Python

A system needs to be created for managing OnlyFans/Fansly accounts. The technical specifications are attached. Please indicate the price and deadlines for the entire project in your bid according to the technical specifications. Feel free to ask any questions or clarifications in the discussions -- I will respond. Please review the technical specifications before submitting your bid.

need someone who can create solutions in Pocket Option create a bot strategy algorithm waiting for a specialist to join the team. for a reasonable price

We are looking for a developer (Python / AI / Integration) to create a Telegram bot that will completely replace the operator for receiving orders for the supply of vegetables and fruits from restaurants. The main task of the bot is to accept unstructured requests in any format (text, photos of handwritten lists, voice messages), convert them to our nomenclature, and send the completed order to 1C. Key functionality: Receiving and recognizing requests (AI module): Recognition of free text, voice messages (Whisper / SpeechKit), and photos/scans (Vision OCR). Entity extraction: item, quantity, unit of measure (kg, boxes, pieces). Nomenclature mapping: Mapping of client slang/abbreviations (“potato”, “cherry”, “bulb onion”) to the official product catalog of the company. Feedback function: if the item or quantity is unclear, the bot asks a clarifying question to the client in the chat. Confirmation and integration: Displaying the final order to the client for confirmation (“Your order: Potatoes — 50 kg, Cherry Tomatoes — 10 kg. Is everything correct?”). Sending the generated order via API to 1C (creating a “Customer Order” document). Implementation stages (MVP and beyond): Stage 1: Bot logic, processing text/voice/photos, and outputting structured JSON. Stage 2: Setting up mapping with the nomenclature database. Stage 3: Setting up data transfer to 1C (REST API / HTTP services). Requirements for the performer: Experience working with OpenAI / Claude APIs and speech/photo recognition libraries. Proficient in Python (Aiogram / Telebot / FastAPI). Experience integrating external services with 1C (having ready cases will be a key advantage).

A local solution (script/emulator/proxy) for Windows is required, which will provide the Lovense Cam Extension in the browser with a constant status of a connected toy (Lush 3). Main goal: The extension in Chrome should show “green” (Connected) 24/7 without the involvement of a mobile phone, original USB dongle, and without a real Bluetooth device. What is submitted as a result: Complete source code of the repository/archive (without obfuscation). A README.md file with a detailed manual for deployment from scratch on a clean Windows. Demonstration of operation: with the script running, the Lovense Cam Extension in Chrome shows a stable “Green” status. When responding, please first write the anti-bot word: dsqwueq

Our old custom system has outlived its usefulness, we need to grow. We are looking for an experienced professional to implement ERPNext. Must have experience, our field is wholesale and retail trade. There will be a lot of different standard and non-standard solutions, as well as classic connections to PRRO, Bank Mono and Privat, Nova Poshta, marketplaces, and so on. Ready?! :) Then send your resume and let's get to know each other.

General information: There is a Telegram bot written in Python (aiogram v3, aiohttp, MySQL, Redis). This is a service for interaction between clients and performers (profiles, projects, etc.). Required skills: - Confident experience with aiogram v3 (not v2!), asyncio, aiohttp; - Knowledge of Telegram Bot API (webhooks); - Experience with MySQL, Redis; - Ability to understand someone else's code; - Communication skills, responsibility. I need to fix a bug in the current version of the bot directly on the server. The problem is as follows: The chats (Nooks) are not functioning properly; there should be a cleaner that works according to an algorithm, but right now it is working with bugs and not cleaning on schedule. - A chat between the client is needed for discussing work matters with the performer. - Chats (nooks are already created, they need to be cleaned and queued. - The user bot creates chats (pair nooks, which we already have) and puts them in the queue; if a deal is made in the chat, the interlocutors are present (one chat for the performer, another chat for the client); if the chat is closed or there was no deal made and users did not write there for 7 days, the bot removes clients or performers from there and puts this chat (nook) back in the queue. - The client can switch to a chat with any performer who responded to the order. - The client and performer are in two parallel chats, so there is no possibility to see the direct account of the interlocutor and no possibility to write to them directly. * We do not change the operation of the chats and everything else. The main thing is that the fix does not lead to other problems.

Auto-notification system for inventory replenishment analytics. On 8n8. Small automation for e-commerce. Once a day, the system calculates the sales velocity for each product and sends a message in Telegram indicating when to reorder (with the recommended quantity) and how many days the remaining stock will last. What we do: Data retrieval: KeyCRM API (order history) + stock/orders from Rozetka (API or export) Storage in SQLite on my VPS (Contabo, Linux) Calculation of average sales, days of stock, reorder point ABC analysis: prioritization of which product to replenish first (class A — main by revenue — more important) Daily Telegram bot + cron job All thresholds (lead time, safety stock, target stock) — in the config

Hello! I need to visually customize the InvenTree system (open-source) for a coffee shop project presentation. A complex backend and integration with the cash register (POS) are not required — the external redesign of the interface is important to show the difference between "Before / After". Project stack: Python (Django) + React (Mantine UI). What needs to be done in the interface: Custom branding, Renaming in the sidebar menu and tables, Cleaning forms from engineering fields, Setting up one recipe for "making" a drink (for example, Cappuccino), so that when manually deducted from the warehouse, all ingredients are visually subtracted, adding functions in the form of additional reports, and explaining the principles of the changes made. We will discuss all specific details in person. Requirements for the performer: Experience with Django and React (Mantine UI). Ability to complete the task quickly — the project is needed for a presentation. We work through Safe (Secure transaction). In your response, start with the word "COFFEE", so I know you read to the end. Please provide your timelines and price.

A small adjustment is needed in the program that generates print orders. Task: We have added to the assortment soft glass with print. In the orders, the name of the print will be highlighted with the symbols #...# for correct recognition. Example: Soft glass, #White abstraction# 2.0mm, Shape: Rectangle... Requirements: If the text of the order contains a fragment between the symbols #...#, the program should recognize it as an order with print; when generating a PDF or print sheet, such an order needs to be highlighted in a different color (any noticeable color, to be agreed upon during execution); the rest of the program's logic should remain unchanged. An example of the order is attached below for testing.

About the project: We are launching a B2B service for end-to-end analytics and advertising campaign management for targeters and media buyers. The product will work with the official Meta API. The main technical challenge and focus of the project is the virtuoso handling of Facebook limits, traffic routing between our application pool, and a robust infrastructure protection system against the blocking of gray advertising accounts. We already have a detailed technical specification, the database architecture is described, the load balancer logic, and interface requirements. We are looking for a contractor who will take this for turnkey implementation (backend + frontend dashboards). What needs to be done (Key tasks): Integration with Meta API: Set up user authorization and regular asynchronous parsing of advertising account statistics. Requests should be sent exclusively in batches to save limits. Auto-unlinking system for accounts: Write a module that continuously monitors the statuses of advertising accounts. If an account gets banned, the system should automatically revoke the access token within 60 seconds to protect our application from Meta sanctions. Proxy infrastructure: Implement tunneling of all API requests through a SOCKS5 pool. A strict binding of a specific user token to a static IP address is mandatory. Smart routing: Create an algorithm that will distribute bindable advertising accounts among several of our Facebook applications in specified proportions to reduce risks. Interface development: Create a client dashboard with a summary statistics table and an advanced admin panel for manual management of user limits, application bindings, and proxy pool. Expected technology stack: Backend: Python, FastAPI. Asynchronous tasks: Celery, Redis. Databases: PostgreSQL (or ClickHouse for statistics, at your discretion). Frontend: Vue.js or React (you can use ready-made UI libraries and dashboard templates, focusing on functionality rather than complex design). Requirements for the contractor: Confident experience with Meta Graph API and Marketing API. You should understand how sliding limits work, how to read load headers, and how to work with tokens. Understanding the specifics of traffic arbitration. The words "billing," "account ban," "business manager," and "farm" should not raise questions for you. Experience in building asynchronous parsers and working with proxy servers at the network request level. Willingness to work according to a clear technical specification and deliver the project in stages. Conditions: Collaboration format: Project work Budget: Discussed individually based on your assessment of the technical specification. Payment: Staged, tied to checkpoints. How to respond: In your cover letter, be sure to indicate your experience with Meta API, attach links to similar projects (or describe their functionality if they are under NDA), and provide an estimated price range and timeline for developing such a system from scratch. Responses without a description of relevant experience with Facebook API will not be considered.

Brief I need a program in Python that runs on my PC (Windows) and creates faceless videos in the format of "voiceover + changing visuals from photos and clips" — like historical documentaries on YouTube (I will attach an example separately). I input a topic → the program writes a script, voices it, selects video/photos from free archives for each piece of text, edits them together → outputs a ready MP4. For me only. No website, no users, no sales. One user — me. How it works (step by step) 1. Input. A simple window opens. I enter the topic of the video and choose a voice from the list (**the list of voices is automatically pulled from ElevenLabs via API** — available on my account). I click "Create." (Second mode: insert a ready script instead of generating.) 2. Script. The program writes a script on the topic of the specified length via LLM API (OpenAI/Anthropic, key in settings). 3. Scene breakdown. LLM divides the script into scenes and returns for each: scene text; visual type: video or photo; search query (a detailed phrase of what should be in the frame); highlight mark + importance rating 1-10 (for intro, see below). 4. Voiceover. The text is sent to ElevenLabs API with the chosen voice → audio. The video sequence is cut to match the length of the audio for each scene. 5. Video/photo selection — from free sources via API (see the list below). A specific request is formed for each source (for accuracy). If one doesn't yield results — it tries the next. 6. Verification (maximum 3 steps per scene). Step 1: the program takes the first found option (video/photo) based on the request. Step 2: sends the frame to LLM — "does it fit the scene?". If it fits → stop. Step 3 (if it doesn't fit): the program switches to searching for photos (it's easier to find photos with an exact request than videos — this guarantees relevance) and takes it with enhanced motion (zoom + pan). No more than 3 steps per scene — this saves the LLM budget and ensures that the frame is relevant. 7. Assembly via ffmpeg/moviepy: clips and photos timed to the voiceover, photos are animated with zoom (Ken Burns effect), voice on top, simple transitions. Output: MP4 1920×1080. Video sequence rules (important — the program is responsible for this) First 60 seconds — intro teaser: a montage of the most impactful clips from the entire video (scenes with the highest importance rating that contain video) under a separate introductory text from LLM ("in this video you will learn..."), frames without explanations, creating intrigue. Then a transition to the main part. Alternation: video insert at least every ~6 seconds, no many photos in a row. Video share: at least ~40% of the time — live clips, the rest — photos with zoom. Frame length: 4-6 seconds (both photos and videos). No rapid cuts, no prolonged static shots. For purely historical topics where there is no video — photos with enhanced motion (zoom + pan). Sources (all free, with API) Modern video + photos: Pexels, Pixabay. Historical/archive (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 new ones. Use only public domain / free licenses with the right for commercial use. No parsing of other YouTube/sites, movie clips, images "from Google".Uniqueness of selection To ensure videos do not match others: take a random clip from the top results (not the first), maintain a database of already used clips (do not repeat), optionally — light processing of the clip (crop/mirror/speed). Options (enable/disable in settings) Photos only — if enabled, the video is assembled PURELY from photos, without video clips. Each photo MUST have motion (zoom and/or pan, Ken Burns effect) — even in this mode, there should be no static "dead" frames, minimal dynamics always. If disabled — standard mode (photos + video clips alternating, as per video sequence rules). No voiceover — if enabled, the video is assembled based on the text WITHOUT generating voice: the code DOES NOT call ElevenLabs and does not overlay voice (the video sequence is selected based on the text of the scenes, timing of frames — according to rules/parameters, without reference to audio). If disabled — it automatically generates voiceover based on the text through ElevenLabs, as usual. Atmospheric overlay — if enabled, a semi-transparent layer with floating particles/dust/glowing bokeh/light fog (particle/dust/bokeh/fog overlay, screen/add mode) is applied over the entire video sequence to make the frames look alive and cinematic. When installing the program, a set of 5-8 popular overlays (particles, dust,copybokeh, fog, light cinematic "grain") is placed in a local folder — I choose the needed one from the list. Adjustable transparency/brightness of the overlay (slider 0-100%), so the effect is neithercopytoo dull nor too pronounced — I adjust the strength myself. Ideally, the overlay should also be applicable to ALREADY finished videos separatelycopy(post-processing: take a ready MP4 → choose overlay → set transparency → save), not just during assembly. Where to get overlays for packaging (free license): Pexels, Pixabay (queriescopy"particle overlay", "bokeh overlay", "dust overlay", "light leaks", "film grain"), Mixkit, Videezy. The performer selects 5-8 pieces and places them in the program folder. Subtitles (embed or separate .srt). Clip processing for uniqueness. Resolution/format, video length, video share, search depth.Technical requirements Python. Modular structure (sources and LLM — through interchangeable modules, to easily replace or add). All API keys — in the settings file, not in the code. Simple window (GUI at the discretion of the performer — Tkinter/PyQt), launched by double-clicking. README with instructions, clear logs, comments in the code.What I provide API keys (ElevenLabs, LLM, where registration is needed — I will arrange). I will pay for any fees myself. Examples of video references (I will attach) and examples of topics for tests.Acceptance (ready if) I launch → window → I enter the topic, choose the voice → "Create" → I receive a ready MP4. The video sequence matches the meaning of the text, alternating video/photos, intro teaser 60 sec, voiceover on top. Works with at least 6 free sources, with fallback between them. Frame verification through LLM: max. 3 steps per scene (found → LLM checked → if not, photo with motion as a sure bet). Uniqueness: randomization + database of used. Only legal sources. There is a README, runs from scratch.Delivery of results All source code — in open view (all files), without obfuscation + compiled working version. I can run it myself from the sources according to the instructions (README: installation, keys, launch). The code must be clean, commented, and understandable, so **any other programmer can continue working** on it if needed (not tied to the author). **All everyday work — through the interface (buttons, fields, sliders, dropdown lists), WITHOUT the need to touch the code.** All settings (topic, voice, options, overlay, folders, length, formats) are changed in the program window, not by editing files. The code in hand — only as my property and insurance, not as a way to control the program. All rights to the code after payment — mine.Disk space management (important) The program should not fill up the disk. Implement: After assembling the video, all intermediate files (downloaded clips, temporary pieces, audio cuts) are automatically deleted — only the ready MP4 remains on the disk. Cache limit (parameter in settings, e.g., 5 GB): when exceeded, old downloaded files are automatically deleted (starting with the oldest). I set the folder for ready videos and for temporary files in the settings. Show how much space is occupied, and a button "clear cache" manually.Please specify in your response Examples of similar works (ffmpeg/moviepy, working with stock/archive APIs, ElevenLabs/LLM). Proposal for GUI. Does the solution use a database (which and why) — or are local files sufficient. Timeline and cost.

We are developing a real-time integration with the external service Trainer. We send structured snapshots of the state, receive recommendations, and display them in a pop-up bubble. The task is stable and complete data transmission for the correct operation of the Trainer. We are looking for a developer for the chain: data processing → HTTP communication → overlay. We need people with the following skills Python is good, Java basics, API HTTP/JSON-APIs The project is 90% ready but there are some inconsistencies

A local Python script needs to be developed to automatically fill a Google Sheet with data from the company's internal service. Main logic: 1. Connect to the Google Sheet. 2. Find rows where the ID is filled but two target values are missing. 3. Form a link based on the template: https://internal-service.example/item/{ID} 4. Retrieve the two values (via API, if it exists, otherwise via Playwright). 5. Write the values back to the Google Sheet. 6. Mark the row as processed. 7. Continue processing the next rows. Requirements: • Python • Google Sheets API • Priority to use the official API • If no API — Playwright • No OCR, screen recognition, or mouse coordinates • Confidential data must not be logged • Configuration via .env • Test mode (without writing to the sheet) • Do not process already filled rows • Batch write changes to Google Sheets • Proper error handling and retries It is necessary to provide: - source code; - requirements.txt; - example .env.example; - installation instructions; - running instructions; - brief architecture description. Before starting implementation, please: 1. Suggest an architecture. 2. List the necessary accesses. 3. Ask clarifying questions. 4. Indicate the cost, deadlines, and estimated number of hours.

As part of enhancing the cybersecurity level of our infrastructure, we need to abandon the practice of storing "eternal" and static API keys, passwords, and integration tokens in the configuration files (.env, appsettings.json, config.yaml) of our microservices. Business Goal: Create a single secure storage point for confidential data (secrets) with a mechanism for their automatic updating (rotation) in external systems on a schedule. Our other services will request current tokens "on the fly" via API, which will minimize damage in case of compromise of any system component.Security Model and Encryption (Crypto Core) No secret should be stored in plaintext in the database. Upon application startup, a Master Key is passed to the environment variables. If the key is missing or has an invalid length, the service should fail at the initialization stage with a clear error in the logs. Each secret is encrypted before being written to the database using this Master Key. Upon request, it is decrypted in memory and returned in the response body.Audit Logging (Audit Trail) Any action with secrets (creation, reading by the service, successful or unsuccessful rotation) must be recorded in a separate log file audit.log (or a separate table in the database). Strict Taboo: It is strictly prohibited to record the actual values of secrets in the audit log (neither in plaintext nor in encrypted form).

Need a specialist for writing parsers who can bypass CLOUDFRAME. Parsing of products occurs from sites with authorization. There are 10+ donors of varying complexity, with different levels of protection. Parsing of products occurs from sites with authorization. Parses data into a ready-made Mysql database + photographs on the server. It is necessary to write a parser according to the tasks described in the technical assignment and adapt the data to the existing database for full functionality on the site. Technical assignment and example donor upon request. Desktop parsers and C# are not considered.