chore: import upstream snapshot with attribution

2026-07-13 13:21:23 +08:00
commit b957a53def
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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from samples.concepts.setup.chat_completion_services import (
+    Services,
+    get_chat_completion_service_and_request_settings,
+)
+from semantic_kernel.contents import AuthorRole, ChatHistory, ChatMessageContent, ImageContent, TextContent
+
+# This sample shows how to create a chatbot that responds to user messages with image input.
+# This sample uses the following three main components:
+# - a ChatCompletionService: This component is responsible for generating responses to user messages.
+# - a ChatHistory: This component is responsible for keeping track of the chat history.
+# - an ImageContent: This component is responsible for representing image content.
+# The chatbot in this sample is called Mosscap.
+
+# You can select from the following chat completion services:
+# - Services.OPENAI
+# - Services.AZURE_OPENAI
+# - Services.AZURE_AI_INFERENCE
+# - Services.ANTHROPIC
+# - Services.BEDROCK
+# - Services.GOOGLE_AI
+# - Services.MISTRAL_AI
+# - Services.OLLAMA
+# - Services.ONNX
+# - Services.VERTEX_AI
+# Please make sure you have configured your environment correctly for the selected chat completion service.
+
+# [NOTE]
+# Not all models support image input. Make sure to select a model that supports image input.
+# Not all services support image input from an image URI. If your image is saved in a remote location,
+# make sure to use a service that supports image input from a URI.
+chat_completion_service, request_settings = get_chat_completion_service_and_request_settings(Services.AZURE_OPENAI)
+
+IMAGE_URI = "https://raw.githubusercontent.com/microsoft/semantic-kernel/main/python/tests/assets/sample_image.jpg"
+IMAGE_PATH = "samples/concepts/resources/sample_image.jpg"
+
+# Create an image content with the image URI.
+image_content_remote = ImageContent(uri=IMAGE_URI)
+# You can also create an image content with a local image path.
+image_content_local = ImageContent.from_image_file(IMAGE_PATH)
+
+
+# This is the system message that gives the chatbot its personality.
+system_message = """
+You are an image reviewing chat bot. Your name is Mosscap and you have one goal critiquing images that are supplied.
+"""
+
+# Create a chat history object with the system message and an initial user message with an image input.
+chat_history = ChatHistory(system_message=system_message)
+chat_history.add_message(
+    ChatMessageContent(
+        role=AuthorRole.USER,
+        items=[TextContent(text="What is in this image?"), image_content_local],
+    )
+)
+
+
+async def chat(skip_user_input: bool = False) -> bool:
+    """Chat with the chatbot.
+
+    Args:
+        skip_user_input (bool): Whether to skip user input. Defaults to False.
+    """
+    if not skip_user_input:
+        try:
+            user_input = input("User:> ")
+        except KeyboardInterrupt:
+            print("\n\nExiting chat...")
+            return False
+        except EOFError:
+            print("\n\nExiting chat...")
+            return False
+
+        if user_input == "exit":
+            print("\n\nExiting chat...")
+            return False
+
+        # Add the user message to the chat history so that the chatbot can respond to it.
+        chat_history.add_user_message(user_input)
+
+    # Get the chat message content from the chat completion service.
+    response = await chat_completion_service.get_chat_message_content(
+        chat_history=chat_history,
+        settings=request_settings,
+    )
+    if response:
+        print(f"Mosscap:> {response}")
+
+        # Add the chat message to the chat history to keep track of the conversation.
+        chat_history.add_message(response)
+
+    return True
+
+
+async def main() -> None:
+    # Start the chat with the image input.
+    await chat(skip_user_input=True)
+    # Continue the chat. The chat loop will continue until the user types "exit".
+    chatting = True
+    while chatting:
+        chatting = await chat()
+
+    # Sample output:
+    # Mosscap:> The image features a large, historic building that exhibits a traditional half-timbered architectural
+    #           style. The structure is located near a dense forest, characterized by lush green trees. The sky above
+    #           is partly cloudy, suggesting a pleasant day. The building itself appears well-maintained, with distinct
+    #           features such as a turret or spire and decorative wood framing, creating an elegant and charming
+    #           appearance in its natural setting.
+    # User:> What do you think about the composition of the photo?
+    # Mosscap:> The composition of the photo is quite effective. Here are a few observations:
+    #           1. **Framing**: The building is positioned slightly off-center, which can create a more dynamic and
+    #           engaging image. This drawing of attention to the structure, while still showcasing the surrounding
+    #           landscape.
+    #           2. **Foreground and Background**: The green foliage and trees in the foreground provide a nice contrast
+    #           to the building, enhancing its visual appeal. The dense forest in the background adds depth and context
+    #           to the scene.
+    #           3. **Lighting**: The light appears to be favorable, suggesting a well-lit scene. The clouds add texture
+    #           to the sky without overwhelming the overall brightness.
+    #           4. **Perspective**: The angle from which the photo is taken allows viewers to appreciate both the
+    #           architecture of the building and its natural environment, creating a harmonious balance.
+    #           Overall, the composition successfully highlights the building while incorporating its natural
+    #           surroundings, inviting viewers to appreciate both elements together.
+
+
+if __name__ == "__main__":
+    asyncio.run(main())