[TESTS][CLEANUP]

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "2.9.9"
+version = "2.3.0"
 description = "Swarms - Pytorch"
 license = "MIT"
 authors = ["Kye Gomez <kye@apac.ai>"]

swarms/models/base_llm.py

@@ -5,7 +5,7 @@ import time
 from abc import ABC, abstractmethod
 from typing import List, Optional
 
-from swarms.utils.llm_metrcs_decorator import metrics_decorator
+from swarms.utils.llm_metrics_decorator import metrics_decorator
 
 
 def count_tokens(text: str) -> int:

swarms/structs/conversation.py

@@ -187,7 +187,6 @@ class Conversation(BaseStructure):
         # Load the conversation history from a JSON file
         with open(filename, "r") as f:
             self.conversation_history = json.load(f)
-        
 
     def search_keyword_in_conversation(self, keyword: str):
         """Search for a keyword in the conversation history

swarms/utils/__init__.py

@@ -5,7 +5,7 @@ from swarms.utils.parse_code import (
 )
 from swarms.utils.pdf_to_text import pdf_to_text
 from swarms.utils.math_eval import math_eval
-from swarms.utils.llm_metrcs_decorator import metrics_decorator
+from swarms.utils.llm_metrics_decorator import metrics_decorator
 
 # from swarms.utils.phoenix_handler import phoenix_trace_decorator
 

tests/swarms/test_godmode.py

@@ -1,36 +0,0 @@
-from unittest.mock import patch
-from swarms.swarms.god_mode import GodMode, LLM
-
-
-def test_godmode_initialization():
-    godmode = GodMode(llms=[LLM] * 5)
-    assert isinstance(godmode, GodMode)
-    assert len(godmode.llms) == 5
-
-
-def test_godmode_run(monkeypatch):
-    def mock_llm_run(self, task):
-        return "response"
-
-    monkeypatch.setattr(LLM, "run", mock_llm_run)
-    godmode = GodMode(llms=[LLM] * 5)
-    responses = godmode.run("task1")
-    assert len(responses) == 5
-    assert responses == [
-        "response",
-        "response",
-        "response",
-        "response",
-        "response",
-    ]
-
-
-@patch("builtins.print")
-def test_godmode_print_responses(mock_print, monkeypatch):
-    def mock_llm_run(self, task):
-        return "response"
-
-    monkeypatch.setattr(LLM, "run", mock_llm_run)
-    godmode = GodMode(llms=[LLM] * 5)
-    godmode.print_responses("task1")
-    assert mock_print.call_count == 1

tests/swarms/test_multi_agent_collab.py

@@ -6,8 +6,6 @@ from swarms.structs import Agent
 from swarms.models import OpenAIChat
 from swarms.swarms.multi_agent_collab import (
     MultiAgentCollaboration,
-    select_next_speaker_director,
-    select_speaker_round_table,
 )
 
 # Sample agents for testing
@@ -105,13 +103,6 @@ def test_set_interaction_rules(collaboration):
     assert collaboration.interaction_rules == rules
 
 
-def test_set_interaction_rules(collaboration):
-    rules = {"rule1": "action1", "rule2": "action2"}
-    collaboration.set_interaction_rules(rules)
-    assert hasattr(collaboration, "interaction_rules")
-    assert collaboration.interaction_rules == rules
-
-
 def test_repr(collaboration):
     repr_str = repr(collaboration)
     assert isinstance(repr_str, str)
@@ -145,16 +136,6 @@ def test_save(collaboration, tmp_path):
 # Add more tests here...
 
 
-# Example of parameterized test for different selection functions
-@pytest.mark.parametrize(
-    "selection_function",
-    [select_next_speaker_director, select_speaker_round_table],
-)
-def test_selection_functions(collaboration, selection_function):
-    collaboration.select_next_speaker = selection_function
-    assert callable(collaboration.select_next_speaker)
-
-
 # Add more parameterized tests for different scenarios...
 
 

tests/utils/test_metrics_decorator.py

@@ -1,5 +1,5 @@
 import time
-from swarms.utils.llm_metrcs_decorator import metrics_decorator
+from swarms.utils.llm_metrics_decorator import metrics_decorator
 
 
 def test_metrics_decorator():