feat(VisualReplayStrategy): adapters: ultralytics, som, anthropic, go…

…ogle; remove_move_before_click; vision.py

* add prompts/, adapters/openai.py, strategies/visual.py (wip)

* adapters.anthropic

* add anthropic.py

* prompt with active segment descriptions

* Set-of-Mark Prompting Adapter (#612)

* Update openadapt/config.py

* remove_move_before_click

* started_counter; adapters.ultralytics

* add vision.py

* add openadapt/adapters/google.py

* filter_masks_by_size

* documentation

* update README

* add ultralytics

* exclude alembic in black/flake8

* exclude .venv in black/flake8

* disable som adapter; remove logging

* add adapters.google

---------

Co-authored-by: Cody DeVilliers <[email protected]>

.flake8

@@ -4,4 +4,4 @@ exclude =
     .venv
 docstring-convention = google
 max-line-length = 88
-extend-ignore = ANN101
+extend-ignore = ANN101, E203

.github/workflows/main.yml

@@ -25,6 +25,9 @@ jobs:
     steps:
       - name: Checkout code
         uses: actions/checkout@v3
+        with:
+          ref: ${{ env.BRANCH }}
+          repository: ${{ env.REPO }}
 
       - name: Set up Python
         uses: actions/setup-python@v3
@@ -35,12 +38,6 @@ jobs:
         if: matrix.os == 'macos-latest'
         run: sh install/install_openadapt.sh
 
-      - name: Checkout code
-        uses: actions/checkout@v3
-        with:
-          ref: ${{ env.BRANCH }}
-          repository: ${{ env.REPO }}
-
       - name: Install poetry
         uses: snok/install-poetry@v1
         with:
@@ -63,7 +60,7 @@ jobs:
         if: steps.cache-deps.outputs.cache-hit == 'true'
 
       - name: Check formatting with Black
-        run: poetry run black --preview --check .
+        run: poetry run black --preview --check . --exclude '/(alembic|\.venv)/'
 
       - name: Run Flake8
-        run: poetry run flake8
+        run: poetry run flake8 --exclude=alembic,.venv

README.md

@@ -189,6 +189,11 @@ python -m openadapt.replay NaiveReplayStrategy
 Other replay strategies include:
 
 - [`StatefulReplayStrategy`](https://github.com/OpenAdaptAI/OpenAdapt/blob/main/openadapt/strategies/stateful.py): Proof-of-concept which uses the OpenAI GPT-4 API with prompts constructed via OS-level window data.
+- [`VisualReplayStrategy`](https://github.com/OpenAdaptAI/OpenAdapt/blob/main/openadapt/strategies/visual.py): Uses [Fast Segment Anything Model (FastSAM)](https://github.com/CASIA-IVA-Lab/FastSAM) to segment active window. Accepts an "instructions" parameter that is used to modify the recording, e.g.:
+
+```
+python -m openadapt.replay VisualReplayStrategy --instructions "Multiply 9x5 instead of 6x8"
+```
 
 See https://github.com/OpenAdaptAI/OpenAdapt/tree/main/openadapt/strategies for a complete list. More ReplayStrategies coming soon! (see [Contributing](#Contributing)).
 

alembic/versions/30a5ba9d6453_add_active_segment_description_and_.py

@@ -0,0 +1,34 @@
+"""add active_segment_description and available_segment_descriptions
+
+Revision ID: 30a5ba9d6453
+Revises: 530f0663324e
+Create Date: 2024-04-05 12:02:57.843244
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = '30a5ba9d6453'
+down_revision = '530f0663324e'
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table('action_event', schema=None) as batch_op:
+        batch_op.add_column(sa.Column('active_segment_description', sa.String(), nullable=True))
+        batch_op.add_column(sa.Column('available_segment_descriptions', sa.String(), nullable=True))
+
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table('action_event', schema=None) as batch_op:
+        batch_op.drop_column('available_segment_descriptions')
+        batch_op.drop_column('active_segment_description')
+
+    # ### end Alembic commands ###

openadapt/adapters/__init__.py

@@ -0,0 +1,40 @@
+"""Adapters for completion and segmentation."""
+
+from types import ModuleType
+
+from openadapt import config
+from . import anthropic
+from . import openai
+from . import replicate
+from . import som
+from . import ultralytics
+from . import google
+
+
+def get_default_prompt_adapter() -> ModuleType:
+    """Returns the default prompt adapter module.
+
+    Returns:
+        The module corresponding to the default prompt adapter.
+    """
+    return {
+        "openai": openai,
+        "anthropic": anthropic,
+        "google": google,
+    }[config.DEFAULT_ADAPTER]
+
+
+def get_default_segmentation_adapter() -> ModuleType:
+    """Returns the default image segmentation adapter module.
+
+    Returns:
+        The module corresponding to the default segmentation adapter.
+    """
+    return {
+        "som": som,
+        "replicate": replicate,
+        "ultralytics": ultralytics,
+    }[config.DEFAULT_SEGMENTATION_ADAPTER]
+
+
+__all__ = ["anthropic", "openai", "replicate", "som", "ultralytics", "google"]

openadapt/adapters/anthropic.py

@@ -0,0 +1,146 @@
+"""Adapter for Anthropic API with vision support."""
+
+from pprint import pprint
+
+from loguru import logger
+import anthropic
+
+from openadapt import cache, config
+
+
+MAX_TOKENS = 4096
+# from https://docs.anthropic.com/claude/docs/vision
+MAX_IMAGES = 20
+MODEL_NAME = "claude-3-opus-20240229"
+
+
+@cache.cache()
+def create_payload(
+    prompt: str,
+    system_prompt: str | None = None,
+    base64_images: list[tuple[str, str]] | None = None,
+    model: str = MODEL_NAME,
+    max_tokens: int | None = None,
+) -> dict:
+    """Creates the payload for the Anthropic API request with image support."""
+    messages = []
+
+    user_message_content = []
+
+    max_tokens = max_tokens or MAX_TOKENS
+    if max_tokens > MAX_TOKENS:
+        logger.warning(f"{max_tokens=} > {MAX_TOKENS=}")
+        max_tokens = MAX_TOKENS
+
+    # Add base64 encoded images to the user message content
+    if base64_images:
+        for image_data in base64_images:
+            # Extract media type and base64 data
+            media_type, base64_str = image_data.split(";base64,", 1)
+            media_type = media_type.split(":")[-1]  # Remove 'data:' prefix
+
+            user_message_content.append(
+                {
+                    "type": "image",
+                    "source": {
+                        "type": "base64",
+                        "media_type": media_type,
+                        "data": base64_str,
+                    },
+                }
+            )
+
+    # Add text prompt
+    user_message_content.append(
+        {
+            "type": "text",
+            "text": prompt,
+        }
+    )
+
+    # Construct user message
+    messages.append(
+        {
+            "role": "user",
+            "content": user_message_content,
+        }
+    )
+
+    # Prepare the full payload
+    payload = {
+        "model": model,
+        "max_tokens": max_tokens,
+        "messages": messages,
+    }
+
+    # Add system_prompt as a top-level parameter if provided
+    if system_prompt:
+        payload["system"] = system_prompt
+
+    return payload
+
+
+client = anthropic.Anthropic(api_key=config.ANTHROPIC_API_KEY)
+
+
+@cache.cache()
+def get_completion(payload: dict) -> str:
+    """Sends a request to the Anthropic API and returns the response."""
+    try:
+        response = client.messages.create(**payload)
+    except Exception as exc:
+        logger.exception(exc)
+        import ipdb
+
+        ipdb.set_trace()
+    """
+    Message(
+        id='msg_01L55ai2A9q92687mmjMSch3',
+        content=[
+            ContentBlock(
+                text='{
+                    "action": [
+                        {
+                            "name": "press",
+                            "key_name": "cmd",
+                            "canonical_key_name": "cmd"
+                        },
+                        ...
+                    ]
+                }',
+                type='text'
+            )
+        ],
+        model='claude-3-opus-20240229',
+        role='assistant',
+        stop_reason='end_turn',
+        stop_sequence=None,
+        type='message',
+        usage=Usage(input_tokens=4379, output_tokens=109))
+    """
+    texts = [content_block.text for content_block in response.content]
+    return "\n".join(texts)
+
+
+def prompt(
+    prompt: str,
+    system_prompt: str | None = None,
+    base64_images: list[str] | None = None,
+    max_tokens: int | None = None,
+) -> str:
+    """Public method to get a response from the Anthropic API with image support."""
+    if len(base64_images) > MAX_IMAGES:
+        # XXX TODO handle this
+        raise Exception(
+            f"{len(base64_images)=} > {MAX_IMAGES=}. Use a different adapter."
+        )
+    payload = create_payload(
+        prompt,
+        system_prompt,
+        base64_images,
+        max_tokens=max_tokens,
+    )
+    # pprint(f"payload=\n{payload}")  # Log payload for debugging
+    result = get_completion(payload)
+    pprint(f"result=\n{result}")  # Log result for debugging
+    return result

openadapt/adapters/google.py

@@ -0,0 +1,74 @@
+"""Adapter for Google Gemini.
+
+See https://ai.google.dev/tutorials/python_quickstart for documentation.
+"""
+
+from pprint import pprint
+
+from PIL import Image
+import fire
+import google.generativeai as genai
+
+from openadapt import cache, config, utils
+
+
+MAX_TOKENS = 2**20  # 1048576
+MODEL_NAME = [
+    "gemini-pro-vision",
+    "models/gemini-1.5-pro-latest",
+][-1]
+# https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/send-multimodal-prompts
+MAX_IMAGES = {
+    "gemini-pro-vision": 16,
+    "models/gemini-1.5-pro-latest": 3000,
+}[MODEL_NAME]
+
+
+@cache.cache()
+def prompt(
+    prompt: str,
+    system_prompt: str | None = None,
+    base64_images: list[str] | None = None,
+    # max_tokens: int | None = None,
+    model_name: str = MODEL_NAME,
+) -> str:
+    """Public method to get a response from the Google API with image support."""
+    full_prompt = "\n\n###\n\n".join([s for s in (system_prompt, prompt) if s])
+    # HACK
+    full_prompt += "\nWhen responding in JSON, you MUST use double quotes around keys."
+
+    # TODO: modify API across all adapters to accept PIL.Image
+    images = (
+        [utils.utf82image(base64_image) for base64_image in base64_images]
+        if base64_images
+        else []
+    )
+
+    genai.configure(api_key=config.GOOGLE_API_KEY)
+    model = genai.GenerativeModel(model_name)
+    response = model.generate_content([full_prompt] + images)
+    response.resolve()
+    pprint(f"response=\n{response}")  # Log response for debugging
+    return response.text
+
+
+def main(text: str, image_path: str | None = None) -> None:
+    """Prompt Google Gemini with text and a path to an image."""
+    if image_path:
+        with Image.open(image_path) as img:
+            # Convert image to RGB if it's RGBA (to remove alpha channel)
+            if img.mode in ("RGBA", "LA") or (
+                img.mode == "P" and "transparency" in img.info
+            ):
+                img = img.convert("RGB")
+            base64_image = utils.image2utf8(img)
+    else:
+        base64_image = None
+
+    base64_images = [base64_image] if base64_image else None
+    output = prompt(text, base64_images=base64_images)
+    print(output)
+
+
+if __name__ == "__main__":
+    fire.Fire(main)