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Technical task: Program for automatic video assembly (local, for personal use) Briefly Program in Python, runs on my PC (Windows). Creates faceless videos in the format of "voiceover + changing visuals from photos and clips" — like historical documentaries on YouTube (references will be attached separately). I input a topic → the program writes a script, voices it, selects video/photos for each piece of text from free archives, assembles → outputs a ready MP4. For me only. No website, no users, no sales. Role division (important to understand) Claude (Anthropic API) — "brain": writes the script, divides it into scenes, selects the best frame by meaning. Works only with text and images. Code (ffmpeg/moviepy) — "hands": searches and downloads files from archives, cuts, glues, overlays sound, zooms, transitions, exports MP4. Claude does NOT edit video files — that’s done by the code. How it works (step by step) 1. Input. Simple window: I enter the topic of the video, choose a voice from the list (the list of voices is pulled from ElevenLabs via API). I click "Create". The second mode — insert a ready script. 2. Script. Claude writes a script on the topic of the specified length. 3. Scene breakdown. Claude divides the script into meaningful scenes (length by meaning, not fixed) and returns for each: scene text; type of visual: video or photo; search query in two formats at once (Claude provides both in one response, no extra queries or costs): expanded phrase — for stocks (Pexels/Pixabay), e.g. "18th century royal portrait, dramatic turning point"; brief keywords/names — for archives (Wikimedia, LoC, Archive.org, Europeana), e.g. "Marie Antoinette portrait". The code takes the appropriate format for each source. Claude generates the query based on the MEANING of the piece and the context of the entire video — even for abstract pieces without a clear object. There are no empty pieces "without a query"; highlight: true/false + importance rating 1-10 (for intro teaser). Meaningful scene ≠ frame. Claude divides the text by MEANING (so that the query is meaningful). The code then cuts this into visual frames of 4-6 seconds, adjusting the timing to the voiceover: short scene (≤6 sec) → 1 frame; long scene (e.g. 12 sec) → 2-3 frames of 4-6 sec each, with each — DIFFERENT, but close in meaning visual (same topic, different angles/frames), so that the thought does not hang on one static picture. The 4-6 sec rule applies to the frame, not to the meaningful breakdown — they are at different levels and do not conflict. 4. Voiceover. Text → ElevenLabs API with the selected voice → audio. The video sequence is cut to the length of the audio of each scene. 5. Frame selection (key process, see section below). 6. Assembly via ffmpeg/moviepy: clips/photos according to the timing of the voiceover, photos with zoom (Ken Burns effect), simple transitions, voice on top. Output: MP4 1920×1080. Frame selection (how video/photo is chosen for the scene) Search across all sources at once by the scene query. For each source, the appropriate query format is taken (see breakdown above): for archives — brief terms/names, for stocks — expanded phrase. The goal is to hit the topic, not a random picture. The code selects the top-5 candidates from all results by scores (scorer, see rules below). For video the code extracts 2-3 still frames from different points of the clip (beginning/middle/end) — so that Claude evaluates the clip by frames. Claude selects the best from 5 candidates by meaning (is the needed content realistically on the frame, does it fit the mood of the scene). Limit of 3 steps per scene: found a suitable one — stop; if nothing fits after checking — the code takes a more general photo by a simplified query (it will definitely be found) with enhanced movement. ALL frames are checked (the price difference is negligible, and quality is more important). Candidate selection rules (scorer) — to take good, not junk Relevance to the query (position in the source output) — main weight. Not used before — checked against a local database of used ones, do not repeat clips in my videos. Format — horizontal 16:9 prioritized. Quality — DIFFERENT for the type of source: modern stocks (Pexels/Pixabay) — filter out low quality, require HD; historical archives — low threshold/off, DO NOT lose film and documentary frames, even low resolution (their value is in the material itself, not in clarity). Clip length (for video) — is it enough to cover the scene. Diversity — do not take many identical frames in a row from one source, mix sources (so that the video sequence is diverse, not from one archive). Uniqueness (fewer matches with others' videos) Randomization of output (random from top-N, not always the first), search depth, local database of used ones (do not repeat clips in my videos), optionally — light processing of the clip (crop/mirror/speed). When unused frames for the query run out: the code first expands the search (other sources, deeper into the output, rephrasing the query). If that is exhausted — allows repeating previously used ones by priority: first those taken long ago (not in the latest videos), with mandatory processing for visual distinction. The video should not stall due to lack of material. Sources (all free, with API) Modern video + photo: Pexels, Pixabay. Historical / archival (public domain): Wikimedia Commons, Archive.org, Library of Congress, Europeana, NASA, Smithsonian Open Access, Flickr Commons, openverse. Each source is a separate module, easy to add a new one. The priority of sources depends on the topic (historical → archives first; modern → stocks first), configured in the settings. Only public domain / free licenses with the right to commercial use. No parsing of others' YouTube/sites, film clips, images "from Google". Video sequence rules Intro teaser (first 60 sec): a montage of the most striking frames from the entire video (scenes with the highest importance rating, where there is video) under a separate text-introduction from Claude ("in this video you will learn..."), frames without explanations — as intrigue. Then transition and main part. Alternation: video insert at least every ~6 sec, cannot have many photos in a row. Video share: at least ~40% of the time — live clips, the rest — photos with movement. Frame length: 4-6 sec. Do not flicker, do not hold static for long. For purely historical topics, where there is no video — photos with enhanced movement (zoom + panorama). Options (on/off in settings) Only photos — video purely from photos, without clips. Each photo must have movement (zoom/pan) — no static "dead" frames even in this mode. No voiceover — video is assembled by text without generating voice (the code does not call ElevenLabs). Otherwise — voiceover is done automatically. Atmospheric overlay (light leaks / dust & bokeh particles / film grain) — a semi-transparent layer with floating particles/light/easy fog over the video sequence, to make the frames look alive. Set 5-8 ready overlays to choose from; adjust transparency with a slider (0-100%); can also be applied to already finished video separately. Free overlays: Pexels, Pixabay, Mixkit, Videezy. Subtitles (embed or separate .srt). Clip processing for uniqueness. Resolution/format, video length, video share, search depth, number of candidates (default 5). LLM savings (set by default) Models — Claude (Anthropic API), separately for two tasks, parameter in settings: frame checking/selection (frequent operation) → Claude Haiku — cheap, analyzes still frames (images), for "suitable/not suitable" evaluation this is enough; script generation and breakdown (rare operation) → Claude Sonnet — for text quality. One API key for Claude gives access to all models (Haiku, Sonnet, etc.) — separate subscriptions are not needed. The model for each task can be changed in settings, without editing code. If no model is selected — the default model is used (does not break). Images are sent as previews of medium resolution (~512-768px on the long side) — sufficient for Claude to confidently recognize the content and assess its relevance to the scene. Do not send originals in full resolution (extra tokens), but also DO NOT reduce to the point of losing details — priority is on accuracy of assessment, savings are secondary. Preview resolution is a parameter in settings. Number of candidates per frame — parameter (default 5). Disk space management After assembly, all intermediate files (downloaded clips, temporary pieces) are automatically deleted — only the finished MP4 remains. The used database (list of clip IDs) is NOT included in auto-cleaning — this is a small text file, stored permanently. Only heavy media files are cleaned, not usage history. Cache limit (parameter, e.g. 5 GB): when exceeded, the oldest files are deleted. Folders for finished videos and temporary files are set in settings. Display of occupied space + "clear cache" button. Technical requirements Python. Modular structure (sources and LLM — interchangeable modules, to easily replace/add). All API keys are entered through the interface (section "Settings"), stored in the config, not in the code. Simple window (GUI — Tkinter/PyQt to choose), launched by double-click. All work is done through the interface (buttons, fields, sliders), without editing code. README (installation, keys, launch), clear logs, comments in the code. Interface (menu and control) Program with a side menu. Sections: New video — mode "generate by topic" OR "insert your script" (switch), voice selection, length, options, "Create" button. During assembly — progress screen by steps (script → voiceover → frame selection → assembly). My videos — list of finished videos. Voices — list of voices from ElevenLabs. Sources — list of all connected archives: shows added ones, can enable/disable each, set priority, and add your new source if you have its API key. Here also — source keys where registration is needed (Pexels, Pixabay, Europeana). Overlays — selection of atmospheric overlay and transparency. Settings — section "API keys" (Claude, ElevenLabs, etc. in one place), selection of Claude models, folders, resolution/format, cache limit. Clear cache — free up space. All keys and settings are entered through the interface, not through files/code. Result delivery All source code in open form (without obfuscation) + assembled working version. I can run it myself from the sources according to the instructions. The code is clean and understandable, so any other programmer can continue. All rights to the code after payment — mine. What I provide API keys (Claude, ElevenLabs, and sources where registration is needed). I pay for the paid ones myself. Examples of video references and topics for tests. Acceptance (ready if) I run → window → I enter the topic, choose a voice → "Create" → I receive a ready MP4. Video sequence according to the meaning of the text, alternation of video/photos, intro teaser 60 sec, voiceover on top. Works with at least 6 free sources, search across all at once + candidate selection. Frame selection: top-5 candidates from the code → selection of the best through Claude (for video — by still frames), limit 3 steps. Quality threshold depends on the source (archives — do not lose film frames).

It is necessary to set up the parser remotely (via Anydesk or TeamViewer), something is broken and not working correctly. Mainly replacing the starting links and configuring the parser. Website https://dveri-pol.com/

# Project Overview Premium Haztrak is a modern environmental compliance platform designed to help organizations manage hazardous-waste tracking, electronic manifest workflows, and regulatory compliance records in a more secure, transparent, and legally defensible way, specifially designed for Office of Chief Counsel, PA Department of Environmental Protection, USA. The platform is built around hazardous-waste cradle-to-grave tracking, allowing generators, transporters, facilities, compliance teams, and legal professionals to manage key records, shipment events, supporting documents, signatures, corrections, and audit history in one structured system. The next phase of the project will introduce blockchain-based verification to strengthen record integrity, chain of custody, and regulatory audit readiness without exposing sensitive compliance data on-chain. # Current Version Status The current version provides the foundation for electronic hazardous-waste tracking and demonstrates how a digital system can support EPA e-Manifest and RCRAInfo-related workflows. At this stage, the platform focuses on core hazardous-waste record management, including manifest-related workflows, shipment tracking concepts, compliance documentation, and integration patterns for working with official environmental systems. The current system should be treated as the base layer for a more advanced compliance product. The goal now is to expand it into a premium version with stronger legal, audit, and verification capabilities. # New Integration Goals The main enhancement will be a blockchain-powered compliance verification layer. This blockchain layer will not store private hazardous-waste records directly on-chain. Instead, the system will generate cryptographic proofs for important documents and events, then anchor those proofs to blockchain so records can be independently verified later. # Key integration points include: Blockchain-based proof of manifest records Tamper-evident chain-of-custody tracking Hashing of documents, signatures, corrections, and shipment events Verifiable audit logs for regulatory and legal review Evidence packet generation for disputes, audits, or investigations Secure off-chain storage for sensitive data On-chain proof references for record integrity verification Compliance dashboard for high-risk, incomplete, or disputed records Core Product Features Premium Haztrak should include: Manifest and shipment management Organization and user role management Document upload and record storage Signature and approval tracking Correction and dispute history Chain-of-custody timeline Blockchain proof generation Verification page for documents and records Audit-ready PDF export Legal evidence packet generation Compliance activity dashboard Blockchain Integration Concept The blockchain component will work as a proof layer. When a critical event happens, such as a manifest being created, signed, corrected, received, or finalized, the system will generate a hash of that event or document. These hashes can be grouped and anchored to blockchain through a smart contract or verification registry. This allows a user, auditor, attorney, or regulator to later confirm whether a record matches the original verified version. The private data stays protected in the application database or secure storage. The blockchain only stores verification proofs. #Product Value Premium Haztrak improves environmental compliance by making records: More traceable More trustworthy Easier to audit Easier to verify Better prepared for legal review More resistant to tampering or undocumented changes The product is especially useful for organizations that need strong documentation, reliable compliance history, and defensible records in case of audits, enforcement questions, disputes, or litigation. #Long-Term Vision The long-term vision of Premium Haztrak is to become a trusted digital compliance layer for environmental records. Starting with hazardous-waste tracking and electronic manifest workflows, the platform can later expand into broader environmental compliance areas such as permits, inspections, remediation records, enforcement documentation, ESG reporting, and carbon-credit verification. Premium Haztrak’s goal is simple: To make environmental compliance records secure, traceable, verifiable, and legally defensible.

A modern online toy store needs to be created on WordPress (WooCommerce). Main requirements: - Responsive design for PCs, tablets, and smartphones. - Product catalog with categories, filters, and search. - Product pages with descriptions, photos, specifications, and prices. - Cart and checkout. - Integration of popular payment and delivery methods. - Customer personal account. - User-friendly control panel for adding and editing products. - Basic SEO optimization and high website loading speed. - Security, backup, and SSL settings. It is preferable to use a modern, lightweight theme or create a custom design. After the work is completed, a brief management guide for the website must be provided.

Data collection is required on buyers and sellers of art items. Databases from Ebay and specialized global art websites are of interest.