Working with Azure AI Services

# python
import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient

def authenticate_client(endpoint: str, key: str) -> TextAnalyticsClient:
    """
    Authenticate and return a TextAnalyticsClient using the provided endpoint and key.
    """
    credential = AzureKeyCredential(key)
    client = TextAnalyticsClient(endpoint=endpoint, credential=credential)
    return client

def sentiment_analysis(client: TextAnalyticsClient, text: str):
    """
    Perform sentiment analysis on a single document string.
    """
    try:
        response = client.analyze_sentiment([text])[0]
        print(f"\nDocument Sentiment: {response.sentiment}")
        print(
            f"Overall scores: positive={response.confidence_scores.positive:.2f}, "
            f"neutral={response.confidence_scores.neutral:.2f}, "
            f"negative={response.confidence_scores.negative:.2f}"
        )
        return response
    except Exception as err:
        print(f"Encountered exception: {err}")
        return None

def main():
    # Replace with your endpoint and key (do not hard-code in production)
    endpoint = "https://ai102cogservices909.cognitiveservices.azure.com/"
    key = "<YOUR_KEY>"
    sample_text = "Learning AI is good for career growth."
    client = authenticate_client(endpoint, key)
    print("Performing sentiment analysis:")
    sentiment_result = sentiment_analysis(client, sample_text)

if __name__ == "__main__":
    main()

# python
import requests
import json

def sentiment_analysis(endpoint: str, key: str, text: str):
    """
    Call the Azure Language analyze-text REST API for sentiment analysis.
    The endpoint should include the trailing slash, e.g. "https://<name>.cognitiveservices.azure.com/".
    """
    url = f"{endpoint}language/:analyze-text?api-version=2023-04-15-preview"

    headers = {
        "Ocp-Apim-Subscription-Key": key,
        "Content-Type": "application/json"
    }

    body = {
        "kind": "SentimentAnalysis",
        "parameters": {
            "modelVersion": "latest",
            "opinionMining": True
        },
        "analysisInput": {
            "documents": [
                {
                    "id": "1",
                    "language": "en",
                    "text": text
                }
            ]
        }
    }

    try:
        response = requests.post(url, headers=headers, json=body)
        if response.status_code == 200:
            sentiment_data = response.json()
            document = sentiment_data["results"]["documents"][0]
            print(f"\nDocument Sentiment: {document['sentiment']}")
            scores = document["confidenceScores"]
            print(
                f"Overall scores: positive={scores['positive']:.2f}, "
                f"neutral={scores['neutral']:.2f}, "
                f"negative={scores['negative']:.2f}"
            )
            return document
        else:
            print(f"Error: {response.status_code}")
            print(response.text)
            return None
    except Exception as err:
        print(f"Encountered exception: {err}")
        return None

def main():
    endpoint = "https://ai102cogservices909.cognitiveservices.azure.com/"  # Endpoint URL (include trailing slash)
    key = "<YOUR_KEY>"
    sample_text = "The food and service were unacceptable."
    print("Performing sentiment analysis:")
    sentiment_analysis(endpoint, key, sample_text)

if __name__ == "__main__":
    main()