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    "info": {
        "author": "Rehan Durrani",
        "author_email": "rdurrani@berkeley.edu",
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        "classifiers": [
            "Development Status :: 1 - Planning",
            "License :: OSI Approved :: Apache Software License",
            "Programming Language :: Python :: 3 :: Only"
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        "description": "# ModelZoo\nPython package to query [ModelZoo.Live](https://modelzoo.live)\n\n## API\nFor more specific API details, please check out the docs within the files.\n\n## Examples\n### Initialization\n```python\n# Connects to a ModelZoo instance running at http://modelzoo.url/\nconn = ModelZooConnection(address=\"http://modelzoo.url/\")\n# Authenticate to service. Neccessary to see your token.\nconn.authenticate(\"myemail@me.com\", \"mypassword\")\n```\n### Get Models\n```python\n# Assuming conn initialized previously\nconn.list_all_models()\n```\n### Get Token\n```python\n# Assuming conn initialized previously, and authenticated to server\nconn.get_token()\n```\n### Create User\n```python\n# Assuming conn initialized previously\nconn.create_user(\"mynewemail@me.com\", \"mynewpassword\")\n```\n### Text Inference\n```python\n# Assuming conn initialized previously\nmodel_name = \"MyModelName\"\ninput = [\"Input1\", \"Input2\"]\n\np = conn.text_inference(model_name, input)\n# type(p) = modelzoo.protos.Payload\n```\nIf your model's output is text, you could expect an output formatted like so:\n```python\np = Payload({type: PayloadType.TEXT, text = t})\nt = Text({metadata: {...}, texts: [\"output1\", ...], model_name: \"name\", access_token: \"token\"})\n```\n### Image Inference\n```python\n# Assuming img is your image input.\n# img can be oneof(filename, PIL.Image, image data uri)\nmodel_name = \"MyModelName\"\n\np = conn.image_inference(model_name, img)\n# type(p) = modelzoo.protos.Payload\n```\nIf your model's output is an image, you could expect an output formatted like so:\n```python\np = Payload({type: PayloadType.IMAGE, image = t})\nt = Image({metadata: {...}, image_data_url: \"img_output_uri\", model_name: \"name\", \n           access_token: \"token\"})\n```\n#### It is important to note that your model is not constrained to return an output of the same type as its input. \nIt is perfectly valid, for example, for a model to take a text input, and return an image output, or vice versa.\n### Extracting Output for Inference\n```python\n...\n# p is the Payload object returned by\n# oneof(text_inference, image_inference)\nif p.type == PayloadType.TEXT:\n    out = sugar.text_input(p.text)\nelif p.type == PayloadType.IMAGE:\n    out = sugar.image_input(p.image)\n# out = oneof(List[string], PIL.Image)\n```\n### Post Processing for Inference\n```python\n...\n# p is the Payload object returned by\n# oneof(text_inference, image_inference)\ndef callback(input):\n    # Input is oneof(pd.DataFrame, List[string], PIL.Image)\n    return output\n\no = conn.process_inference_response(p, callback)\n\n# o == callback(extract(payload))\n```\n\n",
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        "name": "modelzoo",
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