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Add ability to create Extractors 100% in the GUI #708
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,366 @@ | ||
| import React, { useEffect, useState } from "react"; | ||
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|
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| import { | ||
| Autocomplete, | ||
| Box, | ||
| Button, | ||
| ButtonGroup, | ||
| Divider, | ||
| FormControl, | ||
| FormControlLabel, | ||
| FormHelperText, | ||
| FormLabel, | ||
| Grid, | ||
| IconButton, | ||
| InputBase, | ||
| List, | ||
| Radio, | ||
| RadioGroup, | ||
| TextField, | ||
| } from "@mui/material"; | ||
| import { useDispatch, useSelector } from "react-redux"; | ||
| // import { RootState } from "../../types/data"; | ||
|
|
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| // import { CreateListenerModal } from "./CreateListenerModal"; | ||
| // import { CreateMetadata } from "../metadata/CreateMetadata"; | ||
| // import { | ||
| // fetchMetadataDefinitions, | ||
| // postDatasetMetadata, | ||
| // } from "../../actions/metadata"; | ||
| // import { MetadataIn } from "../../openapi/v2"; | ||
| // import { datasetCreated, resetDatsetCreated } from "../../actions/dataset"; | ||
| // import { useNavigate } from "react-router-dom"; | ||
| import Layout from "../Layout"; | ||
| import { ErrorModal } from "../errors/ErrorModal"; | ||
|
|
||
| export const CreateListener = (): JSX.Element => { | ||
| // const dispatch = useDispatch(); | ||
| // @ts-ignore | ||
| // const getMetadatDefinitions = ( | ||
| // name: string | null, | ||
| // skip: number, | ||
| // limit: number | ||
| // ) => dispatch(fetchMetadataDefinitions(name, skip, limit)); | ||
| // const createDatasetMetadata = ( | ||
| // datasetId: string | undefined, | ||
| // metadata: MetadataIn | ||
| // ) => dispatch(postDatasetMetadata(datasetId, metadata)); | ||
| // const = (formData: FormData) => | ||
| // dispatch(datasetCreated(formData)); | ||
| // const newDataset = useSelector( | ||
| // (state: RootState) => state.dataset.newDataset | ||
| // ); | ||
|
|
||
| // useEffect(() => { | ||
| // getMetadatDefinitions(null, 0, 100); | ||
| // }, []); | ||
|
|
||
| // const metadataDefinitionList = useSelector( | ||
| // (state: RootState) => state.metadata.metadataDefinitionList | ||
| // ); | ||
| const [errorOpen, setErrorOpen] = useState(false); | ||
|
|
||
| // const [datasetRequestForm, setdatasetRequestForm] = useState({}); | ||
| // const [metadataRequestForms, setMetadataRequestForms] = useState({}); | ||
| // const [allowSubmit, setAllowSubmit] = React.useState<boolean>(false); | ||
|
|
||
| // const history = useNavigate(); | ||
|
|
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| // const checkIfFieldsAreRequired = () => { | ||
| // let required = false; | ||
|
|
||
| // metadataDefinitionList.forEach((val, idx) => { | ||
| // if (val.fields[0].required) { | ||
| // required = true; | ||
| // } | ||
| // }); | ||
|
|
||
| // return required; | ||
| // }; | ||
|
|
||
| // step 1 | ||
| // const onDatasetSave = (formData: any) => { | ||
| // setdatasetRequestForm(formData); | ||
|
|
||
| // // If no metadata fields are marked as required, allow user to skip directly to submit | ||
| // if (checkIfFieldsAreRequired()) { | ||
| // setAllowSubmit(false); | ||
| // } else { | ||
| // setAllowSubmit(true); | ||
| // } | ||
|
|
||
| // handleNext(); | ||
| // }; | ||
| // step 2 | ||
| // const setMetadata = (metadata: any) => { | ||
| // // TODO wrap this in to a function | ||
| // setMetadataRequestForms((prevState) => { | ||
| // // merge the contents field; e.g. lat lon | ||
| // if (metadata.definition in prevState) { | ||
| // const prevContent = prevState[metadata.definition].content; | ||
| // metadata.content = { ...prevContent, ...metadata.content }; | ||
| // } | ||
| // return { ...prevState, [metadata.definition]: metadata }; | ||
| // }); | ||
|
|
||
| // metadataDefinitionList.map((val, idx) => { | ||
| // if (val.fields[0].required) { | ||
| // // Condition checks whether the current updated field is a required one | ||
| // if ( | ||
| // val.name == metadata.definition || | ||
| // val.name in metadataRequestForms | ||
| // ) { | ||
| // setAllowSubmit(true); | ||
| // return true; | ||
| // } else { | ||
| // setAllowSubmit(false); | ||
| // return false; | ||
| // } | ||
| // } | ||
| // }); | ||
| // }; | ||
|
|
||
| // step | ||
| // const [activeStep, setActiveStep] = useState(0); | ||
| // const handleNext = () => { | ||
| // setActiveStep((prevActiveStep) => prevActiveStep + 1); | ||
| // }; | ||
| // const handleBack = () => { | ||
| // setActiveStep((prevActiveStep) => prevActiveStep - 1); | ||
| // }; | ||
|
|
||
| // // finish button post dataset; dataset ID triggers metadata posting | ||
| // const handleFinish = () => { | ||
| // // create dataset | ||
| // createDataset(datasetRequestForm); | ||
| // }; | ||
|
|
||
| // useEffect(() => { | ||
| // if (newDataset.id) { | ||
| // // post new metadata | ||
| // Object.keys(metadataRequestForms).map((key) => { | ||
| // createDatasetMetadata(newDataset.id, metadataRequestForms[key]); | ||
| // }); | ||
|
|
||
| // //reset dataset so next creation can be done | ||
| // dispatch(resetDatsetCreated()); | ||
| // setMetadataRequestForms({}); | ||
| // setdatasetRequestForm({}); | ||
|
|
||
| // // zoom into that newly created dataset | ||
| // history(`/datasets/${newDataset.id}`); | ||
| // } | ||
| // }, [newDataset]); | ||
|
|
||
| useEffect(() => { | ||
| fetch('https://huggingface.co/api/models') | ||
| .then(response => response.json()) | ||
| .then(data => { | ||
| // Sort the models by downloads before mapping to modelNames | ||
| data.sort((a: any, b: any) => b.downloads - a.downloads); | ||
| const modelNames = data.map((model: any) => model.id); | ||
| setHuggingFaceModelNames(modelNames); | ||
| }) | ||
| .catch(error => console.error('Error:', error)); | ||
| }, []); | ||
|
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||
|
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| const [huggingFaceModelNames, setHuggingFaceModelNames] = useState<string[]>(["meta/llama2-70B-chat", "google/Flan-t5-large"]); | ||
| const [selectedHuggingFaceModelName, setSelectedHuggingFaceModelName] = useState<string>(""); | ||
|
|
||
| const handleHuggingFaceModelSubmit = () => { | ||
| const selectedModelName = document.getElementById('huggingface-model-name')?.value; // type: ignore | ||
| if (selectedModelName) { | ||
| alert(`Selected HuggingFace Model: ${selectedModelName}`); | ||
| } else { | ||
| alert('No model selected'); | ||
| } | ||
| }; | ||
|
|
||
| return ( | ||
| <Layout> | ||
| <Box className="outer-container"> | ||
| {/*Error Message dialogue*/} | ||
| <ErrorModal errorOpen={errorOpen} setErrorOpen={setErrorOpen} /> | ||
| <Box className="inner-container"> | ||
|
|
||
| <IntroMessage /> | ||
|
|
||
| <h1> | ||
| Run any HuggingFace model on your data, no code | ||
| </h1> | ||
|
|
||
| <Box sx={{ margin: "2em auto", padding: "0.5em" }}> | ||
| {/*HuggingFace inference*/} | ||
| <FormControl> | ||
| <FormLabel sx={{ paddingBottom: "1em" }}> | ||
| Run HuggingFace inference over your files | ||
| <FormHelperText sx={{ fontSize: "0.8em" }}> | ||
| You can select any model on the HuggingFace Hub. Then you can run that model over your files, with no code, no infrastructure and all without ever leaving this GUI. | ||
| The below models are sorted by number of downloads on <a href="https://huggingface.co/models" target="_blank" rel="noopener noreferrer">HuggingFace Hub</a>. | ||
| </FormHelperText> | ||
| </FormLabel> | ||
| {/* HuggingFace model name input */} | ||
| <Autocomplete | ||
| id="huggingface-model-name" | ||
| options={huggingFaceModelNames} | ||
| freeSolo | ||
| renderInput={(params) => ( | ||
| <TextField {...params} label="HuggingFace Model Name" variant="outlined" /> | ||
| )} | ||
| onInputChange={(event, newInputValue) => { | ||
| setSelectedHuggingFaceModelName(newInputValue); | ||
| }} | ||
| /> | ||
| <Button | ||
| variant="contained" | ||
| color="primary" | ||
| onClick={handleHuggingFaceModelSubmit} | ||
| disabled={!selectedHuggingFaceModelName} | ||
| > | ||
| Submit | ||
| </Button> | ||
| <Box sx={{ margin: "2em auto", padding: "0.5em" }}> | ||
| {/*access*/} | ||
| <FormControl> | ||
| <FormLabel id="radio-buttons-group-label-access"> | ||
| Security Level | ||
| </FormLabel> | ||
| <FormHelperText sx={{ fontSize: "0.8em" }}> | ||
| Choose your endpoint's level of privacy. | ||
| </FormHelperText> | ||
| <RadioGroup | ||
| aria-labelledby="radio-buttons-group-label-access" | ||
| defaultValue="protected" | ||
| name="radio-buttons-group-access" | ||
| > | ||
| <FormControlLabel value="protected" control={<Radio />} label="Protected" /> | ||
| <FormHelperText sx={{ fontSize: "0.8em" }}> | ||
| A Protected Endpoint is available from the Internet, secured with TLS/SSL and requires a valid Clowder API Token for authentication. | ||
| </FormHelperText> | ||
| <FormControlLabel value="public" control={<Radio />} label="Public" /> | ||
| <FormHelperText sx={{ fontSize: "0.8em" }}> | ||
| A Public Endpoint is available from the internet, secured with TLS/SSL and requires NO authentication. | ||
| </FormHelperText> | ||
| </RadioGroup> | ||
| </FormControl> | ||
| </Box> | ||
|
|
||
| </FormControl> | ||
| </Box> | ||
| {/* </Grid > */} | ||
|
|
||
| {/* TODO: possibly implement the Stepper box for iterative form filling... */} | ||
|
|
||
|
|
||
| {/* <Box> | ||
| <Stepper activeStep={activeStep} orientation="vertical"> | ||
|
|
||
| <Step key="create-dataset"> | ||
| <StepLabel>Basic Information</StepLabel> | ||
| <StepContent> | ||
| <Typography> | ||
| A dataset is a container for files, folders and metadata. | ||
| </Typography> | ||
| <Box> | ||
| <CreateListenerModal onSave={onDatasetSave} /> | ||
| </Box> | ||
| </StepContent> | ||
| </Step> | ||
|
|
||
|
|
||
| <Step key="fill-in-metadata"> | ||
| <StepLabel>Required Metadata</StepLabel> | ||
| <StepContent> | ||
| {metadataDefinitionList.length > 0 ? ( | ||
| <Typography> | ||
| This metadata is required when creating a new dataset. | ||
| </Typography> | ||
| ) : ( | ||
| <Typography>No metadata required.</Typography> | ||
| )} | ||
| <Box> | ||
| <CreateMetadata setMetadata={setMetadata} /> | ||
| </Box> | ||
|
|
||
| <Box sx={{ mb: 2 }}> | ||
| <> | ||
| <Button | ||
| variant="contained" | ||
| onClick={handleFinish} | ||
| disabled={!allowSubmit} | ||
| sx={{ mt: 1, mr: 1 }} | ||
| > | ||
| Finish | ||
| </Button> | ||
| <Button onClick={handleBack} sx={{ mt: 1, mr: 1 }}> | ||
| Back | ||
| </Button> | ||
| </> | ||
| </Box> | ||
| </StepContent> | ||
| </Step> | ||
| </Stepper> | ||
| </Box> */} | ||
| </Box> | ||
| </Box> | ||
| </Layout > | ||
| ); | ||
| }; | ||
|
|
||
| const IntroMessage = () => { | ||
| return ( | ||
| <> | ||
| <h1> | ||
| Create a new Extractor | ||
| </h1> | ||
| <h2> | ||
| Why Extractors? | ||
| </h2> | ||
| <p> | ||
| At its heart, <strong>extractors run a Python function over every file in a dataset</strong>. They can run at the click of a button in Clowder web UI or like an event listener every time a new file is uploaded. | ||
| </p> | ||
| <p> | ||
| Extractors are performant, parallel-by-default, web-native <a href="https://github.com/clowder-framework/pyclowder">Clowder Extractors</a> using <a href="https://research.ibm.com/blog/codeflare-ml-experiments">CodeFlare</a> & <a href="https://www.ray.io/">Ray.io</a>. | ||
| Check out our <a href="https://github.com/clowder-framework/CodeFlare-Extractors/blob/main/utils/media/Getting_Started_with_Ray_Workflows.pdf">📜 blog post on the incredible speed and developer experience</a> of building on Ray. | ||
| </p> | ||
| <h3> | ||
| 🧠 ML Inference | ||
| </h3> | ||
| <p> | ||
| Need to process a lot of files? <strong>This is great for ML inference and data pre-processing</strong>. These examples work out of the box or you can swap in your own model! | ||
| </p> | ||
| <p> | ||
| TODO: These may examples need updating because they're traditional extractors, not this 100% GUI extractor version. | ||
| <img src="https://pytorch.org/assets/images/pytorch-logo.png" width="40" align="left" /> | ||
| <a href="https://github.com/clowder-framework/CodeFlare-Extractors/tree/main/parallel-batch-ml-inference-pytorch">PyTorch example</a> | ||
| <br /> | ||
| <br /> | ||
| <img src="https://upload.wikimedia.org/wikipedia/commons/2/2d/Tensorflow_logo.svg" width="40" align="left" /> | ||
| <a href="https://github.com/clowder-framework/CodeFlare-Extractors/tree/main/parallel_batch_ml_inference">TensorFlow Keras example</a> | ||
| <br /> | ||
| <br /> | ||
| <img src="https://em-content.zobj.net/thumbs/120/apple/325/hugging-face_1f917.png" width="40" align="left" /> | ||
| <a href="https://github.com/clowder-framework/CodeFlare-Extractors/tree/main/parallel-batch-ml-inference-huggingface">Huggingface Transformers example</a> | ||
| <br /> | ||
| <br /> | ||
| </p> | ||
| <h3> | ||
| 🔁 Event-driven | ||
| </h3> | ||
| <p> | ||
| Have daily data dumps? <strong>Extractors are perfect for event-driven actions</strong>. They will run code every time a file is uploaded. Uploads themselves can be automated via <a href="https://github.com/clowder-framework/pyclowder">PyClowder</a> for a totally hands-free data pipeline. | ||
| </p> | ||
| {/* <h3> | ||
| Clowder's rich scientific data ecosystem | ||
| </h3> | ||
| <p> | ||
| Benefit from the rich featureset & full extensibility of Clowder: | ||
| </p> | ||
| <ul> | ||
| <li>Instead of files on your laptop, use Clowder to add collaborators & share datasets via the browser.</li> | ||
| <li>Benefiting scientists, we work with (~)every filetype and have rich extensibility for any job you need to run.</li> | ||
| </ul> */} | ||
| </> | ||
| ) | ||
| } |
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dw, I'll clean up these comments. Just keeping them for now as I do the initial implementation.