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opportunity
Extract blood test data from PDF documents that have been OCR'd
The objective is to build a structured blood test database that allows pathology results to be viewed, edited, filtered, and exported to Excel via a web-based HTML interface. The system stores results in a clean, standardised format so trends can be analysed accurately over time. Using AI-assisted OCR, I have built a local Python extraction pipeline that converts PDF pathology reports into machine-readable text and inserts structured data into a SQLite database. The majority of blood tests extract correctly, including canonical test name, result value, unit, and reference range. However, I have reached a specific technical issue with three markers: • CRP (C-reactive protein) • ESR • GLU (Glucose) The OCR output clearly contains the correct lines, and debug logs confirm they are processed. Yet no rows are inserted for these markers. The failure appears to occur between canonical matching, numeric extraction, or validation logic. Current System Architecture The system runs locally and consists of: • extraction_core_2.py (main engine) • Supporting modules for OCR preprocessing, lab dictionary building, regex matching, and validation • SQLite backend • Schema-driven canonical lab dictionary • Controlled fuzzy fallback logic • HTML viewer for results display and Excel export Pipeline flow: Convert PDF to image (pdf2image) Preprocess Run Tesseract OCR Clean and normalise text Match against canonical lab dictionary Extract: canonical test name numeric result unit reference range Validate Insert into SQLite The engine is deterministic and rule-based. The Specific Problem Example OCR line: CRP H 5.2 mg/L 0-5 OCR text is correct. NUMBER_PATTERN matches. The canonical dictionary contains the test. Yet: Inserted 0 rows from 0126251OrderReport_23B00006604_CRP.pdf Likely failure points include: • Canonical containment match failing due to normalisation • Flag tokens (“H”, “L”) interfering with numeric capture • Numeric extraction anchored incorrectly • Validation rejecting due to strict range formatting • Unit pattern mismatch (e.g. mmol/L) • Dictionary indexing issue • Match overridden by another lab name • Guard conditions too strict If validation fails, the row is rejected silently. All other panels extract correctly. The issue appears isolated. What Is Required This is not a rebuild. We do not want: • Re-architecture • Experimental AI guessing logic • Large-scale changes • Expanded fuzzy matching We need: 1. Precise Diagnosis Identify exactly where CRP, ESR, and GLU are failing insertion and which rule is causing rejection. 2. Minimal Safe Fix Implement a targeted correction that: • Adjusts canonical matching if required • Anchors numeric extraction correctly • Allows flag tokens without blocking capture • Relaxes only necessary validation checks • Preserves deterministic behaviour 3. Zero Regression • No impact to currently working panels • No performance degradation • No uncontrolled fuzzy expansion 4. Modular Implementation If appropriate: • Implement as small isolated module or • Cleanly adjust matching block The existing architecture should remain intact. Constraints The system is designed to be: • Deterministic • Schema-driven • Reproducible • Forensic-grade We cannot introduce probabilistic or unpredictable behaviour. Longer-Term Goal After stabilising extraction: • Migrate to web deployment • Enable structured uploads • Add trend analysis • Later incorporate AI-assisted interpretation Immediate priority: Stabilise deterministic extraction for CRP, ESR, and GLU without breaking the existing engine. Materials Provided Uploaded: • Full extraction_core_2.py (text format) • Screenshot of HTML viewer • Sample PDF files • Export showing required output Additional materials available on request: • Sample OCR blocks • Canonical dictionary entries • Regex patterns • Validation logic • Database schema • Debug logs This is a focused debugging and refinement request. I have spent many hours attempting to isolate the issue and now require an experienced developer to identify the blocking condition and implement a practical fix. I have been advised this should take 1–2 hours for a senior developer. Looking for a swift turnaround.
opportunity
Tidy the server from X-ransom attack
Hello, Our Wordpress website was attacked by x-ransom. We have a backup of the WP and the database dump. We have detected some corrupted files there but it seems that there are still some left that were not detected. It has to be tidy after the attack. It’s an internet shop with uploads files of around 100GB. It’s stored on a private hosting in LV. What is done do far: 1. Update WordPress Version 2. Use z’d updateSecure WP-Admin Login Credentials 3. Set Up Safelist and Blocklist for the Admin Page 4. Use Trusted WordPress Themes 5. Install SSL Certificate 6. Remove Unused WordPress Plugins and Themes 1. Enable Two-Factor Authentication for WP-Admin 2. Back Up WordPress 3. Limit Login Attempts 4. Change the WordPress Login Page URL 5. Log Idle Users Out Automatically 6. Monitor User Activity 7. Check for Malware - found several none-Wordpress specious files and plugins. Deleted them. 1. Disable PHP Error Reporting 3. Turn File Editing Off 4. Restrict Access Using the .htaccess File 5. Change the Default WordPress Database Prefix - not done 6. Disable XML-RPC 7. Hide the WordPress Version 8. Block Hotlinking - not done 9. Manage File Permissions not done After making the list, we received another x-ransome attack. I suspect he has a server level access not only wp level. If you apply, you need to be a server security and a Wordpress specialist. Please, quote for the job.
