Clean dependency

.github/actions/crab_install/action.yml

@@ -26,7 +26,7 @@ runs:
         path: ./.venv
         key: venv-${{ hashFiles('poetry.lock') }}
     - name: Install the project dependencies
-      run: poetry install -E visual-prompt -E server
+      run: poetry install -E client -E server
       shell: bash
     - uses: actions/cache/save@v3
       name: Save caches based on poetry.lock 

README.md

@@ -26,7 +26,7 @@ Crab is a framework for building LLM agent benchmark environments in a Python-ce
 - pip
 
 ```bash
-pip install crab-framework[visual-prompt]
+pip install crab-framework[client]
 ```
 
 ## Examples

crab-benchmark-v0/docs/environment_local_setup.md

@@ -8,7 +8,7 @@ Then pull the crab repo and install:
 git clone https://github.com/camel-ai/crab
 
 cd crab
-poetry install -E visual-prompt
+poetry install -E client
 ```
 
 ## Install Ubuntu VM
@@ -22,7 +22,8 @@ After install Ubuntu, you should install crab-server on it and do necessary init
 ```bash
 git clone https://github.com/camel-ai/crab.git ~/crab/
 cd ~/crab/crab-benchmark-v0/scripts
-bash ubuntu_env_init.sh
+chmod +x ubuntu_env_init.sh
+./ubuntu_env_init.sh
 ```
 
 The VM will reboot after initilization. After rebooting, remember its ip address.

crab/agents/backend_models/claude_model.py

@@ -15,12 +15,17 @@
 from time import sleep
 from typing import Any
 
-import anthropic
-from anthropic.types import TextBlock
-from anthropic.types.beta.tools import ToolUseBlock
-
 from crab import Action, ActionOutput, BackendModel, BackendOutput, MessageType
 
+try:
+    import anthropic
+    from anthropic.types import TextBlock
+    from anthropic.types.beta.tools import ToolUseBlock
+
+    anthropic_model_enable = True
+except ImportError:
+    anthropic_model_enable = False
+
 
 class ClaudeModel(BackendModel):
     def __init__(
@@ -29,6 +34,8 @@ def __init__(
         parameters: dict[str, Any] = dict(),
         history_messages_len: int = 0,
     ) -> None:
+        if anthropic_model_enable is False:
+            raise ImportError("Please install anthropic to use ClaudeModel")
         super().__init__(
             model,
             parameters,

crab/agents/backend_models/gemini_model.py

@@ -15,14 +15,19 @@
 from time import sleep
 from typing import Any
 
-import google.generativeai as genai
-from google.ai.generativelanguage_v1beta import FunctionDeclaration, Part, Tool
-from google.api_core.exceptions import ResourceExhausted
-from google.generativeai.types import content_types
-
 from crab import Action, ActionOutput, BackendModel, BackendOutput, MessageType
 from crab.utils.common import base64_to_image, json_expand_refs
 
+try:
+    import google.generativeai as genai
+    from google.ai.generativelanguage_v1beta import FunctionDeclaration, Part, Tool
+    from google.api_core.exceptions import ResourceExhausted
+    from google.generativeai.types import content_types
+
+    gemini_model_enable = True
+except ImportError:
+    gemini_model_enable = False
+
 
 class GeminiModel(BackendModel):
     def __init__(
@@ -31,6 +36,8 @@ def __init__(
         parameters: dict[str, Any] = dict(),
         history_messages_len: int = 0,
     ) -> None:
+        if gemini_model_enable is False:
+            raise ImportError("Please install google.generativeai to use GeminiModel")
         super().__init__(
             model,
             parameters,
@@ -162,7 +169,6 @@ def _clear_schema(cls, schema_dict: dict):
         if "items" in schema_dict:
             cls._clear_schema(schema_dict["items"])
 
-
     @classmethod
     def _action_to_funcdec(cls, action: Action, env: str):
         "Converts crab Action to google FunctionDeclaration"
@@ -174,4 +180,4 @@ def _action_to_funcdec(cls, action: Action, env: str):
             name=action.name + "__in__" + env,
             description="In {} environment, {}".format(env, action.description),
             parameters=p_schema,
-        )
+        )

crab/agents/backend_models/openai_model.py

@@ -14,11 +14,16 @@
 import json
 from typing import Any
 
-import openai
-from openai.types.chat import ChatCompletion
-
 from crab import Action, ActionOutput, BackendModel, BackendOutput, MessageType
 
+try:
+    import openai
+    from openai.types.chat import ChatCompletion
+
+    openai_model_enable = True
+except ImportError:
+    openai_model_enable = False
+
 
 class OpenAIModel(BackendModel):
     def __init__(
@@ -27,6 +32,8 @@ def __init__(
         parameters: dict[str, Any] = dict(),
         history_messages_len: int = 0,
     ) -> None:
+        if not openai_model_enable:
+            raise ImportError("Please install openai to use OpenAIModel")
         super().__init__(
             model,
             parameters,