From ee38de41053619c7ee177c45c2d070254de20693 Mon Sep 17 00:00:00 2001 From: Hironsan Date: Tue, 13 Sep 2022 15:40:07 +0900 Subject: [PATCH] Describe how to use custom rest api --- docs/advanced/auto_labelling_config.md | 86 +++++++++++++++++- .../custom_rest_request_body.png | Bin 0 -> 17672 bytes .../custom_rest_request_parameters.png | Bin 0 -> 19958 bytes .../custom_rest_request_template.png | Bin 0 -> 38946 bytes .../custom_rest_request_test_parameters.png | Bin 0 -> 31958 bytes 5 files changed, 81 insertions(+), 5 deletions(-) create mode 100644 docs/images/auto-labeling/custom_rest_request_body.png create mode 100644 docs/images/auto-labeling/custom_rest_request_parameters.png create mode 100644 docs/images/auto-labeling/custom_rest_request_template.png create mode 100644 docs/images/auto-labeling/custom_rest_request_test_parameters.png diff --git a/docs/advanced/auto_labelling_config.md b/docs/advanced/auto_labelling_config.md index ffe8eeca..6fff33cc 100644 --- a/docs/advanced/auto_labelling_config.md +++ b/docs/advanced/auto_labelling_config.md @@ -12,13 +12,15 @@ The tutorial is divided into several sections: In this tutorial, we will show you how to set up auto-labeling using Amazon Comprehend Sentiment Analysis as an example. Therefore, we assume that you have a text classification project in doccano, an AWS account and be able to generate access keys. -## Select a Template +## Use pre-defined service + +### Select a Template First, move to the "settings" page and open "Auto Labeling" tab. The new tab should display a "Create" button and an empty table. Click the button and select "Amazon Comprehend Sentiment Analysis" from the dropdown menu: ![](../images/auto-labeling/select_template.png) -## Set Request Parameters +### Set Request Parameters Next, you need to set parameters to send an API request. In the case of Amazon Comprehend Sentiment Analysis, the following parameters are required: @@ -35,7 +37,7 @@ Then, we will test them using the sample text to make sure whether the parameter ![](../images/auto-labeling/test_parameters.png) -## Specify Response Mapping +### Specify Response Mapping Now, you can successfully fetch the API response. Next, you need to convert it to doccano format(below) with the mapping template([Jinja2](https://jinja.palletsprojects.com/en/2.11.x/) format). @@ -64,7 +66,7 @@ After setting the template, we will test them using the sample response. This re ![](../images/auto-labeling/test_mapping_template.png) -## Specify Label Mapping +### Specify Label Mapping Once you specify the mapping template, you need to convert the label in the response into the one you defined at the label page. @@ -76,10 +78,84 @@ After adding the label mapping, we will test them using the sample response: ![](../images/auto-labeling/test_label_mapping.png) -## Enable the Feature +### Enable the Feature Finally, move to the "annotation" page and click "Auto Labeling" button. It should display a "Slide" button for switching enable/disable auto-labeling feature. Try to enable it: ![](../images/auto-labeling/enable.png) Each time you view a new document, it will be labeled automatically. + +## Use your own API + +First, select "Custom REST Request": + +![](../images/auto-labeling/custom_rest_request_template.png) + +Next, you need to build your own API. Any framework can be used. Here we will use [Flask](https://flask.palletsprojects.com/en/2.2.x/) to create a minimal application. This application always returns the same label(`{"label": "NEG"}`). We also call `get_json` method and output its return value to make sure we can receive the data. + +```bash +from flask import Flask, request + +app = Flask(__name__) + +@app.route("/", methods=["POST"]) +def predict(): + print(request.get_json()) + return {"label": "NEG"} +``` + +Save it as `hello.py` or something similar. Make sure to not call your application `flask.py` because this would conflict with Flask itself. + +To run the application, use the `flask` command. You need to tell the Flask where your application is with the `--app` option. + +```bash +$ flask --app hello run + * Serving Flask app 'hello' + * Debug mode: off + * Running on http://127.0.0.1:5000 +Press CTRL+C to quit +``` + +OK. Let's return to doccano. + +Next, you need to set parameters(`url` and `method`). Let's set the Flask application's URL and method: + +![](../images/auto-labeling/custom_rest_request_parameters.png) + +Next, select the add button next to Body. Enter `text` as key and `{{ text }}` as value. This value is a placeholder and will actually be replaced by your own text. + +![](../images/auto-labeling/custom_rest_request_body.png) + +Then, test the parameters with the sample text. If it is working correctly, it should return `{"label": "NEG"}`. + +![](../images/auto-labeling/custom_rest_request_test_parameters.png) + +You should also see the following in the console: + +```bash +127.0.0.1 - - [13/Sep/2022 15:19:57] "GET / HTTP/1.1" 405 - +{'text': 'This is a test sentence.'} +``` + +Next, convert the response from your API into a format that doccano can handle. We can access the response by the `input` variable. The mapping template looks like the following: + +```bash +[ + { + "label": "{{ input.label }}" + } +] +``` + +Test the mapping template. If it is working correctly, it should be as follows: + +```bash +[ + { + "label": "NEG" + } +] +``` + +The rest is the same as when using a predefined service. diff --git a/docs/images/auto-labeling/custom_rest_request_body.png b/docs/images/auto-labeling/custom_rest_request_body.png new file mode 100644 index 0000000000000000000000000000000000000000..f2942c3ba1666b80638cc5471a4dcc502baa698a GIT binary patch literal 17672 zcmeIZbySqy_dW~=BHbXMFaiRCfRYkJNH@|Q(v37j2_lHJl+xYZHH3&D-7%yf-QC0Q z=JWkLGSBb*|6T8T*Vna>@yvbB+4nyC?0sF=IR+~!NaA2pU?L$Q;Ydq~DI+1>BS%6) zrg?B5Xo+p3u0uk?oUjxXRgxAJ1t~e&n_1eJA|Xiy$Ht?ps!Wjv4nTRJgD6rANAys% zNJ0n_+B%5o2?kxvLn8lAkI5gkeg6D_=^o#MjDuAID(N%NuC($BA|G zll=OvcFuEiySg7d4|7ZPJ`vnZsfJ@Ajis^>ntqo-GQp_qh!SZ&ItvivgJXbDut7-1 zNaehQ1ROLp*lFD0o3Xihp>N`NT`^JEDHDDzYV`w=S||| z#C!VCl>dnaNQ{@vlnpnvPrROFc}h!5mj28wrfawY>H3>^<$nsUjIptk>|fYVI~LfM_+afOL<%i7)9>(Hv1jSRnqxxXtaJQ?OTyV z@9{Jr7#$#DH+Pg-T&{lS0Y~O|`SQU1O--p6+Z(e0fs}Z1x>H%lj6c?uLlI79wgFez 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