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41 lines
1.5 KiB
41 lines
1.5 KiB
1 year ago
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import pytest
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from langchain.base_language import BaseLanguageModel
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from langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator import (
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load_response_generator,
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)
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from langchain_experimental.autonomous_agents.hugginggpt.task_executor import (
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TaskExecutor,
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)
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from langchain_experimental.autonomous_agents.hugginggpt.task_planner import (
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load_chat_planner,
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)
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from transformers import load_tool
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from swarms.agents import OmniModalAgent # Replace `your_module_name` with the appropriate module name
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# Mock objects or set up fixtures for dependent classes or external methods
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@pytest.fixture
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def mock_llm():
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# For this mock, we are assuming the BaseLanguageModel has a method named "process"
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class MockLLM(BaseLanguageModel):
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def process(self, input):
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return "mock response"
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return MockLLM()
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@pytest.fixture
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def omni_agent(mock_llm):
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return OmniModalAgent(mock_llm)
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def test_omnimodalagent_initialization(omni_agent):
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assert omni_agent.llm is not None, "LLM initialization failed"
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assert len(omni_agent.tools) > 0, "Tools initialization failed"
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def test_omnimodalagent_run(omni_agent):
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input_string = "Hello, how are you?"
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response = omni_agent.run(input_string)
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assert response is not None, "Response generation failed"
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assert isinstance(response, str), "Response should be a string"
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def test_task_executor_initialization(omni_agent):
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assert omni_agent.task_executor is not None, "TaskExecutor initialization failed"
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