Text

Make predictions on passages of text

Make text predictions via URL

You can make predictions on passages of text hosted on the web

Python
Java
NodeJS
cURL
Python
# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
post_model_outputs_response = stub.PostModelOutputs(
service_pb2.PostModelOutputsRequest(
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:
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))
Java
import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;
// Insert here the initialization code as outlined on this page:
// https://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());
}
NodeJS
// Insert here the initialization code as outlined on this page:
// https://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);
}
}
);
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

Make text predictions on local text files

Make predictions based on local text files.

Python
Java
NodeJS
cURL
Python
# Insert here the initialization code as outlined on this page:
# https://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(
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:
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))
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://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());
}
NodeJS
// Insert here the initialization code as outlined on this page:
// https://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);
}
}
);
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