
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Persist job status in PostgreSQL or Redis so async batch results survive
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restarts.
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- **Rotate default JWT secret.** `auth.py` ships a fallback
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`sparc-secret-key-change-in-production` that will be used if `JWT_SECRET` is
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unset. Add a startup check that refuses to start with the default secret in
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non-development environments.
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- **CORS allow-origins are hardcoded.** `api.py` only permits
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`localhost:3000` and `localhost:5173`. Make the allowed origins configurable
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via environment variable so the dashboard works when deployed behind a real
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domain.
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- **Database credentials in docker-compose.yml.** The compose file embeds
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`postgres:postgres` in plain text. Reference a `.env` file or Docker secrets
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instead.
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### Test coverage gaps
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### Error handling and resilience
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- **Export endpoint tests.** The CSV and PDF export endpoints (`/export/`)
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lack test coverage. Add tests covering auth, success, 404, and edge cases.
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*(Issue #1655)*
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- **Tracked company admin endpoint tests.** The `/admin/tracked` CRUD
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endpoints and scheduler integration lack test coverage. *(Issue #1656)*
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- **`get_db_client()` in `auth.py` creates a new `DatabaseClient` on every
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call.** This bypasses the connection pool and can exhaust database
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connections under load. Refactor to share a single pooled client.
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- **`_jobs` dict is in-memory only.** Job state is lost on API restart. Persist
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job status in PostgreSQL or Redis so async batch results survive restarts.
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- **No rate limiting on auth endpoints.** `/auth/login` and `/auth/register`
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are unprotected against brute-force or abuse. Add rate limiting middleware.
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### Test coverage for auth and admin
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- The existing API tests (`tests/test_api.py`) bypass authentication entirely.
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Add tests that exercise the JWT flow: registration, login, protected-route
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access, token refresh, and admin-only endpoints.
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---
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## P2 -- Medium Priority
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Improvements to reliability, test coverage, and code quality.
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Improvements to usability, performance, and developer experience.
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### Test coverage
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### Backend
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- **Webhook integration tests.** The retry logic, Slack/Discord payload
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format, and multi-URL dispatch in `webhooks.py` need test coverage.
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*(Issue #1657)*
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- **S3/MinIO storage backend tests.** `storage.py` has local filesystem tests
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but no unit tests for the S3 backend (read, write, exists, delete,
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error handling). *(Issue #1660)*
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- **`analyze_single_patent` auto-download path tests.** The auto-download
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fallback (cache lookup, PDF download, FileNotFoundError) in
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`analyzer.py` lacks test coverage. *(Issue #1661)*
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- **Add structured logging.** Replace `print()` calls throughout `analyzer.py`,
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`serp_api.py`, and `llm.py` with Python `logging` so log levels and
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formatting are consistent.
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- **Make LLM model configurable.** `llm.py` hardcodes
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`anthropic/claude-3.5-sonnet`. Accept a `MODEL` environment variable to allow
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switching models without code changes.
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- **SERP cache TTL is hardcoded to 24 hours.** Expose `SERP_CACHE_TTL_HOURS`
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as an environment variable in `config.py`.
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- **Patent PDF storage.** PDFs are saved to a local `patents/` directory. For
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containerized deployments, consider object storage (S3/MinIO) or at minimum
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document the volume mount requirement more prominently.
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- **`analyze_single_patent` assumes local file path.** The method constructs
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`patents/{patent_id}.pdf` and reads from disk, but does not download the PDF
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first. Either integrate the download step or document the prerequisite.
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- **`Patent.patent_id` typed as `int` in `types.py` but used as `str`
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everywhere.** Fix the type annotation to `str`.
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### Code quality
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### Frontend
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- **Scheduler creates its own DatabaseClient.** `scheduler.py` bypasses the
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application-level pooled client, creating a new connection on every tick.
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Refactor to use `get_db_client()`. *(Issue #1658)*
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- **No loading/error states on several pages.** The Batch and Analytics pages
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would benefit from skeleton loaders and user-friendly error messages.
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- **No dark mode.** Tailwind is configured but no dark variant is applied.
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- **Missing `package-lock.json` or `pnpm-lock.yaml`.** The frontend has no
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lockfile committed, leading to non-reproducible builds.
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### API improvements
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### CI/CD
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- **API pagination.** The `/analyze/batch` and `/jobs` endpoints could benefit
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from cursor-based pagination for large result sets.
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- **Request validation improvements.** Add stricter input validation for
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company names (disallow special characters, enforce length limits).
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- **No test stage in the Gitea Actions workflow.** `build.yaml` builds and
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pushes images but never runs `pytest`. Add a test job that gates the build.
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- **No linting or type checking.** Add `ruff` (Python) and `tsc --noEmit`
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(TypeScript) to CI.
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---
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@@ -139,20 +94,23 @@ Improvements to reliability, test coverage, and code quality.
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Lower-urgency enhancements and future features.
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- **Historical analysis diffing.** Show what changed between two analysis runs
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for the same company, highlighting new patents and score shifts.
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- **Patent classification tagging.** Automatically tag patents by technology
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domain (AI, semiconductors, materials science) using LLM classification.
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- **User-level API keys.** Allow users to generate personal API keys for
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programmatic access without JWT token refresh.
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- **Batch export.** Export analysis results for multiple companies at once as
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a ZIP archive.
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- **Rate limiting dashboard.** Surface rate limit status and usage statistics
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in the admin panel.
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- **Async webhook delivery.** Move webhook delivery to a background task queue
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(e.g., Celery, arq) to avoid blocking the scheduler.
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- **Multi-tenant support.** Scope analysis results and tracked companies per
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user or organization.
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- **Export analysis reports.** Allow users to download analysis results as PDF
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or CSV from the dashboard.
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- **Comparison view.** Side-by-side comparison of two companies' patent
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portfolios.
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- **Scheduled/recurring analysis.** Periodically re-analyze tracked companies
|
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and alert on significant changes.
|
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- **Webhook/notification support.** Send alerts (Slack, Discord, email) when
|
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batch jobs complete or when a company's innovation score changes
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significantly.
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- **Multi-model support.** Let users choose between LLM providers per analysis
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(e.g., GPT-4o, Gemini, Claude) and compare outputs.
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- **Patent trend charts.** Visualize patent filing frequency and technology
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category distribution over time in the Analytics page.
|
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- **API pagination.** The `/analyze/batch` and `/jobs` endpoints could benefit
|
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from cursor-based pagination for large result sets.
|
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- **OpenAPI client generation.** Auto-generate the TypeScript API client from
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the FastAPI OpenAPI spec to keep frontend types in sync.
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---
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@@ -0,0 +1,224 @@
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"""Tests for export endpoints: CSV and PDF export of analysis results.
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Covers issue #1655:
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- GET /export/{company_name} (CSV export)
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- GET /export/{company_name}/pdf (PDF export)
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All tests mock the database layer and use JWT auth fixtures from test_auth patterns.
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"""
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from datetime import datetime, timezone
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from unittest.mock import MagicMock, patch
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import pytest
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from fastapi.testclient import TestClient
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from SPARC.api import app
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from SPARC.auth import create_access_token
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@pytest.fixture
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def client():
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"""Create test client."""
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return TestClient(app)
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@pytest.fixture(autouse=True)
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def mock_db():
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"""Mock the database client used by export and auth endpoints."""
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db = MagicMock()
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# Default: user exists for auth
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db.get_user_by_id.return_value = {
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"id": 1,
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"email": "user@test.com",
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"role": "user",
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"created_at": datetime(2025, 1, 1, tzinfo=timezone.utc),
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}
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# Mock get_conn for export queries
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mock_cursor = MagicMock()
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mock_conn = MagicMock()
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mock_conn.cursor.return_value.__enter__ = MagicMock(return_value=mock_cursor)
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mock_conn.cursor.return_value.__exit__ = MagicMock(return_value=False)
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db.get_conn.return_value.__enter__ = MagicMock(return_value=mock_conn)
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db.get_conn.return_value.__exit__ = MagicMock(return_value=False)
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db._mock_cursor = mock_cursor
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with patch("SPARC.api.get_db_client", return_value=db), \
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patch("SPARC.auth.get_db_client", return_value=db):
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yield db
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def _auth_header():
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"""Create an Authorization header with a valid access token."""
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token = create_access_token(1, "user@test.com", "user")
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return {"Authorization": f"Bearer {token}"}
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def _sample_rows():
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"""Return sample llm_messages rows as tuples (matching cursor.fetchall format)."""
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return [
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(
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"NVIDIA",
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"company_analysis",
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"anthropic/claude-3.5-sonnet",
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"Strong AI patent portfolio with focus on GPU architectures.",
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datetime(2025, 6, 15, 10, 30, 0),
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),
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(
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"NVIDIA",
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"patent_analysis",
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"openai/gpt-4o",
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"Patent US-12345678-B2 covers novel tensor core design.",
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datetime(2025, 6, 14, 9, 0, 0),
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),
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]
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class TestCSVExport:
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"""GET /export/{company_name} -- CSV export."""
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def test_csv_export_success(self, client, mock_db):
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"""Valid company with results returns a CSV file."""
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mock_db._mock_cursor.fetchall.return_value = _sample_rows()
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response = client.get("/export/NVIDIA", headers=_auth_header())
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assert response.status_code == 200
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assert response.headers["content-type"].startswith("text/csv")
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assert "attachment" in response.headers.get("content-disposition", "")
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assert "sparc_nvidia_export.csv" in response.headers["content-disposition"]
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# Verify CSV content (CSV uses \r\n line endings)
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lines = response.text.strip().split("\n")
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assert len(lines) == 3 # header + 2 data rows
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assert lines[0].strip() == "company_name,analysis_type,model,analysis,timestamp"
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assert "NVIDIA" in lines[1]
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assert "company_analysis" in lines[1]
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def test_csv_export_no_results_returns_404(self, client, mock_db):
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"""Unknown company returns 404."""
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mock_db._mock_cursor.fetchall.return_value = []
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response = client.get("/export/nonexistent", headers=_auth_header())
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assert response.status_code == 404
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assert "No analysis results found" in response.json()["detail"]
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|
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def test_csv_export_unauthenticated_returns_401(self, client):
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"""Request without token returns 401."""
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response = client.get("/export/NVIDIA")
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assert response.status_code == 401
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|
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def test_csv_export_invalid_token_returns_401(self, client):
|
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"""Request with invalid token returns 401."""
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response = client.get(
|
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"/export/NVIDIA",
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headers={"Authorization": "Bearer invalid.token.here"},
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)
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assert response.status_code == 401
|
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|
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def test_csv_export_filename_sanitization(self, client, mock_db):
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"""Company names with spaces get sanitized in the filename."""
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mock_db._mock_cursor.fetchall.return_value = [
|
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(
|
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"Tesla Motors",
|
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"company_analysis",
|
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"anthropic/claude-3.5-sonnet",
|
||||
"EV patent portfolio analysis.",
|
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datetime(2025, 6, 15, 10, 0, 0),
|
||||
),
|
||||
]
|
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|
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response = client.get("/export/Tesla Motors", headers=_auth_header())
|
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|
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assert response.status_code == 200
|
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assert "tesla_motors" in response.headers["content-disposition"]
|
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|
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def test_csv_export_single_row(self, client, mock_db):
|
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"""Single analysis result produces valid CSV with one data row."""
|
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mock_db._mock_cursor.fetchall.return_value = [_sample_rows()[0]]
|
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|
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response = client.get("/export/NVIDIA", headers=_auth_header())
|
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|
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assert response.status_code == 200
|
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lines = response.text.strip().split("\n")
|
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assert len(lines) == 2 # header + 1 data row
|
||||
|
||||
|
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class TestPDFExport:
|
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"""GET /export/{company_name}/pdf -- PDF report export."""
|
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|
||||
def test_pdf_export_success(self, client, mock_db):
|
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"""Valid company with results returns a PDF file."""
|
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mock_db._mock_cursor.fetchall.return_value = _sample_rows()
|
||||
|
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response = client.get("/export/NVIDIA/pdf", headers=_auth_header())
|
||||
|
||||
assert response.status_code == 200
|
||||
assert response.headers["content-type"] == "application/pdf"
|
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assert "attachment" in response.headers.get("content-disposition", "")
|
||||
# PDF files start with %PDF
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assert response.content[:4] == b"%PDF"
|
||||
|
||||
def test_pdf_export_no_results_returns_404(self, client, mock_db):
|
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"""Unknown company returns 404."""
|
||||
mock_db._mock_cursor.fetchall.return_value = []
|
||||
|
||||
response = client.get("/export/nonexistent/pdf", headers=_auth_header())
|
||||
|
||||
assert response.status_code == 404
|
||||
assert "No analysis results found" in response.json()["detail"]
|
||||
|
||||
def test_pdf_export_unauthenticated_returns_401(self, client):
|
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"""Request without token returns 401."""
|
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response = client.get("/export/NVIDIA/pdf")
|
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assert response.status_code == 401
|
||||
|
||||
def test_pdf_export_invalid_token_returns_401(self, client):
|
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"""Request with invalid token returns 401."""
|
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response = client.get(
|
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"/export/NVIDIA/pdf",
|
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headers={"Authorization": "Bearer invalid.token.here"},
|
||||
)
|
||||
assert response.status_code == 401
|
||||
|
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def test_pdf_export_filename_contains_date(self, client, mock_db):
|
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"""PDF filename includes the analysis date."""
|
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mock_db._mock_cursor.fetchall.return_value = _sample_rows()
|
||||
|
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response = client.get("/export/NVIDIA/pdf", headers=_auth_header())
|
||||
|
||||
assert response.status_code == 200
|
||||
disposition = response.headers["content-disposition"]
|
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assert "nvidia-analysis-" in disposition
|
||||
assert ".pdf" in disposition
|
||||
|
||||
def test_pdf_export_special_chars_in_response(self, client, mock_db):
|
||||
"""Analysis text with XML-special chars (<, >, &) does not break PDF generation."""
|
||||
rows = [
|
||||
(
|
||||
"TestCo",
|
||||
"company_analysis",
|
||||
"anthropic/claude-3.5-sonnet",
|
||||
"Revenue > $1B & growth <20% for Q4. Test <html> escaping.",
|
||||
datetime(2025, 6, 15, 10, 0, 0),
|
||||
),
|
||||
]
|
||||
mock_db._mock_cursor.fetchall.return_value = rows
|
||||
|
||||
response = client.get("/export/TestCo/pdf", headers=_auth_header())
|
||||
|
||||
assert response.status_code == 200
|
||||
assert response.content[:4] == b"%PDF"
|
||||
|
||||
def test_pdf_export_multiple_analyses(self, client, mock_db):
|
||||
"""Multiple analysis records produce a valid PDF with content."""
|
||||
mock_db._mock_cursor.fetchall.return_value = _sample_rows()
|
||||
|
||||
response = client.get("/export/NVIDIA/pdf", headers=_auth_header())
|
||||
|
||||
assert response.status_code == 200
|
||||
# PDF should have reasonable size (more than just headers)
|
||||
assert len(response.content) > 500
|
||||
Reference in New Issue
Block a user