Hugging Face Transformers

pip install netra-sdk transformers torch

npm install netra-sdk @huggingface/inference

from netra import Netra
from transformers import pipeline
import os

# Initialize Netra with Hugging Face instrumentation
Netra.init(
    app_name="my-ai-app",
    headers=f"x-api-key={os.environ.get('NETRA_API_KEY')}",
    trace_content=True
)

# Use Transformers pipeline - all calls are automatically traced
generator = pipeline("text-generation", model="gpt2")

response = generator("What is observability?", max_length=50)
print(response[0]['generated_text'])

import { Netra } from "netra-sdk";
import { HfInference } from "@huggingface/inference";

async function main() {
  // Initialize Netra with Hugging Face instrumentation (must await)
  await Netra.init({
    appName: "my-ai-app",
    headers: `x-api-key=${process.env.NETRA_API_KEY}`,
    traceContent: true
  });

  // Use Hugging Face Inference API - all calls are automatically traced
  const hf = new HfInference(process.env.HF_TOKEN);

  const response = await hf.textGeneration({
    model: "meta-llama/Llama-2-7b-chat-hf",
    inputs: "What is observability?"
  });

  console.log(response.generated_text);
}

main();

from transformers import TextStreamer

generator = pipeline(
    "text-generation",
    model="gpt2",
    streamer=TextStreamer(skip_prompt=True)
)

# Streaming output is automatically captured
generator("Tell me a story", max_length=100)

const stream = hf.textGenerationStream({
  model: "meta-llama/Llama-2-7b-chat-hf",
  inputs: "Tell me a story"
});

for await (const chunk of stream) {
  process.stdout.write(chunk.token.text);
}

# Text Classification
classifier = pipeline("sentiment-analysis")
result = classifier("I love this product!")

# Summarization
summarizer = pipeline("summarization")
summary = summarizer("Long text to summarize...", max_length=50)

# Question Answering
qa = pipeline("question-answering")
answer = qa({
    "question": "What is AI?",
    "context": "Artificial Intelligence is..."
})

// Text Classification
const classifier = await hf.textClassification({
  model: "distilbert-base-uncased-finetuned-sst-2-english",
  inputs: "I love this product!"
});

// Summarization
const summary = await hf.summarization({
  model: "facebook/bart-large-cnn",
  inputs: "Long text to summarize..."
});

// Question Answering
const answer = await hf.questionAnswering({
  model: "deepset/roberta-base-squad2",
  inputs: {
    question: "What is AI?",
    context: "Artificial Intelligence is..."
  }
});

from netra import Netra
from netra.instrumentation.instruments import InstrumentSet

# Only enable Hugging Face instrumentation
Netra.init(
    headers=f"x-api-key={os.environ.get('NETRA_API_KEY')}",
    instruments={InstrumentSet.TRANSFORMERS}
)

# Or block specific instrumentations
Netra.init(
    headers=f"x-api-key={os.environ.get('NETRA_API_KEY')}",
    block_instruments={InstrumentSet.HTTPX}
)

import { Netra, NetraInstruments } from "netra-sdk";

// Only enable Hugging Face instrumentation
await Netra.init({
  headers: `x-api-key=${process.env.NETRA_API_KEY}`,
  instruments: new Set([NetraInstruments.HUGGINGFACE])
});

// Or block specific instrumentations
await Netra.init({
  headers: `x-api-key=${process.env.NETRA_API_KEY}`,
  blockInstruments: new Set([NetraInstruments.HTTP])
});