Add vision capability

.github/workflows/contrib-tests.yml

@@ -247,7 +247,7 @@ jobs:
       - name: Coverage
         run: |
           pip install coverage>=5.3
-          coverage run -a -m pytest test/agentchat/contrib/test_img_utils.py test/agentchat/contrib/test_lmm.py test/agentchat/contrib/test_llava.py test/agentchat/contrib/capabilities/test_image_generation_capability.py --skip-openai
+          coverage run -a -m pytest test/agentchat/contrib/test_img_utils.py test/agentchat/contrib/test_lmm.py test/agentchat/contrib/test_llava.py test/agentchat/contrib/capabilities/test_image_generation_capability.py test/agentchat/contrib/capabilities/test_vision_capability.py --skip-openai
           coverage xml
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v3

autogen/agentchat/contrib/capabilities/vision_capability.py

@@ -0,0 +1,211 @@
+import copy
+from typing import Callable, Dict, List, Optional, Union
+
+from autogen.agentchat.assistant_agent import ConversableAgent
+from autogen.agentchat.contrib.capabilities.agent_capability import AgentCapability
+from autogen.agentchat.contrib.img_utils import (
+    convert_base64_to_data_uri,
+    get_image_data,
+    get_pil_image,
+    gpt4v_formatter,
+    message_formatter_pil_to_b64,
+)
+from autogen.agentchat.contrib.multimodal_conversable_agent import MultimodalConversableAgent
+from autogen.agentchat.conversable_agent import colored
+from autogen.code_utils import content_str
+from autogen.oai.client import OpenAIWrapper
+
+DEFAULT_DESCRIPTION_PROMPT = (
+    "Write a detailed caption for this image. "
+    "Pay special attention to any details that might be useful or relevant "
+    "to the ongoing conversation."
+)
+
+
+class VisionCapability(AgentCapability):
+    """We can add vision capability to regular ConversableAgent, even if the agent does not have the multimodal capability,
+    such as GPT-3.5-turbo agent, Llama, Orca, or Mistral agents. This vision capability will invoke a LMM client to describe
+    the image (captioning) before sending the information to the agent's actual client.
+
+        The vision capability will hook to the ConversableAgent's `process_last_received_message`.
+
+        Some technical details:
+        When the agent (who has the vision capability) received an message, it will:
+        1. _process_received_message:
+            a. _append_oai_message
+        2. generate_reply: if the agent is a MultimodalAgent, it will also use the image tag.
+            a. hook process_last_received_message (NOTE: this is where the vision capability will be hooked to.)
+            b. hook process_all_messages_before_reply
+        3. send:
+            a. hook process_message_before_send
+            b. _append_oai_message
+    """
+
+    def __init__(
+        self,
+        lmm_config: Dict,
+        description_prompt: Optional[str] = DEFAULT_DESCRIPTION_PROMPT,
+        custom_caption_func: Callable = None,
+    ) -> None:
+        """
+        Initializes a new instance, setting up the configuration for interacting with
+        a Language Multimodal (LMM) client and specifying optional parameters for image
+        description and captioning.
+
+        Args:
+            lmm_config (Dict): Configuration for the LMM client, which is used to call
+                the LMM service for describing the image. This must be a dictionary containing
+                the necessary configuration parameters. If `lmm_config` is False or an empty dictionary,
+                it is considered invalid, and initialization will assert.
+            description_prompt (Optional[str], optional): The prompt to use for generating
+                descriptions of the image. This parameter allows customization of the
+                prompt passed to the LMM service. Defaults to `DEFAULT_DESCRIPTION_PROMPT` if not provided.
+            custom_caption_func (Callable, optional): A callable that, if provided, will be used
+                to generate captions for images. This allows for custom captioning logic outside
+                of the standard LMM service interaction.
+                The callable should take three parameters as input:
+                    1. an image URL (or local location)
+                    2. image_data (a PIL image)
+                    3. lmm_client (to call remote LMM)
+                and then return a description (as string).
+                If not provided, captioning will rely on the LMM client configured via `lmm_config`.
+                If provided, we will not run the default self._get_image_caption method.
+
+        Raises:
+            AssertionError: If neither a valid `lmm_config` nor a `custom_caption_func` is provided,
+                an AssertionError is raised to indicate that the Vision Capability requires
+                one of these to be valid for operation.
+        """
+        self._lmm_config = lmm_config
+        self._description_prompt = description_prompt
+        self._parent_agent = None
+
+        if lmm_config:
+            self._lmm_client = OpenAIWrapper(**lmm_config)
+        else:
+            self._lmm_client = None
+
+        self._custom_caption_func = custom_caption_func
+        assert (
+            self._lmm_config or custom_caption_func
+        ), "Vision Capability requires a valid lmm_config or custom_caption_func."
+
+    def add_to_agent(self, agent: ConversableAgent) -> None:
+        self._parent_agent = agent
+
+        # Append extra info to the system message.
+        agent.update_system_message(agent.system_message + "\nYou've been given the ability to interpret images.")
+
+        # Register a hook for processing the last message.
+        agent.register_hook(hookable_method="process_last_received_message", hook=self.process_last_received_message)
+
+    def process_last_received_message(self, content: Union[str, List[dict]]) -> str:
+        """
+        Processes the last received message content by normalizing and augmenting it
+        with descriptions of any included images. The function supports input content
+        as either a string or a list of dictionaries, where each dictionary represents
+        a content item (e.g., text, image). If the content contains image URLs, it
+        fetches the image data, generates a caption for each image, and inserts the
+        caption into the augmented content.
+
+        The function aims to transform the content into a format compatible with GPT-4V
+        multimodal inputs, specifically by formatting strings into PIL-compatible
+        images if needed and appending text descriptions for images. This allows for
+        a more accessible presentation of the content, especially in contexts where
+        images cannot be displayed directly.
+
+        Args:
+            content (Union[str, List[dict]]): The last received message content, which
+                can be a plain text string or a list of dictionaries representing
+                different types of content items (e.g., text, image_url).
+
+        Returns:
+            str: The augmented message content
+
+        Raises:
+            AssertionError: If an item in the content list is not a dictionary.
+
+        Examples:
+            Assuming `self._get_image_caption(img_data)` returns
+            "A beautiful sunset over the mountains" for the image.
+
+        - Input as String:
+            content = "Check out this cool photo!"
+            Output: "Check out this cool photo!"
+            (Content is a string without an image, remains unchanged.)
+
+        - Input as String, with image location:
+            content = "What's weather in this cool photo: <img http://example.com/photo.jpg>"
+            Output: "What's weather in this cool photo: <img http://example.com/photo.jpg> in case you can not see, the caption of this image is:
+            A beautiful sunset over the mountains\n"
+            (Caption added after the image)
+
+        - Input as List with Text Only:
+            content = [{"type": "text", "text": "Here's an interesting fact."}]
+            Output: "Here's an interesting fact."
+            (No images in the content, it remains unchanged.)
+
+        - Input as List with Image URL:
+            content = [
+                {"type": "text", "text": "What's weather in this cool photo:"},
+                {"type": "image_url", "image_url": {"url": "http://example.com/photo.jpg"}}
+            ]
+            Output: "What's weather in this cool photo: <img http://example.com/photo.jpg> in case you can not see, the caption of this image is:
+            A beautiful sunset over the mountains\n"
+            (Caption added after the image)
+        """
+        copy.deepcopy(content)
+        # normalize the content into the gpt-4v format for multimodal
+        # we want to keep the URL format to keep it concise.
+        if isinstance(content, str):
+            content = gpt4v_formatter(content, img_format="url")
+
+        aug_content: str = ""
+        for item in content:
+            assert isinstance(item, dict)
+            if item["type"] == "text":
+                aug_content += item["text"]
+            elif item["type"] == "image_url":
+                img_url = item["image_url"]["url"]
+                img_caption = ""
+
+                if self._custom_caption_func:
+                    img_caption = self._custom_caption_func(img_url, get_pil_image(img_url), self._lmm_client)
+                elif self._lmm_client:
+                    img_data = get_image_data(img_url)
+                    img_caption = self._get_image_caption(img_data)
+                else:
+                    img_caption = ""
+
+                aug_content += f"<img {img_url}> in case you can not see, the caption of this image is: {img_caption}\n"
+            else:
+                print(f"Warning: the input type should either be `test` or `image_url`. Skip {item['type']} here.")
+
+        return aug_content
+
+    def _get_image_caption(self, img_data: str) -> str:
+        """
+        Args:
+            img_data (str): base64 encoded image data.
+        Returns:
+            str: caption for the given image.
+        """
+        response = self._lmm_client.create(
+            context=None,
+            messages=[
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": self._description_prompt},
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": convert_base64_to_data_uri(img_data),
+                            },
+                        },
+                    ],
+                }
+            ],
+        )
+        description = response.choices[0].message.content
+        return content_str(description)

autogen/agentchat/contrib/img_utils.py

@@ -24,6 +24,12 @@ def get_pil_image(image_file: Union[str, Image.Image]) -> Image.Image:
         # Already a PIL Image object
         return image_file
 
+    # Remove quotes if existed
+    if image_file.startswith('"') and image_file.endswith('"'):
+        image_file = image_file[1:-1]
+    if image_file.startswith("'") and image_file.endswith("'"):
+        image_file = image_file[1:-1]
+
     if image_file.startswith("http://") or image_file.startswith("https://"):
         # A URL file
         response = requests.get(image_file)

autogen/agentchat/conversable_agent.py

@@ -5,37 +5,35 @@
 import json
 import logging
 import re
+import warnings
 from collections import defaultdict
 from functools import partial
 from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Type, TypeVar, Union
-import warnings
+
 from openai import BadRequestError
 
 from autogen.exception_utils import InvalidCarryOverType, SenderRequired
 
-from ..coding.base import CodeExecutor
-from ..coding.factory import CodeExecutorFactory
-from ..formatting_utils import colored
-
-from ..oai.client import OpenAIWrapper, ModelClient
-from ..runtime_logging import logging_enabled, log_new_agent
+from .._pydantic import model_dump
 from ..cache.cache import Cache
 from ..code_utils import (
     UNKNOWN,
-    content_str,
     check_can_use_docker_or_throw,
+    content_str,
     decide_use_docker,
     execute_code,
     extract_code,
     infer_lang,
 )
-from .utils import gather_usage_summary, consolidate_chat_info
-from .chat import ChatResult, initiate_chats, a_initiate_chats
-
-
+from ..coding.base import CodeExecutor
+from ..coding.factory import CodeExecutorFactory
+from ..formatting_utils import colored
 from ..function_utils import get_function_schema, load_basemodels_if_needed, serialize_to_str
+from ..oai.client import ModelClient, OpenAIWrapper
+from ..runtime_logging import log_new_agent, logging_enabled
 from .agent import Agent, LLMAgent
-from .._pydantic import model_dump
+from .chat import ChatResult, a_initiate_chats, initiate_chats
+from .utils import consolidate_chat_info, gather_usage_summary
 
 __all__ = ("ConversableAgent",)
 
@@ -2603,22 +2601,25 @@ def process_last_received_message(self, messages):
             return messages  # Last message contains a context key.
         if "content" not in last_message:
             return messages  # Last message has no content.
-        user_text = last_message["content"]
-        if not isinstance(user_text, str):
-            return messages  # Last message content is not a string. TODO: Multimodal agents will use a dict here.
-        if user_text == "exit":
+
+        user_content = last_message["content"]
+        if not isinstance(user_content, str) and not isinstance(user_content, list):
+            # if the user_content is a string, it is for regular LLM
+            # if the user_content is a list, it should follow the multimodal LMM format.
+            return messages
+        if user_content == "exit":
             return messages  # Last message is an exit command.
 
         # Call each hook (in order of registration) to process the user's message.
-        processed_user_text = user_text
+        processed_user_content = user_content
         for hook in hook_list:
-            processed_user_text = hook(processed_user_text)
-        if processed_user_text == user_text:
+            processed_user_content = hook(processed_user_content)
+        if processed_user_content == user_content:
             return messages  # No hooks actually modified the user's message.
 
         # Replace the last user message with the expanded one.
         messages = messages.copy()
-        messages[-1]["content"] = processed_user_text
+        messages[-1]["content"] = processed_user_content
         return messages
 
     def print_usage_summary(self, mode: Union[str, List[str]] = ["actual", "total"]) -> None: