From 5ebf3205aa9c746db6c58a7acd8a75cd75525205 Mon Sep 17 00:00:00 2001 From: Sheepsta300 <128811766+Sheepsta300@users.noreply.github.com> Date: Sat, 19 Oct 2024 13:20:40 +1300 Subject: [PATCH] Feature/azure face (#20) * Create an Azure face tool and unit tests * Update test file and create docs * Update cell order * Lint files * Correct spelling and types * Add suggested review changes --- docs/docs/example_data/face.jpg | Bin 0 -> 1096627 bytes .../integrations/tools/azure_ai_face.ipynb | 389 ++++++++++++++++++ libs/community/extended_testing_deps.txt | 1 + .../tools/azure_ai_services/face_analysis.py | 235 +++++++++++ libs/community/tests/examples/face.jpg | Bin 0 -> 1096627 bytes libs/community/tests/examples/no_face.jpg | Bin 0 -> 3160856 bytes .../azure_ai_services/test_face_analysis.py | 79 ++++ 7 files changed, 704 insertions(+) create mode 100644 docs/docs/example_data/face.jpg create mode 100644 docs/docs/integrations/tools/azure_ai_face.ipynb create mode 100644 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the Azure AI Face Service/API. The Tool will detect faces is in an image \n", + ">and can supply the position of faces, as well as important features like eyes, pupils, head pose etc." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tool features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This integration allows for the detection of faces using Azure's Face API. It supports various different models, but defaults to the latest. Furthermore, the user can configure which information they would like to be returned such as position of the faces and image quality of the faces." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "This section provides details about how the Azure AI Face integration works, including setup." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from langchain import hub" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use a prompt to instruct the model. LangChain prompts can be configured, but for simplicity, we will use a premade prompt from LangSmith. This requires an API key, which can be set up here after registration." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "LANGSMITH_KEY = os.environ[\"LANGSMITH_KEY\"]\n", + "prompt = hub.pull(\"hwchase17/structured-chat-agent\", api_key=LANGSMITH_KEY)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use the `AzureAIFaceAnalysisTool` combined with a model, using `create_structured_chat_agent`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.agents import AgentExecutor, create_structured_chat_agent\n", + "from langchain_community.tools.azure_ai_services.face_analysis import (\n", + " AzureAIFaceAnalysisTool,\n", + ")\n", + "from langchain_openai import AzureChatOpenAI" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Credentials" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Credentials can be set by being passed as parameters and should be stored locally as environment variables." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "face_endpoint = os.environ[\"AZURE_AI_FACE_ENDPOINT\"]\n", + "face_key = os.environ[\"AZURE_AI_FACE_KEY\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Instantiation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Credentials can be passed directly, but they can also be retrieved automatically by the constructor if environment variables named `AZURE_AI_FACE_ENDPOINT` and `AZURE_AI_FACE_KEY` are set." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "face = AzureAIFaceAnalysisTool(\n", + " azure_ai_face_key=face_key,\n", + " azure_ai_face_endpoint=face_endpoint,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "tools = [face]\n", + "\n", + "model = AzureChatOpenAI(\n", + " openai_api_version=os.environ[\"OPENAI_API_VERSION\"],\n", + " azure_deployment=os.environ[\"COMPLETIONS_MODEL\"],\n", + " azure_endpoint=os.environ[\"AZURE_OPENAI_ENDPOINT\"],\n", + " api_key=os.environ[\"AZURE_OPENAI_API_KEY\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Creating an `AgentExecutor` chain allows a model to use tools to assist in it's response." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "agent = create_structured_chat_agent(model, tools, prompt)\n", + "\n", + "agent_executor = AgentExecutor(\n", + " agent=agent, tools=tools, verbose=True, handle_parsing_errors=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Invocation\n", + "\n", + "Get either a local file path or remote image url as input" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "image_path = \"../../example_data/face.jpg\"" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "input = image_path" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [Invoke directly with args](/docs/concepts/#invoke-with-just-the-arguments)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Firstly, the tool can be used by directly passing input as an argument." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"FACE: 1\\nfaceId: 9b31b927-24a5-405c-95a9-d76f457e7160\\nfaceRectangle: {'top': 760, 'left': 1159, 'width': 853, 'height': 1337}\\nfaceLandmarks: {'pupilLeft': {'x': 1293.2, 'y': 1319.9}, 'pupilRight': {'x': 1671.1, 'y': 1306.7}, 'noseTip': {'x': 1383.7, 'y': 1593.6}, 'mouthLeft': {'x': 1333.1, 'y': 1821.3}, 'mouthRight': {'x': 1614.4, 'y': 1823.6}, 'eyebrowLeftOuter': {'x': 1167.6, 'y': 1199.4}, 'eyebrowLeftInner': {'x': 1348.4, 'y': 1214.3}, 'eyeLeftOuter': {'x': 1237.2, 'y': 1321.2}, 'eyeLeftTop': {'x': 1294.7, 'y': 1286.6}, 'eyeLeftBottom': {'x': 1289.7, 'y': 1347.3}, 'eyeLeftInner': {'x': 1351.2, 'y': 1324.3}, 'eyebrowRightInner': {'x': 1522.6, 'y': 1201.8}, 'eyebrowRightOuter': {'x': 1826.9, 'y': 1196.0}, 'eyeRightInner': {'x': 1605.4, 'y': 1317.3}, 'eyeRightTop': {'x': 1662.8, 'y': 1268.2}, 'eyeRightBottom': {'x': 1672.7, 'y': 1335.6}, 'eyeRightOuter': {'x': 1743.3, 'y': 1305.7}, 'noseRootLeft': {'x': 1396.7, 'y': 1342.7}, 'noseRootRight': {'x': 1499.7, 'y': 1337.8}, 'noseLeftAlarTop': {'x': 1350.5, 'y': 1516.6}, 'noseRightAlarTop': {'x': 1522.3, 'y': 1518.2}, 'noseLeftAlarOutTip': {'x': 1321.3, 'y': 1611.3}, 'noseRightAlarOutTip': {'x': 1561.3, 'y': 1623.0}, 'upperLipTop': {'x': 1426.4, 'y': 1773.3}, 'upperLipBottom': {'x': 1428.9, 'y': 1811.0}, 'underLipTop': {'x': 1428.9, 'y': 1836.5}, 'underLipBottom': {'x': 1430.2, 'y': 1898.5}}\\nfaceAttributes: {'headPose': {'pitch': -12.9, 'roll': 1.3, 'yaw': -22.0}, 'qualityForRecognition': 'medium'}\\nrecognitionModel: recognition_04\\n\"" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "face.invoke({\"query\": input})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [Invoke with ToolCall](/docs/concepts/#invoke-with-toolcall)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By using `.invoke`, the model can be told what to do and assess if using the tools it was given would assist in it's response. Here we will use a remote url as input." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "image_path = \"https://upload.wikimedia.org/wikipedia/commons/thumb/1/17/Guy_Sebastian_2014.jpg/330px-Guy_Sebastian_2014.jpg\"\n", + "input = image_path" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", + "\u001b[32;1m\u001b[1;3m{\n", + " \"action\": \"azure_ai_face_analysis\",\n", + " \"action_input\": {\n", + " \"query\": \"https://upload.wikimedia.org/wikipedia/commons/thumb/1/17/Guy_Sebastian_2014.jpg/330px-Guy_Sebastian_2014.jpg\"\n", + " }\n", + "}\u001b[0m\u001b[36;1m\u001b[1;3mFACE: 1\n", + "faceId: ab842360-a7fc-4570-8082-8f0928064141\n", + "faceRectangle: {'top': 69, 'left': 47, 'width': 209, 'height': 266}\n", + "faceLandmarks: {'pupilLeft': {'x': 101.5, 'y': 192.0}, 'pupilRight': {'x': 194.8, 'y': 183.8}, 'noseTip': {'x': 148.4, 'y': 237.3}, 'mouthLeft': {'x': 109.1, 'y': 265.7}, 'mouthRight': {'x': 199.8, 'y': 257.2}, 'eyebrowLeftOuter': {'x': 70.6, 'y': 174.6}, 'eyebrowLeftInner': {'x': 120.7, 'y': 175.5}, 'eyeLeftOuter': {'x': 85.3, 'y': 192.7}, 'eyeLeftTop': {'x': 102.7, 'y': 188.3}, 'eyeLeftBottom': {'x': 100.1, 'y': 195.2}, 'eyeLeftInner': {'x': 118.0, 'y': 191.7}, 'eyebrowRightInner': {'x': 168.4, 'y': 172.0}, 'eyebrowRightOuter': {'x': 225.9, 'y': 164.1}, 'eyeRightInner': {'x': 178.8, 'y': 186.5}, 'eyeRightTop': {'x': 193.0, 'y': 179.8}, 'eyeRightBottom': {'x': 195.7, 'y': 187.1}, 'eyeRightOuter': {'x': 211.6, 'y': 181.8}, 'noseRootLeft': {'x': 132.9, 'y': 196.0}, 'noseRootRight': {'x': 159.7, 'y': 194.0}, 'noseLeftAlarTop': {'x': 128.6, 'y': 222.3}, 'noseRightAlarTop': {'x': 168.8, 'y': 219.0}, 'noseLeftAlarOutTip': {'x': 120.7, 'y': 236.2}, 'noseRightAlarOutTip': {'x': 179.7, 'y': 231.9}, 'upperLipTop': {'x': 154.7, 'y': 261.4}, 'upperLipBottom': {'x': 153.4, 'y': 267.4}, 'underLipTop': {'x': 154.5, 'y': 281.0}, 'underLipBottom': {'x': 155.5, 'y': 291.9}}\n", + "faceAttributes: {'headPose': {'pitch': -12.3, 'roll': -5.0, 'yaw': -1.5}, 'qualityForRecognition': 'high'}\n", + "recognitionModel: recognition_04\n", + "\u001b[0m\u001b[32;1m\u001b[1;3mAction:\n", + "```\n", + "{\n", + " \"action\": \"Final Answer\",\n", + " \"action_input\": \"The image contains one face. The faceId is ab842360-a7fc-4570-8082-8f0928064141, and the face is located at the rectangle with top: 69, left: 47, width: 209, and height: 266. The face has various landmarks and attributes, including a high quality for recognition.\"\n", + "}\n", + "```\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "{'input': 'Can you check the following image for faces : https://upload.wikimedia.org/wikipedia/commons/thumb/1/17/Guy_Sebastian_2014.jpg/330px-Guy_Sebastian_2014.jpg',\n", + " 'output': 'The image contains one face. The faceId is ab842360-a7fc-4570-8082-8f0928064141, and the face is located at the rectangle with top: 69, left: 47, width: 209, and height: 266. The face has various landmarks and attributes, including a high quality for recognition.'}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "agent_executor.invoke(\n", + " {\"input\": f\"Can you check the following image for faces : {input}\"}\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chaining" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When creating an `AgentExecutor` as described earlier, an execution chain is formed. The sequence of events will be printed using the given prompt, and actions will occur in a chain-like manner." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "agent = create_structured_chat_agent(model, tools, prompt)\n", + "\n", + "agent_executor = AgentExecutor(\n", + " agent=agent, tools=tools, verbose=True, handle_parsing_errors=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## API reference" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Azure AI Face python SDK](https://learn.microsoft.com/python/api/overview/azure/ai-vision-face-readme?view=azure-python-preview) | [Azure AI Face Overview](https://learn.microsoft.com/azure/ai-services/computer-vision/overview-identity)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/libs/community/extended_testing_deps.txt b/libs/community/extended_testing_deps.txt index 060c2d824016f..8e3d772743625 100644 --- a/libs/community/extended_testing_deps.txt +++ b/libs/community/extended_testing_deps.txt @@ -6,6 +6,7 @@ assemblyai>=0.17.0,<0.18 atlassian-python-api>=3.36.0,<4 azure-ai-contentsafety>=1.0.0 azure-ai-documentintelligence>=1.0.0b1,<2 +azure.ai.vision.face>=1.0.0b1 azure-identity>=1.15.0,<2 azure-search-documents==11.4.0 azure.ai.vision.imageanalysis>=1.0.0b3 diff --git a/libs/community/langchain_community/tools/azure_ai_services/face_analysis.py b/libs/community/langchain_community/tools/azure_ai_services/face_analysis.py new file mode 100644 index 0000000000000..6d8b02aeba814 --- /dev/null +++ b/libs/community/langchain_community/tools/azure_ai_services/face_analysis.py @@ -0,0 +1,235 @@ +from __future__ import annotations + +import logging +from typing import Any, Dict, List, Optional + +from langchain_core.callbacks import CallbackManagerForToolRun +from langchain_core.tools import BaseTool +from langchain_core.utils import get_from_dict_or_env +from pydantic import model_validator + +from langchain_community.tools.azure_ai_services.utils import ( + detect_file_src_type, +) + +logger = logging.getLogger(__name__) + + +class AzureAIFaceAnalysisTool(BaseTool): + """ + A wrapper for the Azure AI Face API. + + This tool facilitates interaction with the Azure AI Face Analysis API, + enabling developers to analyze images for facial features and + recognition attributes. The analysis can be performed on local image files, + with configurable attributes and models for both detection and recognition. + + Attributes: + azure_ai_face_key (Optional[str]): + The API key for Azure AI Face Analysis. + azure_ai_face_endpoint (Optional[str]): + The endpoint URL for Azure AI Face Analysis. + face_attributes (Any): + The facial attributes to be return by the model. + Can be in the form of a list of strings and/or + azure.ai.vision.face.models.FaceAttributeTypeDetection03 + or azure.ai.vision.face.models.FaceAttributeTypeRecognition04 + e.g [FaceAttributeTypeDetection03['HEADPOSE'], "QUALITYFORRECOGNITION"] + detection_model (Any): + The detection model to use (default: 'DETECTION_03'). + recognition_model (Any): + The recognition model to use (default: 'RECOGNITION_04'). + face_id_time_to_live (int): + Time to live for face ID (default: 120 seconds). + return_recognition_model (bool): + Whether to return recognition model details (default: True). + return_face_landmarks (bool): + Whether to return face landmarks (default: True). + return_face_id (bool): + Whether to return face IDs (default: True). + """ + + azure_ai_face_key: Optional[str] = None #: :meta private: + azure_ai_face_endpoint: Optional[str] = None #: :meta private: + face_client: Any = None #: :meta private: + + detection_model: Any = "DETECTION_03" + recognition_model: Any = "RECOGNITION_04" + face_attributes: Any = None + + face_id_time_to_live: int = 120 + return_recognition_model: bool = True + return_face_landmarks: bool = True + return_face_id: bool = True + + name: str = "azure_ai_face_analysis" + description: str = ( + "A wrapper around Azure AI Face API. " + "Useful for when you need to analyze images with potential faces/people" + "Input must be a local path or remote url to an image." + ) + + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: + """Validate that api key and endpoint exists in environment.""" + azure_ai_face_key = get_from_dict_or_env( + values, "azure_ai_face_key", "AZURE_AI_FACE_KEY" + ) + + azure_ai_face_endpoint = get_from_dict_or_env( + values, "azure_ai_face_endpoint", "AZURE_AI_FACE_ENDPOINT" + ) + + """Validate that azure-ai-vision-face is installed.""" + try: + from azure.ai.vision.face import FaceClient + from azure.ai.vision.face.models import ( + FaceDetectionModel, + FaceRecognitionModel, + ) + from azure.core.credentials import AzureKeyCredential + except ImportError: + raise ImportError( + "azure.ai.vision.face is not installed. " + "Run `pip install azure-ai-vision-face` to install. " + ) + + """Validate Azure AI Vision Face Analysis client can be initialized.""" + try: + values["face_client"] = FaceClient( + endpoint=azure_ai_face_endpoint, + credential=AzureKeyCredential(azure_ai_face_key), + ) + except Exception as e: + raise RuntimeError( + f"Initialization of Azure AI Face Analysis client failed: {e}" + ) + + rec_model = values.get("recognition_model", "RECOGNITION_04") + det_model = values.get("detection_model", "DETECTION_03") + values["recognition_model"] = FaceRecognitionModel[rec_model] + values["detection_model"] = FaceDetectionModel[det_model] + + attributes = values.get( + "return_face_attributes", ["HEADPOSE", "QUALITYFORRECOGNITION"] + ) + values["face_attributes"] = attributes + + values["face_id_time_to_live"] = values.get("face_id_time_to_live", 120) + values["return_recognition_model"] = values.get( + "return_recognition_model", True + ) + values["return_face_landmarks"] = values.get("return_face_landmarks", True) + values["return_face_id"] = values.get("return_face_id", True) + + return values + + def _face_analysis(self, image_path: str) -> List: + """ + Analyze an image using Azure AI Face Analysis. + + This method performs facial detection and recognition on the given + image using the configured attributes, detection model, and recognition model. + + Args: + image_path (str): Path to a local image or url to a remote image. + + Returns: + List: A list containing the results of the face analysis, + including detected attributes. + """ + + from azure.ai.vision.face import FaceClient + from azure.ai.vision.face.models import ( + FaceDetectionModel, + FaceRecognitionModel, + ) + + self.face_client: FaceClient + self.formatted_attributes: List + self.detection_model: FaceDetectionModel + self.recognition_model: FaceRecognitionModel + + image_src_type = detect_file_src_type(image_path) + if image_src_type == "local": + with open(image_path, "rb") as image_file: + image_data = image_file.read() + result = self.face_client.detect( + image_data, + detection_model=self.detection_model, + recognition_model=self.recognition_model, + return_face_attributes=self.face_attributes, + return_face_id=self.return_face_id, + return_face_landmarks=self.return_face_landmarks, + return_recognition_model=self.return_recognition_model, + face_id_time_to_live=self.face_id_time_to_live, + ) + elif image_src_type == "remote": + result = self.face_client.detect_from_url( + url=image_path, + detection_model=self.detection_model, + recognition_model=self.recognition_model, + return_face_attributes=self.face_attributes, + return_face_id=self.return_face_id, + return_face_landmarks=self.return_face_landmarks, + return_recognition_model=self.return_recognition_model, + face_id_time_to_live=self.face_id_time_to_live, + ) + else: + raise ValueError(f"Invalid image path: {image_path}") + + return result + + def _format_face_analysis_result(self, result: List) -> str: + """ + Format the results from the face analysis into a readable string. + + This method takes the analysis results and formats them into a human-readable + string that includes information such as face attributes and + recognition details. + + Args: + result (List): The results of the face analysis. + + Returns: + str: A formatted string containing the face analysis results. If no + faces are detected, returns "No faces found". + """ + if not result: + return "No faces found" + + formatted: str = "" + for idx, face in enumerate(result): + formatted += f"FACE: {idx+1}\n" + for k, v in face.items(): + formatted += f"{k}: {v}\n" + return formatted + + def _run( + self, + query: str, + run_manager: Optional[CallbackManagerForToolRun] = None, + ) -> str: + """ + Execute the face analysis tool. + + This method runs the face analysis based on the provided query and returns + a formatted result. + + Args: + query (str): A local path to the image to be analyzed. + run_manager (Optional[CallbackManagerForToolRun]): + Optional callback manager for managing the run. + + Returns: + str: The formatted face analysis result. + + Raises: + RuntimeError: If an error occurs while running the analysis. + """ + try: + image_analysis_result = self._face_analysis(query) + return self._format_face_analysis_result(image_analysis_result) + except Exception as e: + raise 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b/libs/community/tests/unit_tests/tools/azure_ai_services/test_face_analysis.py @@ -0,0 +1,79 @@ +"""Tests for the Azure AI Face Analysis Tool.""" + +from pathlib import Path +from typing import Any + +import pytest + +from langchain_community.tools.azure_ai_services.face_analysis import ( + AzureAIFaceAnalysisTool, +) + +_THIS_DIR = Path(__file__).parents[3] + +_EXAMPLES_DIR = _THIS_DIR / "examples" +FACE = _EXAMPLES_DIR / "face.jpg" +NO_FACE = _EXAMPLES_DIR / "no_face.jpg" + + +@pytest.mark.requires("azure.ai.vision.face") +def test_face_analysis(mocker: Any) -> None: + mocker.patch("azure.ai.vision.face.FaceClient", autospec=True) + mocker.patch("azure.core.credentials.AzureKeyCredential", autospec=True) + + key = "key" + endpoint = "endpoint" + + tool = AzureAIFaceAnalysisTool( + azure_ai_face_key=key, azure_ai_face_endpoint=endpoint + ) + assert tool.azure_ai_face_key == key + assert tool.azure_ai_face_endpoint == endpoint + + +@pytest.mark.requires("azure.ai.vision.face") +def test_faces_detected(mocker: Any) -> None: + key = "key" + endpoint = "endpoint" + + mocker.patch("azure.ai.vision.face.FaceClient", autospec=True) + mocker.patch("azure.core.credentials.AzureKeyCredential", autospec=True) + tool = AzureAIFaceAnalysisTool( + azure_ai_face_key=key, azure_ai_face_endpoint=endpoint + ) + + mock_content_client = mocker.Mock() + mock_content_client.detect.return_value = [ + {"faceId": "69d017e8-464e-42c1-adfb-5b7fec0526ea"} + ] + + tool.face_client = mock_content_client + + input: str = str(FACE) + output = "FACE: 1\nfaceId: 69d017e8-464e-42c1-adfb-5b7fec0526ea\n" + + result = tool._run(input) + assert result == output + + +@pytest.mark.requires("azure.ai.vision.face") +def test_no_faces_detected(mocker: Any) -> None: + key = "key" + endpoint = "endpoint" + + mocker.patch("azure.ai.vision.face.FaceClient", autospec=True) + mocker.patch("azure.core.credentials.AzureKeyCredential", autospec=True) + tool = AzureAIFaceAnalysisTool( + azure_ai_face_key=key, azure_ai_face_endpoint=endpoint + ) + + mock_content_client = mocker.Mock() + mock_content_client.detect.return_value = [] + + tool.face_client = mock_content_client + + input: str = str(NO_FACE) + output = "No faces found" + + result = tool._run(input) + assert result == output

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