Projetsleadbot-command-center
Outils numeriques

Leadbot Command Center

Un centre de commande FastAPI et Playwright pour l'extraction de leads Google Maps, les journaux en direct et les exports Excel/CSV telechargeables.

PythonFastAPIPlaywrightWebSocketsDockerPandasOpenPyXLHugging Face Spaces
GitHubLive DemoLive Demo: AvailableHosting: Hugging Face Spaces
LC

Outils numeriques

GitHub

Repository available

Source code and project documentation

Live demo

Available

Public deployment linked from project data

Live Demo

Available

Hosting

Hugging Face Spaces

Probleme

Ce qu'il fallait resoudre

Lead research is repetitive when business listings, locations, and contact details are collected manually. A small operator needs a focused tool that can start a search, show progress, and return usable spreadsheet exports.

Solution

Comment le projet y repond

I built the project around a FastAPI web service, a lightweight HTML interface, Playwright automation, WebSocket progress updates, and file export endpoints. The app is packaged with a Dockerfile so it can run on container-friendly hosting.

Fonctionnalites clefs

Ce que le projet comprend

FastAPI web app with health endpoint
Playwright-powered Google Maps scraping workflow
Live progress updates through WebSockets
Excel and CSV result exports
Docker-based deployment
Public Hugging Face Space demo

Architecture / decisions techniques

Choix d'implementation a examiner

Used FastAPI for a small backend surface with explicit health, scrape, websocket, and download endpoints.
Packaged the app with a Playwright Python Docker image so browser automation dependencies are available in the runtime.
Stored generated result files in a runtime results directory and exposed download endpoints for reviewer-friendly exports.

Validation / qualite

Comment j'ai rendu le travail evaluable

Local Docker image build completed successfully
Local /health endpoint returned HTTP 200
Hugging Face Space reports the Docker runtime as running on CPU Basic hardware

Ce que j'ai appris

Enseignement technique

I learned how to package a browser-automation workflow as a deployable web service, including health checks, runtime port binding, and a lightweight command-center UI.

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Plus d'etudes de cas

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