mirror of
https://github.com/User0332/rewards-farmer.git
synced 2026-09-16 01:31:36 +00:00
Ethan Stoner
bfe30dd9a0
Both taken from TheNetsky/Microsoft-Rewards-Script, which packages a container and handles several accounts. Approach only: that project is GPL-3.0 and this one MIT, so no code crosses over. **Accounts.** Rewards is per Microsoft account and the browser profile holds the sign-in, so an account here is a profile directory. REWARDS_ACCOUNTS takes a comma separated list and gives each its own directory under the configured one. They run in sequence, and a profile that will not start is reported and skipped rather than ending the run. Left unset, a run uses the single profile exactly as before. Names are validated rather than trusted: they become directory names, so "../escape" is refused instead of quietly writing outside data-dir. **Docker.** The image carries only what main.py actually reaches, selenium and numpy. pygetwindow, keyboard, matplotlib and pygame are used solely by the recording and visualisation scripts, and two of those are Windows-only, so none of them belong in a container. msedgedriver is pinned at build time to the Edge the image installed rather than to latest, which drifts from it between releases. QUERY_SOURCE defaults to trends in the image, so a container needs no Ollama account and no model download at all. That default turned out to require a fix. queries.py imported llm_utils at module scope, which imports ollama, so a trends-only install still needed the ollama package: exactly what running in a minimal image is good at exposing. The import is now made inside the llm branch, and the wordlist fallback reads nouns.txt directly rather than borrowing llm_utils.get_random_noun. REWARDS_HEADLESS drives the headless flags. The window size is set explicitly because the pointer code works in viewport coordinates and the default headless window is small enough to put cards out of reach, which is the MoveTargetOutOfBoundsException from #19. Verified on the host that move_to_element and human_like_click both work headless before relying on it. Verified in the built image: Edge 151.0.4129.107 with a driver of exactly the same build, the trends feed reachable from inside, Edge driven to bing.com and rewards.bing.com at 1920x1080, and REWARDS_ACCOUNTS producing separate profile directories with traversal refused.
112 lines
5.9 KiB
Markdown
112 lines
5.9 KiB
Markdown
# User0332/rewards-farmer
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Automation for MS Rewards based on [https://youtu.be/4qdPcMNaioA](https://youtu.be/4qdPcMNaioA).
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# Running Instructions
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IMPORTANT: Use at your own risk. Microsoft may take action against your account for using automated scripts to gain rewards points. The YouTube video contains more details about the techniques implemented to avoid detection of this script.
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Clone the repository.
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```sh
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git clone https://github.com/User0332/rewards-farmer
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```
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A sample `nouns.txt` file is included in the project root and can be modified by the user to contain seed words for the LLM to complete 20 searches. The wordlist should be separated by newline.
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```sh
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cd rewards-farmer
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# Edit the included nouns.txt file to add or replace words as needed
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```
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# Where search queries come from
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The bot needs short strings to type into Bing. Two backends produce them, set with `QUERY_SOURCE`:
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| `QUERY_SOURCE` | Needs | Notes |
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| --- | --- | --- |
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| `llm` (default) | Ollama account + model | Current behaviour, unchanged |
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| `trends` | nothing | Google Trends, Wikipedia and Bing autosuggest |
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```sh
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QUERY_SOURCE=trends python src/main.py # bash
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$env:QUERY_SOURCE="trends"; python src/main.py # PowerShell
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```
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`trends` needs no account, no API key and no model download, so the Ollama setup below is optional if you use it. If every feed is unreachable it falls back to `nouns.txt` rather than failing the run.
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You should also have an Ollama account created (for the LLM), the `ollama` tool installed, and you should have signed in to the Ollama CLI via the command line using `ollama signin`. This project will use a minimal amount of Ollama cloud usage using `gemma4:cloud`. If you wish to use a different model, please change the `model` parameter in the `get_ollama_response` function in `src/llm_utils.py`.
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You must also provide an image for the script to upload to complete the visual search task. Currently, this image is named `keypress_times.png` and is located in the root directory of the project (yes, I used a random image from my keyboard analysis to do this). You may provide an image of your own, just ensure that the absolute path of the image is placed in the `VISUAL_SEARCH_IMAGE_PATH` constant at the top of `rewards_tasks.py`.
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Activate the virtual environment & install dependencies (you may have to use `python -m poetry` instead of `poetry`).
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You must have Python 3.12+ and Poetry installed.
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If `iex (poetry env activate)` fails with *"Cannot bind argument to parameter 'Command' because it is null"*, `poetry install` did not create an environment. Run `python --version` first: an older Python leaves poetry with nothing to activate, and the message explaining that goes to stderr rather than into `iex`.
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Windows (PowerShell)
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```sh
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poetry install
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iex (poetry env activate)
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```
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*nix (Bash)
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```sh
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poetry install
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eval $(poetry env activate)
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```
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You must also have a [webdriver for Microsoft Edge](https://learn.microsoft.com/en-us/microsoft-edge/webdriver/?tabs=c-sharp) installed. If you already have the Edge Browser installed, you probably have this component as well.
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The profile directory in `src/constants.py` is set to `Default`. If this signs you in to a global profile that you do not want to use for automation, then you can create a new profile from within the webdriver instance manually and then change the `PROFILE_NAME` constant to `Profile 1` (or the equivalent number).
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Run main.py (`python src/main.py`, it must be run from the root directory so the relative paths work out), wait for the page to launch, and then CTRL-C to quit the application immediately. Sign in to the created profile with your Microsoft account on both Bing and `rewards.bing.com`.
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EU Users: you may have to accept a consent banner once on `rewards.bing.com` and on the Bing search page, `bing.com`. Once you consent, your choice will be saved for future runs using the same profile, so you will not need to interact with the banner during automated runs.
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Close all webdriver browser instances. Run `main.py` again; the automation should start working.
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# Running more than one account
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Rewards is per Microsoft account and the browser profile holds the sign-in, so an account here is a profile directory. `REWARDS_ACCOUNTS` takes a comma separated list, and each name gets its own directory under `data-dir`:
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```sh
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REWARDS_ACCOUNTS=personal,spare python src/main.py
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```
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Each is signed in once by hand, the same way as the single profile, using its own directory:
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```
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msedge --user-data-dir="<repo>\data-dir\personal" --profile-directory=Default https://rewards.bing.com
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```
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They run one after another, and a profile that fails to start is reported and skipped rather than ending the run. Leave `REWARDS_ACCOUNTS` unset and everything behaves exactly as before, using the single profile in `data-dir`.
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# Docker
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Runs the bot without installing Edge, a driver or Python on the host.
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```sh
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docker compose build
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docker compose run --rm rewards-farmer
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```
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The container defaults to `QUERY_SOURCE=trends`, so it needs no Ollama account and no model. Set `QUERY_SOURCE=llm` and `OLLAMA_HOST` to a reachable address to use a model instead.
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**Sign in first.** The profile in `data-dir` starts logged out and the container has no display to sign in with, so do it once on the host with a normal Edge window and let the volume carry it in:
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```
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msedge --user-data-dir="<repo>\data-dir" --profile-directory=Default https://rewards.bing.com
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```
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Close every window of that profile afterwards. Chromium allows one process per profile directory, so a window left open on the host stops the container from starting.
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Multiple accounts work the same way in the container:
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```sh
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REWARDS_ACCOUNTS=personal,spare docker compose run --rm rewards-farmer
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```
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`REWARDS_HEADLESS=1` is set in the image. It also works on the host if you want a run with no visible window; the pointer code needs an explicit window size in that mode, which `main.py` sets.
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Please open up a GitHub issue if you run into any difficulties. |