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author | terminaldweller <devi@terminaldweller.com> | 2023-04-27 10:58:44 +0000 |
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committer | terminaldweller <devi@terminaldweller.com> | 2023-04-27 10:58:44 +0000 |
commit | 3444cf785d0e73ab28649abb8fc8a45256973ade (patch) | |
tree | 0bfaed8f41fbc4d24e2d2860ad44372504224a07 | |
parent | updated the readme (diff) | |
download | magni-3444cf785d0e73ab28649abb8fc8a45256973ade.tar.gz magni-3444cf785d0e73ab28649abb8fc8a45256973ade.zip |
magni can now get a user agent from an env var, fixed the readme for http_proxy
Diffstat (limited to '')
-rw-r--r-- | README.md | 19 | ||||
-rwxr-xr-x | magni.py | 16 |
2 files changed, 19 insertions, 16 deletions
@@ -31,17 +31,24 @@ HTTPS_PROXY=socks5h://127.0.0.1:9094 poetry run ./magni.py --url https://chapman ## Env Vars magni recognizes three environment variables:</br> -### HTTPS_PROXY -You must specify a socks5 proxy here since magni uses `pysocks` to make the connections.</br> -If the env var is not defined magni will not use any proxy.</br> +### HTTP_PROXY/HTTPS_PROXY +You can also specify a socks5 proxy here since magni uses `pysocks` to make the connections.</br> +If the env vars are not defined or are empty magni will not use any proxy.</br> ### MAGNI_MODEL_PATH -Path to the directory where magni will store the model.</br> -If the env var is not defined, magni will use `./models` as a default value.</br> +Path to the directory where magni will store the models.</br> +If the env var is not defined or is empty, magni will use `./models` as a default value.</br> ### MAGNI_IMAGE_PATH Path to the directory where magni will store the upscaled images.</br> -If the env var is not defined or empty magni will use `./images` as a default value.</br> +If the env var is not defined or is empty magni will use `./images` as a default value.</br> + +### MAGNI_USER_AGENT +The user agent magni will use the download the images.</br> +If the env var is not defined or is empty, magni will use a default user agent you can see below:</br> +``` +Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/108.0.0.0 Safari/537.36 +``` ## TODO * currently the models we are using are not as effective. I should either fine ones that are specifically trained on greyscale images or just train some myself.</br> @@ -120,17 +120,13 @@ def fsrcnn_superscaler(img): # flake8: noqa: E501 def get_user_agent() -> str: """Returns a random user agent.""" - # user_agents = [ - # "Mozilla/5.0 (Windows NT 10.0; rv:91.0) Gecko/20100101 Firefox/91.0", - # "Mozilla/5.0 (Windows NT 10.0; rv:78.0) Gecko/20100101 Firefox/78.0", - # "Mozilla/5.0 (X11; Linux x86_64; rv:95.0) Gecko/20100101 Firefox/95.0", - # "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/108.0.0.0 Safari/537.36", - # ] - user_agents = [ - "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/108.0.0.0 Safari/537.36", - ] + user_agent: str = "" + if "MAGNI_USER_AGENT" in os.environ and os.environ["MAGNI_USER_AGENT"]: + user_agent = os.environ["MAGNI_USER_AGENT"] + else: + user_agent = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/108.0.0.0 Safari/537.36" - return user_agents[secrets.randbelow(len(user_agents))] + return user_agent def get_proxies() -> typing.Dict: |