| description |
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Make predictions on passages of text |
You can make predictions on passages of text hosted on the web
{% tabs %} {% tab title="Python" %}
# Insert here the initialization code as outlined on this page:
# https://old-docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
post_model_outputs_response = stub.PostModelOutputs(
service_pb2.PostModelOutputsRequest(
user_app_id=userDataObject, # The userDataObject is created in the overview and is required when using a PAT
model_id="{THE_MODEL_ID}",
version_id="{THE_MODEL_VERSION_ID}", # This is optional. Defaults to the latest model version.
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(
text=resources_pb2.Text(
base64=file_bytes
)
)
)
]
),
metadata=metadata
)
if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
print("There was an error with your request!")
print("\tCode: {}".format(post_model_outputs_response.outputs[0].status.code))
print("\tDescription: {}".format(post_model_outputs_response.outputs[0].status.description))
print("\tDetails: {}".format(respopost_model_outputs_responsense.outputs[0].status.details))
raise Exception("Post model outputs failed, status: " + post_model_outputs_response.status.description)
# Since we have one input, one output will exist here.
output = post_model_outputs_response.outputs[0]
print("Predicted concepts:")
for concept in output.data.concepts:
print("%s %.2f" % (concept.name, concept.value)){% endtab %}
{% tab title="PHP" %}
<?php
# Insert here the initialization code as outlined on this page:
# https://old-docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
///////////////////////////////////////////////////////////////////////////////
// Specifying the Request Data
///////////////////////////////////////////////////////////////////////////////
//
// In the Clarifai platform a text is defined by a special Text object.
// There are several ways in which a Text object can be populated including
// by url and raw string.
//
$text = new Text([
'url' => 'https://samples.clarifai.com/negative_sentence_12.txt'
]);
//
// After an Text object is created, a Data object is constructed around it.
// The Data object offers a container that contains additional text independent
// metadata. In this particular use case, no other metadata is needed to be
// specified.
//
$data = new Data([
'text' => $text
]);
//
// The Data object is then wrapped in an Input object in order to meet the
// API specification. Additional fields are available to populate in the Input
// object, but for the purposes of this example we can send in just the
// Data object.
//
$input = new Input([
'data' => $data
]);
///////////////////////////////////////////////////////////////////////////////
// Creating the request object
///////////////////////////////////////////////////////////////////////////////
//
// Finally, the request object itself is created. This object carries the request
// along with the request status and other metadata related to the request itself.
// In this example we populate:
// - the `user_app_id` field with the UserAppIDSet constructed above
// - the `model_id` field with the ID of the model we are referencing
// - the `inputs` field with an array of input objects constructed above
//
$request = new PostModelOutputsRequest([
'user_app_id' => $userDataObject, // This is defined above
'model_id' => 'aaa03c23b3724a16a56b629203edc62c', // This is the ID of the publicly available General model.
'inputs' => [$input]
]);
///////////////////////////////////////////////////////////////////////////////
// Making the RPC call
///////////////////////////////////////////////////////////////////////////////
//
// Once the request object is constructed, we can call the actual request to the
// Clarifai platform. This uses the opened gRPC client channel to communicate the
// request and then wait for the response.
//
[$response, $status] = $client->PostModelOutputs(
$request,
$metadata
)->wait();
///////////////////////////////////////////////////////////////////////////////
// Handling the Response
///////////////////////////////////////////////////////////////////////////////
//
// The response is returned and the first thing we do is check the status of it.
// A successful response will have a status code of 0, otherwise there is some
// reported error.
//
if ($status->code !== 0) throw new Exception("Error: {$status->details}");
//
// In addition to the RPC response status, there is a Clarifai API status that
// reports if the operationo was a success or failure (not just that the commuunication)
// was successful.
//
if ($response->getStatus()->getCode() != StatusCode::SUCCESS) {
throw new Exception("Failure response: " . $response->getStatus()->getDescription() . " " .
$response->getStatus()->getDetails());
}
//
// The output of a successful call can be used in many ways. In this example,
// we loop through all of the predicted concepts and print them out along with
// their numerical prediction value (confidence).
//
echo "Predicted concepts:\n";
foreach ($response->getOutputs()[0]->getData()->getConcepts() as $concept) {
echo $concept->getName() . ": " . number_format($concept->getValue(), 2) . "\n";
}
?>{% endtab %}
{% tab title="Java" %}
import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;
// Insert here the initialization code as outlined on this page:
// https://old-docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
MultiOutputResponse postModelOutputsResponse = stub.postModelOutputs(
PostModelOutputsRequest.newBuilder()
.setModelId("{THE_MODEL_ID}")
.setVersionId("{THE_MODEL_VERSION_ID") // This is optional. Defaults to the latest model version.
.addInputs(
Input.newBuilder().setData(
Data.newBuilder().setText(
Text.newBuilder().setUrl("https://samples.clarifai.com/negative_sentence_12.txt")
)
)
)
.build()
);
if (postModelOutputsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
throw new RuntimeException("Post model outputs failed, status: " + postModelOutputsResponse.getStatus());
}
// Since we have one input, one output will exist here.
Output output = postModelOutputsResponse.getOutputs(0);
System.out.println("Predicted concepts:");
for (Concept concept : output.getData().getConceptsList()) {
System.out.printf("%s %.2f%n", concept.getName(), concept.getValue());
}{% endtab %}
{% tab title="NodeJS" %}
// Insert here the initialization code as outlined on this page:
// https://old-docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
stub.PostModelOutputs(
{
model_id: "{THE_MODEL_ID}",
version_id: "{THE_MODEL_VERSION_ID}", // This is optional. Defaults to the latest model version.
inputs: [
{data: {text: {url: "https://samples.clarifai.com/negative_sentence_12.txt"}}}
]
},
metadata,
(err, response) => {
if (err) {
throw new Error(err);
}
if (response.status.code !== 10000) {
throw new Error("Post model outputs failed, status: " + response.status.description);
}
// Since we have one input, one output will exist here.
const output = response.outputs[0];
console.log("Predicted concepts:");
for (const concept of output.data.concepts) {
console.log(concept.name + " " + concept.value);
}
}
);{% endtab %}
{% tab title="cURL" %}
curl -X POST
-H 'Authorization: Key YOUR_API_KEY'
-H "Content-Type: application/json"
-d '
{
"inputs": [
{
"data": {
"text": {
"url": "https://samples.clarifai.com/negative_sentence_12.txt"
}
}
}
]
}'
https://api.clarifai.com/v2/models/{THE_MODEL_ID}/versions/{THE_MODEL_VERSION_ID}/outputs
{% endtab %}
{% tab title="Javascript (REST)" %}
const raw = JSON.stringify({
"user_app_id": {
"user_id": "{YOUR_USER_ID}",
"app_id": "{YOUR_APP_ID}"
},
"inputs": [
{
"data": {
"text": {
"url": "https://samples.clarifai.com/negative_sentence_12.txt"
}
}
}
]
});
const requestOptions = {
method: 'POST',
headers: {
'Accept': 'application/json',
'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
},
body: raw
};
// NOTE: MODEL_VERSION_ID is optional, you can also call prediction with the MODEL_ID only
// https://api.clarifai.com/v2/models/{YOUR_MODEL_ID}/outputs
// this will default to the latest version_id
fetch("https://api.clarifai.com/v2/models/{YOUR_MODEL_ID}/versions/{MODEL_VERSION_ID}/outputs", requestOptions)
.then(response => response.text())
.then(result => console.log(result))
.catch(error => console.log('error', error));{% endtab %}
{% endtabs %}
Make predictions based on local text files.
{% tabs %} {% tab title="Python" %}
# Insert here the initialization code as outlined on this page:
# https://old-docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
with open("{YOUR_IMAGE_FILE_LOCATION}", "rb") as f:
file_bytes = f.read()
post_model_outputs_response = stub.PostModelOutputs(
service_pb2.PostModelOutputsRequest(
user_app_id=userDataObject, # The userDataObject is created in the overview and is required when using a PAT
model_id="{THE_MODEL_ID}",
version_id="{THE_MODEL_VERSION_ID}", # This is optional. Defaults to the latest model version.
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(
text=resources_pb2.Text(
raw="Butchart Gardens contains over 900 varieties of plants."
)
)
)
]
),
metadata=metadata
)
if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
print("There was an error with your request!")
print("\tCode: {}".format(post_model_outputs_response.outputs[0].status.code))
print("\tDescription: {}".format(post_model_outputs_response.outputs[0].status.description))
print("\tDetails: {}".format(respopost_model_outputs_responsense.outputs[0].status.details))
raise Exception("Post model outputs failed, status: " + post_model_outputs_response.status.description)
# Since we have one input, one output will exist here.
output = post_model_outputs_response.outputs[0]
print("Predicted concepts:")
for concept in output.data.concepts:
print("%s %.2f" % (concept.name, concept.value)){% endtab %}
{% tab title="Java" %}
import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;
import com.google.protobuf.ByteString;
import java.io.File;
import java.nio.file.Files;
// Insert here the initialization code as outlined on this page:
// https://old-docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
MultiOutputResponse postModelOutputsResponse = stub.postModelOutputs(
PostModelOutputsRequest.newBuilder()
.setModelId("{THE_MODEL_ID}")
.setVersionId("{THE_MODEL_VERSION_ID") // This is optional. Defaults to the latest model version.
.addInputs(
Input.newBuilder().setData(
Data.newBuilder().setText(
Text.newBuilder()
.setRaw(ByteString.copyFrom(Files.readAllBytes(
new File("{YOUR_IMAGE_FILE_LOCATION}").toPath()
)))
)
)
)
.build()
);
if (postModelOutputsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
throw new RuntimeException("Post model outputs failed, status: " + postModelOutputsResponse.getStatus());
}
// Since we have one input, one output will exist here.
Output output = postModelOutputsResponse.getOutputs(0);
System.out.println("Predicted concepts:");
for (Concept concept : output.getData().getConceptsList()) {
System.out.printf("%s %.2f%n", concept.getName(), concept.getValue());
}{% endtab %}
{% tab title="NodeJS" %}
// Insert here the initialization code as outlined on this page:
// https://old-docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
const fs = require("fs");
const imageBytes = fs.readFileSync("{YOUR_IMAGE_FILE_LOCATION}");
stub.PostModelOutputs(
{
model_id: "{THE_MODEL_ID}",
version_id: "{THE_MODEL_VERSION_ID}", // This is optional. Defaults to the latest model version.
inputs: [
{data: {text: {raw: textFile}}}
]
},
metadata,
(err, response) => {
if (err) {
throw new Error(err);
}
if (response.status.code !== 10000) {
throw new Error("Post model outputs failed, status: " + response.status.description);
}
// Since we have one input, one output will exist here.
const output = response.outputs[0];
console.log("Predicted concepts:");
for (const concept of output.data.concepts) {
console.log(concept.name + " " + concept.value);
}
}
);{% endtab %}
{% tab title="cURL" %}
// Smaller files (195 KB or less)
curl -X POST \
-H "Authorization: Key YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '
{
"inputs": [
{
"data": {
"text": {
"raw": "'"$(raw /home/user/image.jpeg)"'"
}
}
}
]
}'\
https://api.clarifai.com/v2/models/{THE_MODEL_ID}/outputs
{% endtab %}
{% tab title="Javascript (REST)" %}
const raw = JSON.stringify({
"user_app_id": {
"user_id": "{YOUR_USER_ID}",
"app_id": "{YOUR_APP_ID}"
},
"inputs": [
{
"data": {
"text": {
"raw": "{YOUR_RAW_TEXT}"
}
}
}
]
});
const requestOptions = {
method: 'POST',
headers: {
'Accept': 'application/json',
'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
},
body: raw
};
// NOTE: MODEL_VERSION_ID is optional, you can also call prediction with the MODEL_ID only
// https://api.clarifai.com/v2/models/{YOUR_MODEL_ID}/outputs
// this will default to the latest version_id
fetch("https://api.clarifai.com/v2/models/{YOUR_MODEL_ID}/versions/{MODEL_VERSION_ID}/outputs", requestOptions)
.then(response => response.text())
.then(result => console.log(result))
.catch(error => console.log('error', error));{% endtab %}
{% endtabs %}