Getting started

services/azure_ai_services.py

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+import json
+import io
+import openai
+import os
+import requests
+
+from typing import Generator
+
+from daily_ai.services.ai_services import LLMService, TTSService, ImageGenService
+from PIL import Image
+
+# See .env.example for Azure configuration needed
+from azure.cognitiveservices.speech import SpeechSynthesizer, SpeechConfig, ResultReason, CancellationReason
+
+class AzureTTSService(TTSService):
+    def __init__(self):
+        super().__init__()
+
+        self.speech_key = os.getenv("AZURE_SPEECH_SERVICE_KEY")
+        self.speech_region = os.getenv("AZURE_SPEECH_SERVICE_REGION")
+
+        self.speech_config = SpeechConfig(subscription=self.speech_key, region=self.speech_region)
+        self.speech_synthesizer = SpeechSynthesizer(speech_config=self.speech_config, audio_config=None)
+
+    def run_tts(self, sentence) -> Generator[bytes, None, None]:
+        self.logger.info("⌨️ running azure tts async")
+        ssml = "<speak version='1.0' xml:lang='en-US' xmlns='http://www.w3.org/2001/10/synthesis' " \
+           "xmlns:mstts='http://www.w3.org/2001/mstts'>" \
+           "<voice name='en-US-SaraNeural'>" \
+           "<mstts:silence type='Sentenceboundary' value='20ms' />" \
+           "<mstts:express-as style='lyrical' styledegree='2' role='SeniorFemale'>" \
+           "<prosody rate='1.05'>" \
+           f"{sentence}" \
+           "</prosody></mstts:express-as></voice></speak> "
+        result = self.speech_synthesizer.speak_ssml(ssml)
+        self.logger.info("⌨️ got azure tts result")
+        if result.reason == ResultReason.SynthesizingAudioCompleted:
+            self.logger.info("⌨️ returning result")
+            # azure always sends a 44-byte header. Strip it off.
+            yield result.audio_data[44:]
+        elif result.reason == ResultReason.Canceled:
+            cancellation_details = result.cancellation_details
+            self.logger.info("Speech synthesis canceled: {}".format(cancellation_details.reason))
+            if cancellation_details.reason == CancellationReason.Error:
+                self.logger.info("Error details: {}".format(cancellation_details.error_details))
+
+class AzureLLMService(LLMService):
+    def get_response(self, messages, stream):
+        return openai.ChatCompletion.create(
+            api_type="azure",
+            api_version="2023-06-01-preview",
+            api_key=os.getenv("AZURE_CHATGPT_KEY"),
+            api_base=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+            deployment_id=os.getenv("AZURE_CHATGPT_DEPLOYMENT_ID"),
+            stream=stream,
+            messages=messages,
+        )
+
+
+    def run_llm_async(self, messages) -> Generator[str, None, None]:
+        local_messages = messages.copy()
+        messages_for_log = json.dumps(local_messages)
+        self.logger.info(f"==== generating chat via azure: {messages_for_log}")
+
+        response = self.get_response(local_messages, stream=True)
+
+        for chunk in response:
+            if len(chunk["choices"]) == 0:
+                continue
+
+            if "content" in chunk["choices"][0]["delta"]:
+                if (
+                    chunk["choices"][0]["delta"]["content"] != {}
+                ):  # streaming a content chunk
+                    yield chunk["choices"][0]["delta"]["content"]
+
+
+    def run_llm(self, messages) -> str or None:
+        local_messages = messages.copy()
+        messages_for_log = json.dumps(local_messages)
+        self.logger.info(f"==== generating chat via azure: {messages_for_log}")
+
+        response = self.get_response(local_messages, stream=False)
+        if (
+            response
+            and len(response["choices"]) > 0
+            and "message" in response["choices"][0]
+            and "content" in response["choices"][0]["message"]
+        ):
+            return response["choices"][0]["message"]["content"]
+        else:
+            return None
+
+
+class AzureImageGenService(ImageGenService):
+    def run_image_gen(self, sentence) -> Image.Image:
+        self.logger.info("generating azure image", sentence)
+
+        image = openai.Image.create(
+            api_type = 'azure',
+            api_version = '2023-06-01-preview',
+            api_key = os.getenv('AZURE_DALLE_KEY'),
+            api_base = os.getenv('AZURE_DALLE_ENDPOINT'),
+            deployment_id = os.getenv("AZURE_DALLE_DEPLOYMENT_ID"),
+            prompt=f'{sentence} in the style of {self.image_style}',
+            n=1,
+            size=f"1024x1024",
+        )
+
+        url = image["data"][0]["url"]
+        response = requests.get(url)
+
+        dalle_stream = io.BytesIO(response.content)
+        dalle_im = Image.open(dalle_stream)
+
+        return (url, dalle_im)