Eden AI: simplify the use and deployment of AI technologies by providing a unique API that connects to the best possible AI engines
Home Page: https://www.edenai.co/
License: Apache License 2.0
Hi there, I am trying to upload docx file but getting not supported docx file type in resume parse api.
Could you please let me know if any provider that's support docx?
Thanks,
Why google always return empty string for pdf document?
In order to use multi-language identification by Amazon, I want to enable the IdentifyMultipleLanguages argument.
Therefore I do not set any language in data.
import requests
import json
headers = {"Authorization": "Bearer Your API KEY"}
url="[https://api.edenai.run/v2/audio/speech_to_text_async"](https://api.edenai.run/v2/audio/speech_to_text_async%22)
amazon_params = json.dumps({"amazon": {"IdentifyMultipleLanguages": True}})
data={"providers": "amazon", "provider_params": amazon_params}
files = {'file': open("multilingual_audio.mp3",'rb')}
response = requests.post(url, data=data, files=files, headers=headers)
data = response.json()
But it seems like not setting the language in data switches directly to language identification.
In this particular case with Amazon, it seems to be enabling IdentifyLanguage and not the wanted IdentifyMultipleLanguages .
When using a personal API Key (ONLY in this case) with DeepL for Translation, it appears to be that the wrong end point is used.
{
"deepl": {
"error": {
"message": "Deepl has returned an error: Wrong endpoint. Use https://api.deepl.com",
"type": "ProviderException"
},
"status": "fail",
"provider_status_code": 403,
"own_api_keys": true,
"cost": 0
}
}
Any quick fix, please?
Thanks in advance!
It's a test
I would like to make use of additional provider parameters, notably in this case additional_command from Cohere for summarization.
But it seems like it is not taking it into account.
And in general, is it possible to use parameters that you did not specify in edenapis' payloads?
Because it seems like all non-specified parameters are discarded.
I do not know if there is a way to allow the use of non-specified provider parameters.
If possible, would you have a solution? It would allow the users more flexibility.
Thanks!
I should be able to copy paste directly from the Quickstart box based on the selections I've made in the Live Testing form.
Love this product. Thanks guys.
Endpoint for ocr is not working.
It is throwing a validation error message about provider not being set, while within guzzle it is clearly being set and passed.
Your live preview cannot test when header is set to 'Content-Type' => 'multipart/form-data;'
https://docs.edenai.co/reference/ocr_ocr_create
$client = new \GuzzleHttp\Client();
$response = $client->post('https://api.edenai.run/v2/ocr/ocr', [
'multipart' => [
[
'name' => 'providers',
'contents' => 'api4ai'
]
],
'headers' => [
'Authorization' => 'Bearer ' . $this->token,
'Content-Type' => 'multipart/form-data;'
]
])
Response
Client error: `POST https://api.edenai.run/v2/ocr/ocr` resulted in a `400 Bad Request` response:
{"error":{"type":"Invalid request","message":{"providers":["Please enter the name of the provider(s)"]}}}
repro
pip install -r requirements.txtexpected
actual
error: subprocess-exited-with-error
× python setup.py egg_info did not run successfully.
│ exit code: 1
╰─> [37 lines of output]
/Users/beijbom/.virtualenvs/edenai9/lib/python3.9/site-packages/setuptools/config/setupcfg.py:293: _DeprecatedConfig: Deprecated config in `setup.cfg`
!!
********************************************************************************
The license_file parameter is deprecated, use license_files instead.
By 2023-Oct-30, you need to update your project and remove deprecated calls
or your builds will no longer be supported.
See https://setuptools.pypa.io/en/latest/userguide/declarative_config.html for details.
********************************************************************************
!!
parsed = self.parsers.get(option_name, lambda x: x)(value)
running egg_info
creating /private/var/folders/59/565v8hvn3_db0sqz6w0f8snh0000gn/T/pip-pip-egg-info-ll2022vk/psycopg2.egg-info
writing /private/var/folders/59/565v8hvn3_db0sqz6w0f8snh0000gn/T/pip-pip-egg-info-ll2022vk/psycopg2.egg-info/PKG-INFO
writing dependency_links to /private/var/folders/59/565v8hvn3_db0sqz6w0f8snh0000gn/T/pip-pip-egg-info-ll2022vk/psycopg2.egg-info/dependency_links.txt
writing top-level names to /private/var/folders/59/565v8hvn3_db0sqz6w0f8snh0000gn/T/pip-pip-egg-info-ll2022vk/psycopg2.egg-info/top_level.txt
writing manifest file '/private/var/folders/59/565v8hvn3_db0sqz6w0f8snh0000gn/T/pip-pip-egg-info-ll2022vk/psycopg2.egg-info/SOURCES.txt'
Hello,
I am using https://api.edenai.run/v2/image/face_recognition/recognize API for face recognize usage.
Currently region is selected as World but I want to change it to India. Region change option is disable for my account. Please help me here.
I'm trying to use generic OCR sending a 3 page pdf and EDEN is not working extracting text, it works just sending images, I need to send pdf and extract text.
Add Wordcab.com as a provider.
Thanks for this tool!
There seems to be a problem parsing the response for an image detection API call with the google provider.
Google's documentation for Cloud Vision API "Detect multiple objects" has a live example (scroll to the bottom, "Try this method").
For the reference image, https://cloud.google.com/vision/docs/images/bicycle_example.png, it produces the following output .
Bicycle wheel (x2)BicyclePicture frame{
"responses": [
{
"localizedObjectAnnotations": [
{
"mid": "/m/01bqk0",
"name": "Bicycle wheel",
"score": 0.94234306,
"boundingPoly": {
"normalizedVertices": [
{
"x": 0.31524897,
"y": 0.78658724
},
{
"x": 0.44186485,
"y": 0.78658724
},
{
"x": 0.44186485,
"y": 0.9692919
},
{
"x": 0.31524897,
"y": 0.9692919
}
]
}
},
{
"mid": "/m/01bqk0",
"name": "Bicycle wheel",
"score": 0.9337022,
"boundingPoly": {
"normalizedVertices": [
{
"x": 0.50342137,
"y": 0.7553652
},
{
"x": 0.6289583,
"y": 0.7553652
},
{
"x": 0.6289583,
"y": 0.9428141
},
{
"x": 0.50342137,
"y": 0.9428141
}
]
}
},
{
"mid": "/m/0199g",
"name": "Bicycle",
"score": 0.8973106,
"boundingPoly": {
"normalizedVertices": [
{
"x": 0.31594256,
"y": 0.66489404
},
{
"x": 0.63338375,
"y": 0.66489404
},
{
"x": 0.63338375,
"y": 0.9687162
},
{
"x": 0.31594256,
"y": 0.9687162
}
]
}
},
{
"mid": "/m/06z37_",
"name": "Picture frame",
"score": 0.7171168,
"boundingPoly": {
"normalizedVertices": [
{
"x": 0.7882889,
"y": 0.16610023
},
{
"x": 0.9662418,
"y": 0.16610023
},
{
"x": 0.9662418,
"y": 0.3178568
},
{
"x": 0.7882889,
"y": 0.3178568
}
]
}
}
]
}
]
}When I pass the same image to Eden (with providers=google), the output has duplicate items.
In the "google" JSON object, it appears the *_min, *_max fields are populated incrementally in four "copies" of the data. For each item, note
x_min == x_max && y_min == y_max).y_min == y_max).The "eden-ai" JSON object contains the redundant "copies" of each item.
{
"google": {
"status": "success",
"items": [
{
"label": "Bicycle wheel",
"confidence": 0.94234306,
"x_min": 0.31524897,
"x_max": 0.31524897,
"y_min": 0.78658724,
"y_max": 0.78658724
},
{
"label": "Bicycle wheel",
"confidence": 0.94234306,
"x_min": 0.31524897,
"x_max": 0.44186485,
"y_min": 0.78658724,
"y_max": 0.78658724
},
{
"label": "Bicycle wheel",
"confidence": 0.94234306,
"x_min": 0.31524897,
"x_max": 0.44186485,
"y_min": 0.78658724,
"y_max": 0.9692919
},
{
"label": "Bicycle wheel",
"confidence": 0.94234306,
"x_min": 0.31524897,
"x_max": 0.44186485,
"y_min": 0.78658724,
"y_max": 0.9692919
},
{
"label": "Bicycle wheel",
"confidence": 0.93370223,
"x_min": 0.50342137,
"x_max": 0.50342137,
"y_min": 0.7553652,
"y_max": 0.7553652
},
{
"label": "Bicycle wheel",
"confidence": 0.93370223,
"x_min": 0.50342137,
"x_max": 0.6289583,
"y_min": 0.7553652,
"y_max": 0.7553652
},
{
"label": "Bicycle wheel",
"confidence": 0.93370223,
"x_min": 0.50342137,
"x_max": 0.6289583,
"y_min": 0.7553652,
"y_max": 0.9428141
},
{
"label": "Bicycle wheel",
"confidence": 0.93370223,
"x_min": 0.50342137,
"x_max": 0.6289583,
"y_min": 0.7553652,
"y_max": 0.9428141
},
{
"label": "Bicycle",
"confidence": 0.89731073,
"x_min": 0.31594256,
"x_max": 0.31594256,
"y_min": 0.66489404,
"y_max": 0.66489404
},
{
"label": "Bicycle",
"confidence": 0.89731073,
"x_min": 0.31594256,
"x_max": 0.63338375,
"y_min": 0.66489404,
"y_max": 0.66489404
},
{
"label": "Bicycle",
"confidence": 0.89731073,
"x_min": 0.31594256,
"x_max": 0.63338375,
"y_min": 0.66489404,
"y_max": 0.9687162
},
{
"label": "Bicycle",
"confidence": 0.89731073,
"x_min": 0.31594256,
"x_max": 0.63338375,
"y_min": 0.66489404,
"y_max": 0.9687162
},
{
"label": "Picture frame",
"confidence": 0.7171168,
"x_min": 0.7882889,
"x_max": 0.7882889,
"y_min": 0.16610023,
"y_max": 0.16610023
},
{
"label": "Picture frame",
"confidence": 0.7171168,
"x_min": 0.7882889,
"x_max": 0.9662418,
"y_min": 0.16610023,
"y_max": 0.16610023
},
{
"label": "Picture frame",
"confidence": 0.7171168,
"x_min": 0.7882889,
"x_max": 0.9662418,
"y_min": 0.16610023,
"y_max": 0.3178568
},
{
"label": "Picture frame",
"confidence": 0.7171168,
"x_min": 0.7882889,
"x_max": 0.9662418,
"y_min": 0.16610023,
"y_max": 0.3178568
}
],
"cost": 0.00225
},
"eden-ai": {
"status": "success",
"items": [
{
"label": "Bicycle wheel",
"confidence": 0.94234306,
"x_min": 0.31524897,
"x_max": 0.31524897,
"y_min": 0.78658724,
"y_max": 0.78658724
},
{
"label": "Bicycle wheel",
"confidence": 0.94234306,
"x_min": 0.31524897,
"x_max": 0.44186485,
"y_min": 0.78658724,
"y_max": 0.78658724
},
{
"label": "Bicycle wheel",
"confidence": 0.94234306,
"x_min": 0.31524897,
"x_max": 0.44186485,
"y_min": 0.78658724,
"y_max": 0.9692919
},
{
"label": "Bicycle wheel",
"confidence": 0.93370223,
"x_min": 0.50342137,
"x_max": 0.50342137,
"y_min": 0.7553652,
"y_max": 0.7553652
},
{
"label": "Bicycle wheel",
"confidence": 0.93370223,
"x_min": 0.50342137,
"x_max": 0.6289583,
"y_min": 0.7553652,
"y_max": 0.7553652
},
{
"label": "Bicycle wheel",
"confidence": 0.93370223,
"x_min": 0.50342137,
"x_max": 0.6289583,
"y_min": 0.7553652,
"y_max": 0.9428141
},
{
"label": "Bicycle",
"confidence": 0.89731073,
"x_min": 0.31594256,
"x_max": 0.31594256,
"y_min": 0.66489404,
"y_max": 0.66489404
},
{
"label": "Bicycle",
"confidence": 0.89731073,
"x_min": 0.31594256,
"x_max": 0.63338375,
"y_min": 0.66489404,
"y_max": 0.66489404
},
{
"label": "Bicycle",
"confidence": 0.89731073,
"x_min": 0.31594256,
"x_max": 0.63338375,
"y_min": 0.66489404,
"y_max": 0.9687162
},
{
"label": "Picture frame",
"confidence": 0.7171168,
"x_min": 0.7882889,
"x_max": 0.7882889,
"y_min": 0.16610023,
"y_max": 0.16610023
},
{
"label": "Picture frame",
"confidence": 0.7171168,
"x_min": 0.7882889,
"x_max": 0.9662418,
"y_min": 0.16610023,
"y_max": 0.16610023
},
{
"label": "Picture frame",
"confidence": 0.7171168,
"x_min": 0.7882889,
"x_max": 0.9662418,
"y_min": 0.16610023,
"y_max": 0.3178568
}
]
}
}
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