Managing Inputs

Learn how to interact with inputs using Clarifai SDKs

---

Effortlessly handle and organize your input data with Clarifai SDKs. The Input Management feature empowers you to efficiently manage various types of data, including images, videos, and text, facilitating seamless integration into your machine learning workflows. Take control of your inputs, whether sourced from URLs, file paths, or raw bytes, and streamline the preparation process for predictive model inferences. Clarifai's Input Management simplifies the task of organizing and preparing data for an enhanced and streamlined machine learning experience.

Image Data as Inputs

The Clarifai SDKs empowers you to seamlessly upload image data through various methods, providing flexibility and ease of integration. Whether your images are hosted online via URLs, stored locally as file paths, or represented as bytes within your application, our API accommodates all these formats. This versatility ensures a smooth and efficient workflow, allowing you to leverage Clarifai's powerful capabilities with the convenience that suits your specific use case.

Visit this page for more information.

Python

from clarifai.client.input import Inputs

img_url = "https://samples.clarifai.com/metro-north.jpg"
input_obj = Inputs(user_id="user_id", app_id="test_app", pat="YOUR_PAT")
# You can also upload data through Bytes and Filepath,

# Upload from file
# input_obj.upload_from_file(input_id='demo', image_file=’image_filepath')

# Upload from bytes
# input_obj.upload_from_bytes(input_id='demo', image_bytes=image)

input_obj.upload_from_url(input_id="demo", image_url=img_url)

Output

2024-01-15 16:38:49 INFO     clarifai.client.input:                                                    input.py:669

                             Inputs Uploaded                                                                       

                             code: SUCCESS                                                                         

                             description: "Ok"                                                                     

                             details: "All inputs successfully added"                                              

                             req_id: "a14eda72951b06cd25561381d70ced74"    

Typescript

import { Input } from "clarifai-nodejs";


const imageUrl = "https://samples.clarifai.com/metro-north.jpg";
const input = new Input({
  authConfig: {
    userId: process.env.CLARIFAI_USER_ID,
    pat: process.env.CLARIFAI_PAT,
    appId: "test_app",
  },
});
await input.uploadFromUrl({
  inputId: "demo",
  imageUrl,
});

Text Data as Inputs

Use the potential of the Clarifai SDKs to effortlessly upload text data through diverse methods, providing a seamless experience and fostering adaptability in your integration process. Whether your text is accessible online via URLs, resides locally as file paths, or is represented as bytes within your application, our API seamlessly accommodates these formats. This versatility ensures a fluid and effective workflow, enabling you to unlock Clarifai's robust capabilities with the utmost convenience tailored to your specific use case.

Visit this page for more information.

Python

from clarifai.client.input import Inputs

input_text = b"Write a tweet on future of AI"
input_obj = Inputs(user_id="user_id", app_id="test_app", pat="YOUR_PAT")

# You can also upload data through URLand Filepath,

# Upload from file
# input_obj.upload_from_file(input_id='text_dat', text_file=’text_filepath')

# Upload from url
# input_obj.upload_from_url(input_id='text,text_url=”text_url”)

input_obj.upload_from_bytes(input_id="text_data", text_bytes=input_text)

Output

2024-01-16 14:14:41 INFO     clarifai.client.input:                                                    input.py:669

                             Inputs Uploaded                                                                       

                             code: SUCCESS                                                                         

                             description: "Ok"                                                                     

                             details: "All inputs successfully added"                                              

                             req_id: "80d2454a1dea0411e20fb03b2fe0c8b1"

Typescript

import { Input } from "clarifai-nodejs";


const inputText = "Write a tweet on future of AI";
const input = new Input({
  authConfig: {
    userId: process.env.CLARIFAI_USER_ID,
    pat: process.env.CLARIFAI_PAT,
    appId: "test_app",
  },
});
input.uploadText({
  inputId: "text_data",
  rawText: inputText,
});

Audio Data as Inputs

Unlock the potential of audio analysis with the Clarifai SDKs, offering seamless integration for uploading audio data through multiple avenues. Whether your audio files reside on external servers accessible via URLs, are stored locally with file paths, or are represented as raw bytes within your application, our API effortlessly accommodates each of these formats. This adaptability ensures a streamlined and user-friendly workflow, providing you the freedom to harness Clarifai's advanced capabilities with the utmost convenience tailored to your specific use case.

Visit this page for more information.

Python

from clarifai.client.input import Inputs

audio_url = "https://s3.amazonaws.com/samples.clarifai.com/GoodMorning.wav"
input_obj = Inputs(user_id="user_id", app_id="test_app", pat="YOUR_PAT")

# You can also upload data through Bytes and Filepath,

# Upload from file
# input_obj.upload_from_file(input_id='audio_data', audio_file=’audio_filepath')

# Upload from bytes
# input_obj.upload_from_bytes(input_id='audio_data’, audio_bytes=audio)

input_obj.upload_from_url(
    input_id="audio_data",
    audio_url=audio_url,
)

Output

2024-01-16 14:18:58 INFO     clarifai.client.input:                                                    input.py:669

                             Inputs Uploaded                                                                       

                             code: SUCCESS                                                                         

                             description: "Ok"                                                                     

                             details: "All inputs successfully added"                                              

                             req_id: "c16d3dd066d7ee48d038744daacef6e8" 

Typescript

import { Input } from "clarifai-nodejs";


const audioUrl =
  "https://s3.amazonaws.com/samples.clarifai.com/GoodMorning.wav";
const input = new Input({
  authConfig: {
    userId: process.env.CLARIFAI_USER_ID,
    pat: process.env.CLARIFAI_PAT,
    appId: "test_app",
  },
});
input.uploadFromUrl({
  inputId: "audio_data",
  audioUrl,
});

Video Data as Inputs

Unlock the potential of video analysis with the Clarifai SDKs, offering seamless integration for uploading video data through various methods. Whether your videos are accessible online via URLs, residing locally as file paths, or encapsulated as bytes within your application, our API effortlessly accommodates these diverse formats.

Visit this page for more information.

Python

from clarifai.client.input import Inputs
video_url = "https://samples.clarifai.com/beer.mp4"
input_obj = Inputs(user_id="user_id", app_id="test_app", pat="YOUR_PAT")

# You can also upload data through Bytes and Filepath,

# Upload from file
# input_obj.upload_from_file(input_id='video_data', video_file=’video_filepath')

# Upload from bytes
# input_obj.upload_from_bytes(input_id='video_data’, video_bytes=video)

input_obj.upload_from_url(
    input_id="video_data", video_url= video_url
)

Output

2024-01-16 14:25:26 INFO     clarifai.client.input:                                                    input.py:669

                             Inputs Uploaded                                                                       

                             code: SUCCESS                                                                         

                             description: "Ok"                                                                     

                             details: "All inputs successfully added"                                              

                             req_id: "00576d040a6254019942ab4eceb306ad" 

Typescript

import { Input } from "clarifai-nodejs";


const videoUrl = "https://samples.clarifai.com/beer.mp4";
const input = new Input({
  authConfig: {
    userId: process.env.CLARIFAI_USER_ID,
    pat: process.env.CLARIFAI_PAT,
    appId: "test_app",
  },
});
await input.uploadFromUrl({
  inputId: "video_data",
  videoUrl,
});

Multimodal Data as Inputs

With the Clarifai SDKs, the integration of multimodal inputs becomes a seamless and intuitive process. Unlock the power of combining various types of inputs by leveraging our API. Whether you're incorporating a mix of images, text, or other data sources, our SDK allows you to specify and upload these multimodal inputs effortlessly. For now the Clarifai platform only supports multimodal inputs like [Image ,Text]->text.

Visit this page for more information.

Python

from clarifai.client.input import Inputs

input_obj = Inputs(user_id="user_id", app_id="test_app", pat="YOUR_PAT")

# initialize inputs of different type
prompt = "What time of day is it?"
image_url = "https://samples.clarifai.com/metro-north.jpg"

# Here you can give the value for different types of inputs
input_obj.get_multimodal_input(
    input_id="multimodal_data", image_url=image_url, raw_text=prompt
)

Output

id: "multimodal_data"
data {
  image {
    url: "https://samples.clarifai.com/metro-north.jpg"
  }
  text {
    raw: "What time of day is it?"
  }
}

Typescript

import { Input } from "clarifai-nodejs";


const prompt = "What time of day is it?";
const imageUrl = "https://samples.clarifai.com/metro-north.jpg";
const multimodalInput = Input.getMultimodalInput({
  inputId: "multimodal_data",
  imageUrl,
  rawText: prompt,
});
console.log(multimodalInput);

Custom Metadata

When working with the Clarifai SDKs, you can add inputs with custom metadata in addition to concepts. This allows you to attach additional information to your inputs, which can be useful for various purposes such as categorization, filtering, or later reference.

Visit this page for more information.

Image With Metadata

In the below example we are uploading an image with metadata that includes details about the filename and to which split it belongs to.

Python

# Import necessary modules
from google.protobuf.struct_pb2 import Struct
from clarifai.client.input import Inputs

# Create an Inputs object with user_id and app_id
input_object = Inputs(user_id="user_id", app_id="app_id", pat="YOUR_PAT")

# Create a Struct object for metadata
metadata = Struct()

# Update metadata with filename and split information
metadata.update({"filename": "XiJinping.jpg", "split": "train"})

# URL of the image to upload
url = "https://samples.clarifai.com/XiJinping.jpg"

# Upload the image from the URL with associated metadata
input_object.upload_from_url(input_id="metadata", image_url=url, metadata=metadata)

Output

2024-04-05 13:03:24 INFO     clarifai.client.input:                                                    input.py:674
                             Inputs Uploaded                                                                       
                             code: SUCCESS                                                                         
                             description: "Ok"                                                                     
                             details: "All inputs successfully added"                                              
                             req_id: "951a64b950cccf05c8d274c8acc1f0f6"                                            
                                                                                                                   
INFO:clarifai.client.input:
Inputs Uploaded
code: SUCCESS
description: "Ok"
details: "All inputs successfully added"
req_id: "951a64b950cccf05c8d274c8acc1f0f6"

('8557e0f57f464c22b3483de76757fb4f',
 status {
   code: SUCCESS
   description: "Ok"
   details: "All inputs successfully added"
   req_id: "951a64b950cccf05c8d274c8acc1f0f6"
 }
 inputs {
   id: "metadata"
   data {
     image {
       url: "https://samples.clarifai.com/XiJinping.jpg"
       image_info {
         format: "UnknownImageFormat"
         color_mode: "UnknownColorMode"
       }
     }
     metadata {
       fields {
         key: "filename"
         value {
           string_value: "XiJinping.jpg"
         }
       }
       fields {
         key: "split"
         value {
           string_value: "train"
         }
       }
     }
   }
   created_at {
     seconds: 1712322204
     nanos: 737881425
   }
   modified_at {
     seconds: 1712322204
     nanos: 737881425
   }
   status {
     code: INPUT_DOWNLOAD_PENDING
     description: "Download pending"
   }
 }
 inputs_add_job {
   id: "8557e0f57f464c22b3483de76757fb4f"
   progress {
     pending_count: 1
   }
   created_at {
     seconds: 1712322204
     nanos: 714751000
   }
   modified_at {
     seconds: 1712322204
     nanos: 714751000
   }
   status {
     code: JOB_QUEUED
     description: "Job is queued to be ran."
   }
 })

Typescript

import { Input } from "clarifai-nodejs";


const input = new Input({
  authConfig: {
    userId: process.env.CLARIFAI_USER_ID,
    pat: process.env.CLARIFAI_PAT,
    appId: "test_app",
  },
});
const metadata = {
  filename: "XiJinping.jpg",
  split: "train",
};
const imageUrl = "https://samples.clarifai.com/XiJinping.jpg";
await input.uploadFromUrl({
  inputId: "image_with_metadata",
  imageUrl,
  metadata,
});

Video With Metadata

In the below example we are uploading a video file with metadata that includes details about the filename and to which split it belongs to.

Python

from google.protobuf.struct_pb2 import Struct
from clarifai.client.input import Inputs

# Initialize an Inputs object with specified user_id and app_id
input_object = Inputs(user_id="user_id", app_id="app_id", pat="YOUR_PAT")

# Define the URL of the video to upload
video_url = "https://samples.clarifai.com/beer.mp4"

# Create a Struct object to hold metadata
metadata = Struct()

# Update the metadata with filename and split information
metadata.update({"filename": "drinks.jpg", "split": "train"})

# Upload the video from the specified URL with the provided metadata
input_object.upload_from_url(
    input_id="video_data_metadata", video_url=video_url, metadata=metadata
)

Output

2024-04-05 13:05:49 INFO     clarifai.client.input:                                                    input.py:674
                             Inputs Uploaded                                                                       
                             code: SUCCESS                                                                         
                             description: "Ok"                                                                     
                             details: "All inputs successfully added"                                              
                             req_id: "72c9820d805efb9f3ee7f0508778c1f3"                                            
                                                                                                                   
INFO:clarifai.client.input:
Inputs Uploaded
code: SUCCESS
description: "Ok"
details: "All inputs successfully added"
req_id: "72c9820d805efb9f3ee7f0508778c1f3"

('7fdc30b9c2a24f31b6a41b32bd9fea02',
 status {
   code: SUCCESS
   description: "Ok"
   details: "All inputs successfully added"
   req_id: "72c9820d805efb9f3ee7f0508778c1f3"
 }
 inputs {
   id: "video_data_metadata"
   data {
     video {
       url: "https://samples.clarifai.com/beer.mp4"
       video_info {
         video_format: "UnknownVideoFormat"
       }
     }
     metadata {
       fields {
         key: "filename"
         value {
           string_value: "drinks.jpg"
         }
       }
       fields {
         key: "split"
         value {
           string_value: "train"
         }
       }
     }
   }
   created_at {
     seconds: 1712322349
     nanos: 628288634
   }
   modified_at {
     seconds: 1712322349
     nanos: 628288634
   }
   status {
     code: INPUT_DOWNLOAD_PENDING
     description: "Download pending"
   }
 }
 inputs_add_job {
   id: "7fdc30b9c2a24f31b6a41b32bd9fea02"
   progress {
     pending_count: 1
   }
   created_at {
     seconds: 1712322349
     nanos: 602487000
   }
   modified_at {
     seconds: 1712322349
     nanos: 602487000
   }
   status {
     code: JOB_QUEUED
     description: "Job is queued to be ran."
   }
 })

Typescript

import { Input } from "clarifai-nodejs";


const input = new Input({
  authConfig: {
    userId: process.env.CLARIFAI_USER_ID,
    pat: process.env.CLARIFAI_PAT,
    appId: "test_app",
  },
});
const metadata = {
  filename: "beer.mp4",
  split: "train",
};
const videoUrl = "https://samples.clarifai.com/beer.mp4";
await input.uploadFromUrl({
  inputId: "video_data_metadata",
  videoUrl,
  metadata,
});

Text With Metadata

In the below example we are uploading a text file with metadata that includes details about the filename and to which split it belongs to.

Python

# Import necessary modules
from google.protobuf.struct_pb2 import Struct
from clarifai.client.input import Inputs

# Define the input object with user_id and app_id
input_object = Inputs(user_id="user_id", app_id="app_id", pat="YOUR_PAT")

# Define the input text
input_text = b"Write a tweet on future of AI"

# Create a Struct object for metadata
metadata = Struct()

# Update metadata with filename and split information
metadata.update({"filename": "tweet.txt", "split": "train"})

# Upload the input from bytes with custom metadata
input_object.upload_from_bytes(input_id="text_data_metadata", text_bytes=input_text, metadata=metadata)

Output

2024-04-05 13:07:04 INFO     clarifai.client.input:                                                    input.py:674
                             Inputs Uploaded                                                                       
                             code: SUCCESS                                                                         
                             description: "Ok"                                                                     
                             details: "All inputs successfully added"                                              
                             req_id: "835f6c736f032947d1f4067e39c10b72"                                            
                                                                                                                   
INFO:clarifai.client.input:
Inputs Uploaded
code: SUCCESS
description: "Ok"
details: "All inputs successfully added"
req_id: "835f6c736f032947d1f4067e39c10b72"

('e3de274f644a4e98a488e7c85f94c0d1',
 status {
   code: SUCCESS
   description: "Ok"
   details: "All inputs successfully added"
   req_id: "835f6c736f032947d1f4067e39c10b72"
 }
 inputs {
   id: "text_data_metadata"
   data {
     metadata {
       fields {
         key: "filename"
         value {
           string_value: "tweet.txt"
         }
       }
       fields {
         key: "split"
         value {
           string_value: "train"
         }
       }
     }
     text {
       url: "https://data.clarifai.com/orig/users/8tzpjy1a841y/apps/visual_classifier_eval/inputs/text/c439598b04d8112867eec70097aa00c2"
       text_info {
         encoding: "UnknownTextEnc"
       }
     }
   }
   created_at {
     seconds: 1712322424
     nanos: 56818659
   }
   modified_at {
     seconds: 1712322424
     nanos: 56818659
   }
   status {
     code: INPUT_DOWNLOAD_PENDING
     description: "Download pending"
   }
 }
 inputs_add_job {
   id: "e3de274f644a4e98a488e7c85f94c0d1"
   progress {
     pending_count: 1
   }
   created_at {
     seconds: 1712322423
     nanos: 941401000
   }
   modified_at {
     seconds: 1712322423
     nanos: 941401000
   }
   status {
     code: JOB_QUEUED
     description: "Job is queued to be ran."
   }
 })

Typescript

import { Input } from "clarifai-nodejs";


const input = new Input({
  authConfig: {
    userId: process.env.CLARIFAI_USER_ID,
    pat: process.env.CLARIFAI_PAT,
    appId: "test_app",
  },
});
const textBytes = Buffer.from("Write a tweet on future of AI");
const metadata = {
  filename: "tweet.txt",
  split: "train",
};
await input.uploadFromBytes({
  inputId: "text_with_metadata",
  textBytes,
  metadata,
});

Audio With Metadata

In the below example we are uploading an audio file with metadata that includes details about the filename and to which split it belongs to.

Python

# Import necessary modules
from clarifai.client.input import Inputs
from google.protobuf.struct_pb2 import Struct


# Define the input object with user_id and app_id
input_object = Inputs(user_id="user_id", app_id="app_id", pat="YOUR_PAT")

# Define the URL of the audio file
audio_url = "https://s3.amazonaws.com/samples.clarifai.com/GoodMorning.wav"

# Create a new Struct to hold metadata
metadata = Struct()

# Update the metadata with filename and split information
metadata.update({"filename": "goodmorning.wav", "split": "test"})

# Upload the input from the specified URL with metadata
input_object.upload_from_url(
    input_id="audio_data_metadata",  # Specify an ID for the input
    audio_url=audio_url,  # URL of the audio file
    metadata=metadata  # Custom metadata associated with the input
)

Output

2024-04-08 06:39:32 INFO     clarifai.client.input:                                                    input.py:674
                             Inputs Uploaded                                                                       
                             code: SUCCESS                                                                         
                             description: "Ok"                                                                     
                             details: "All inputs successfully added"                                              
                             req_id: "4c96e4167170c174838c7987101f3478"                                            
                                                                                                                   
INFO:clarifai.client.input:
Inputs Uploaded
code: SUCCESS
description: "Ok"
details: "All inputs successfully added"
req_id: "4c96e4167170c174838c7987101f3478"

('109349aa790a404db39f6324415a47a5',
 status {
   code: SUCCESS
   description: "Ok"
   details: "All inputs successfully added"
   req_id: "4c96e4167170c174838c7987101f3478"
 }
 inputs {
   id: "audio_data_metadata"
   data {
     metadata {
       fields {
         key: "filename"
         value {
           string_value: "goodmorning.wav"
         }
       }
       fields {
         key: "split"
         value {
           string_value: "test"
         }
       }
     }
     audio {
       url: "https://s3.amazonaws.com/samples.clarifai.com/GoodMorning.wav"
       audio_info {
         audio_format: "UnknownAudioFormat"
       }
     }
   }
   created_at {
     seconds: 1712558372
     nanos: 764691920
   }
   modified_at {
     seconds: 1712558372
     nanos: 764691920
   }
   status {
     code: INPUT_DOWNLOAD_PENDING
     description: "Download pending"
   }
 }
 inputs_add_job {
   id: "109349aa790a404db39f6324415a47a5"
   progress {
     pending_count: 1
   }
   created_at {
     seconds: 1712558372
     nanos: 751997000
   }
   modified_at {
     seconds: 1712558372
     nanos: 751997000
   }
   status {
     code: JOB_QUEUED
     description: "Job is queued to be ran."
   }
 })

Typescript

import { Input } from "clarifai-nodejs";


const input = new Input({
  authConfig: {
    userId: process.env.CLARIFAI_USER_ID,
    pat: process.env.CLARIFAI_PAT,
    appId: "test_app",
  },
});
const metadata = {
  filename: "goodmorning.wav",
  split: "test",
};
const audioUrl =
  "https://s3.amazonaws.com/samples.clarifai.com/GoodMorning.wav";
await input.uploadFromUrl({
  inputId: "audio_data_metadata",
  audioUrl,
  metadata,
});

List inputs

Effortlessly explore and manage your inputs with the Clarifai SDKs. By utilizing the list_inputs() method, you gain the ability to seamlessly view all inputs within your app. This powerful function supports features like pagination, enabling a well-organized display of information. Tailor your queries by setting parameters such as page_no and per_page to align with your specific requirements.

Visit this page for more information.

Python

from clarifai.client.user import User

# Create the input object
input_obj = User(user_id="user_id").app(app_id="test_app", pat="YOUR_PAT").inputs()
# list the inputs with pagination
all_inputs = list(input_obj.list_inputs(page_no=1,per_page=3))
print(all_inputs)

Output

[id: "demo1"
data {
  image {
    url: "https://samples.clarifai.com/metro-north.jpg"
    hosted {
      prefix: "https://data.clarifai.com"
      suffix: "users/8tzpjy1a841y/apps/test_app/inputs/image/140c856dc82565d2c4d6ea720fceff78"
      sizes: "orig"
      sizes: "tiny"
      sizes: "small"
      sizes: "large"
      crossorigin: "use-credentials"
    }
    image_info {
      width: 512
      height: 384
      format: "JPEG"
      color_mode: "YUV"
    }
  }
}
created_at {
  seconds: 1705917660
  nanos: 789409000
}
...
  code: INPUT_DOWNLOAD_SUCCESS
  description: "Download complete"
}
]

Delete Inputs

Effortlessly manage your input data with the Clarifai SDKs's Delete Inputs feature. Through the API, you gain the ability to delete inputs seamlessly by providing a list of input IDs. This straightforward and intuitive process empowers you to maintain control over your dataset, allowing for efficient removal of specific inputs as needed.

caution

Be certain that you want to delete a particular input as the operation cannot be undone.

Python

from clarifai.client.user import User

input_obj = User(user_id="user_id", pat="YOUR_PAT").app(app_id="test_app").inputs()
# provide the inputs ids as parameters in delete_inputs function
input_obj.delete_inputs(list(input_obj.list_inputs()))

Output

2024-01-16 14:44:28 INFO     clarifai.client.input:                                                    input.py:732

                             Inputs Deleted                                                                        

                             code: SUCCESS                                                                         

                             description: "Ok"                                                                     

                             req_id: "4ae26cd15c7da98a1c2d3647b03d2768"  

Patch Inputs

You can apply patch operations to an input, allowing for the merging or removal of items. By default, these actions overwrite existing data, but they behave differently when handling lists of objects.

• The merge action replaces a key:value pair with a key:new_value, or appends new values to an existing list. When dealing with dictionaries, it merges entries that share the same id field.

• The remove action replaces a key:value pair with a key:new_value, or removes any items from a list that match the IDs of the provided values.

• The overwrite action fully replaces an existing object with a new one.

Metadata

Here is an example of how to patch the metadata of an input.

Python

from clarifai.client.input import Inputs
from google.protobuf.struct_pb2 import Struct

# Metadata structure should be of Struct, so we create it, add the necessary details and provide it to input proto
metadata = Struct() 
metadata.update({"split": "test"}) 

# Initialize the Inputs object with user and app IDs
input_object = Inputs(user_id="YOUR_USER_ID_HERE", app_id="YOUR_APP_ID_HERE", pat="YOUR_PAT_HERE")

new_input = input_object._get_proto(input_id="YOUR_INPUT_ID_HERE", metadata= metadata)

# Update the metadata
input_object.patch_inputs([new_input],action="merge")

# Overwrite the metadata
input_object.patch_inputs([new_input],action='overwrite')

Bounding Box Annotation

Here is an example of how to patch a bounding box annotation on an input.

Python

from clarifai.client.input import Inputs

# Initialize the Inputs object with user and app IDs
input_object = Inputs(user_id="YOUR_USER_ID_HERE", app_id="YOUR_APP_ID_HERE", pat="YOUR_PAT_HERE")

# Upload the image with a specific input ID
input_object.upload_from_url(input_id="bbox", image_url="https://samples.clarifai.com/BarackObama.jpg")

# Upload initial bounding box annotations
bbox_points = [.1, .1, .8, .9]  # Coordinates of the bounding box
annotation = input_object.get_bbox_proto(input_id="bbox", label="face", bbox=bbox_points, label_id="id-face", annot_id="demo")
input_object.upload_annotations([annotation])

# Update existing bounding box annotations with new coordinates
bbox_points = [.35, .45, .6, .7]  # New coordinates of the bounding box
annotation = input_object.get_bbox_proto(input_id="bbox", label="face", bbox=bbox_points, label_id="id-face", annot_id="demo")
input_object.patch_annotations([annotation], action='merge')

# Remove the bounding box annotations
bbox_points = [.3, .3, .6, .7]  # Coordinates of the bounding box to be removed
annotation = input_object.get_bbox_proto(input_id="bbox", label="face", bbox=bbox_points, label_id="id-face", annot_id="demo")
input_object.patch_annotations([annotation], action='remove')

Polygon Annotation

Here is an example of how to patch a polygon annotation on an input.

Python

from clarifai.client.input import Inputs

# Initialize the Inputs object with user and app IDs
input_object = Inputs(user_id="YOUR_USER_ID_HERE", app_id="YOUR_APP_ID_HERE", pat="YOUR_PAT_HERE")

# Upload the image with a specific input ID
input_object.upload_from_url(input_id="polygon", image_url="https://samples.clarifai.com/BarackObama.jpg")

# Upload initial polygon annotations
polygon_pts = [[.1,.1],[.1,.9],[.9,.9],[.9,.1]] # Coordinates of the polygon
annotation = input_object.get_mask_proto(input_id="polygon", label="label", polygons=polygon_pts, annot_id="annotation_id")
input_object.upload_annotations([annotation])

# Update existing polygon annotations with new coordinates
polygon_pts = [[.15,.15],[.15,.95],[.95,.95],[.95,.15]] # New coordinates of the polygon
annotation = input_object.get_mask_proto(input_id="polygon", label="label", polygons=polygon_pts, annot_id="annotation_id")
input_object.patch_annotations([annotation],action='merge')

# Remove the polygon annotations
polygon_pts = [[.3,.3],[.3,.7],[.8,.8],[.7,.3]] # Coordinates of the polygon to be removed
annotation = input_object.get_mask_proto(input_id="polygon", label="label", polygons=polygon_pts, annot_id="annotation_id")
input_object.patch_annotations([annotation],action='remove')

Concepts

Below is an example of performing a patch operation on concepts. Currently, only the overwrite action is supported, allowing you to update the label names associated with an input.

Python

from clarifai.client.input import Inputs

# Initialize the Inputs object with user and app IDs
input_object = Inputs(user_id="YOUR_USER_ID_HERE", app_id="YOUR_APP_ID_HERE", pat="YOUR_PAT_HERE")

# This example changes the existing concept label "id-face" to "obama_face"
input_object.patch_concepts(
    concept_ids=["id-face"],  # The ID of the concept you want to update
    labels=["obama_face"],    # The new label name to overwrite the existing one
    values=[],                
    action='overwrite'        
)