Implement text analysis solutions

from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient

client = TextAnalyticsClient(endpoint=LANGUAGE_ENDPOINT, credential=AzureKeyCredential(KEY))

# PII detection returns a redacted string plus per-entity detail and a
# confidence score — deterministic and auditable, which a prompt is not.
result = client.recognize_pii_entities(
    documents=[transcript],
    language="en",
    categories_filter=["Person", "PhoneNumber", "Email", "CreditCardNumber"],
)[0]

print(result.redacted_text)
for entity in result.entities:
    print(entity.category, entity.text, round(entity.confidence_score, 2))

from pydantic import BaseModel
from typing import Literal

class ComplaintAnalysis(BaseModel):
    summary: str
    product: str
    sentiment: Literal["positive", "neutral", "negative"]
    topics: list[str]
    contains_personal_data: bool
    escalate: bool

# One call replaces separate entity, topic, sentiment and summarisation calls —
# and the schema guarantees the shape for downstream code.
result = openai.responses.parse(
    model="gpt-5-mini",
    input=[
        {"role": "system", "content":
            "Analyse the customer complaint. Escalate only when there is a "
            "regulatory, safety or contractual breach. Topics must come from: "
            "billing, delivery, quality, support, privacy."},
        {"role": "user", "content": complaint_text},
    ],
    text_format=ComplaintAnalysis,
)
analysis = result.output_parsed

import azure.cognitiveservices.speech as speechsdk

speech_config = speechsdk.SpeechConfig(endpoint=SPEECH_ENDPOINT)
speech_config.speech_recognition_language = "en-GB"

# A custom speech model handles product names, acronyms and accents that the
# base model mis-transcribes. Point the config at your deployed endpoint id.
speech_config.endpoint_id = "your-custom-speech-endpoint-id"

recognizer = speechsdk.SpeechRecognizer(speech_config=speech_config)
result = recognizer.recognize_once_async().get()

# Synthesis: pick a voice by name; SSML gives control over prosody and style.
speech_config.speech_synthesis_voice_name = "en-GB-SoniaNeural"
synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config)
synthesizer.speak_text_async("Your order has shipped.").get()

<speak version="1.0" xmlns="http://www.w3.org/2001/10/synthesis"
       xmlns:mstts="https://www.w3.org/2001/mstts" xml:lang="en-GB">
  <voice name="en-GB-SoniaNeural">
    <mstts:express-as style="empathetic" styledegree="1.4">
      I'm sorry your delivery was late.
    </mstts:express-as>
    <break time="400ms"/>
    Your refund of <say-as interpret-as="currency">GBP 24.99</say-as>
    has been issued. Reference
    <say-as interpret-as="characters">AB1274</say-as>.
    <prosody rate="-8%">Thank you for your patience.</prosody>
  </voice>
</speak>