Writing Parsers in Python

Set up automatic daily updates of product availability on our website on prom.ua. We have a supplier who sends a price list of products in Excel format to our email every day. The items on our website and in the supplier's price list are the same. The values in the "stock" column are either out of stock, a number, or more than a box - these need to be updated on the site to either Ready for shipment or Out of stock. Items that are not in the supplier's price list should remain unchanged. Please propose a solution, timeline, and budget. Thank you in advance for your response, I look forward to collaborating with a specialist.

Hello! I am looking for a performer for ongoing collaboration who is knowledgeable about Opencart. A person who is available and has a positive attitude) Parsing, uploading products in two languages UA + ru, as well as forming the necessary markup immediately I want to complete the work in several stages. 1. Update stock for all suppliers and completely remove outdated products from the site and database. 2. Refinement of the product category, specifically parsing subcategories. 3. Parse new items in old categories. 4. Parse new suppliers into new categories.

A project needs to be implemented for collecting and structuring a large array of images from open web sources (initially 2000 images). The task includes: - automated image collection; - uploading files in the highest available quality; - classifying images by categories. Expected results: - a structured image database; - a clear cataloging system; - delivery of the results via Google Drive or another agreed method;

Good day! Two tasks need to be completed: 1. Develop a product parser from an external website (10–40 thousand items, marketplace) with structured data saved in MySQL for subsequent output in WordPress. 2. Install and configure n8n on VPS, as well as organize AI content processing: prompt setup, text rewriting, image processing, SEO optimization, and text checking for AI detection. You can estimate the cost of completing both the entire project and each task separately. .

Task: one dashboard with all business metrics — advertising, funnel, payments, manager performance, revenue planning. Data is pulled automatically via API. Scope: only the YCL direction (employment in Europe). Kommo has other directions — only YCL funnel deals will be included in the repository (filter by funnel/tag to be agreed upon).1. Data Sources (Integrations) Kommo CRM — leads, deals, funnel stages, responsible persons, sources, dates of transitions between stages (must keep history), reasons for refusals, custom deal fields (see point 2). Stripe — payments, amounts, statuses (success/failure/refund), linked to deals. Meta Ads — expenses, impressions, clicks, CPL, leads by campaigns (currently operational). Google Ads, Reddit Ads, LinkedIn Ads — planned; architecture — extensible connectors without core rework. SEO/organic— Google Search Console + GA4. Cross-link: traffic source → lead in Kommo → payment in Stripe (UTM, deal ID in Stripe metadata — propose the mechanism). 2. Mandatory Cuts (Deal Fields in Kommo) Each metric must be filtered/grouped by: Client Citizenship (Kenya, Nigeria, India, etc.). Residence Status: lives in their country / expat (already in Europe). These are two different segments with different cycles, conversion rates, and checks. Country of Placement / Service: Poland, Serbia, Slovakia, Germany (ZAV). Manager, team, traffic channel, period. If any fields are missing in Kommo — the executor indicates which fields need to be added, the client adds them.3. Funnel and Leading Indicators Data by funnel, for each stage — summary and leading metrics: Traffic → lead: leads, CPL by channels + day-to-day expense/click dynamics. Lead → qualification: conversion + first response speed, touches/calls to the manager per day, unanswered leads. Qualification → contract/invoice: conversion + sent offers, stalled deals (days in stage above norm). Invoice → payment: payments, average check + unpaid invoices, failed payments. Summary: revenue, ROMI by channels, run rate to monthly plan. 4. Deal Cycle Average and median lead → payment cycle (business benchmark ~4 weeks), cycle trend over time. Breakdown of cycle by stages (how many days a deal sits at each stage) — to see which stage is dragging. List of deals that have stalled at a stage longer than normal. Cycle breakdown by segments: citizenship, residence status, country of placement, manager. 5. Early Warning of Decline (Key Block) Since the cycle is ~4 weeks, today's leads = payments in a month. The system must: Compare leads/qualifications of the current week with the moving average (4 weeks) and issue an alert if there is a downward deviation: “leads -X%, with a 4-week cycle expect a payment decline in the week [date].” Build payment forecast for 4 weeks ahead from the current pipeline: deals at each stage × historical conversion of the stage × remaining cycle. Highlight in red weeks where the forecast is below plan — with time to react. 6. Additional Payments and Sales Planning In the Kommo deal card, the date and amount of the planned additional payment are stored. The system must: Collect a calendar of upcoming additional payments: total expected, by weeks/months. Highlight overdue additional payments (date passed, no payments in Stripe) — a separate list for follow-up. Calculate the monthly plan as: plan − already paid − scheduled additional payments = how many new sales are needed (in money and in deal units at average check). Weekly schedule: additional payments + forecast of new payments against the weekly plan. 7. Manager Performance Daily snapshot for each manager: touches/calls, conversations, sent offers, payments — for each day separately, with a chart over the period. Progress on personal plan compared to monthly pace (ahead / on pace / behind). Benchmarking with colleagues. 8. Visualization and Roles “Traffic lights” (green/yellow/red) for key metrics relative to norms/plans; progress scales; trend graphs; mobile adaptive. Roles: CEO — everything; COO — entire funnel and managers; team lead — their team; manager — their metrics and position relative to colleagues. 9. Reports and AI Automated reports on schedule (daily summary, weekly report) in the dashboard and/or messenger. Free-form queries (“how has CPL from Meta changed over 2 weeks?”) — LLM over the repository. Alerts in the red zone and according to the rules from points 5–6. 10. Technical Expectations and Staging Repository (PostgreSQL/BigQuery or equivalent) + ETL: Kommo webhooks + periodic synchronization (15–60 min). Frontend: custom or BI tool — propose with justification; requirements for roles, traffic lights, forecasts, and AI queries must be implementable. Stages: (1) audit and metrics map → (2) MVP: Kommo + Stripe + Meta, funnel, traffic lights, roles → (3) deal cycle, early warning, additional payments and plan → (4) SEO, AI reports, alerts → (5) new advertising channels. Payment is staged, with a demo for each stage. In the response, indicate: similar projects (end-to-end analytics), stack with justification, timeline and cost estimates by stages, monthly ownership cost (hosting, tokens, licenses).