Update agents.py

agents.py

@@ -6,7 +6,6 @@ from mcp import StdioServerParameters
 from huggingface_hub import HfApi, login
 from dotenv import load_dotenv
 from typing import Optional
-from models.gemini_model import GeminiModel
 import requests
 import re
 import string
@@ -26,7 +25,6 @@ def download_file(task_id: str) -> str:
     Args:
         task_id: the ID of the task to download the file for.
     """
-    # Implement your file download logic here
     data = requests.get(f"{DEFAULT_API_URL}/files/{task_id}")
     if data.status_code == 200:
         file_path = f"/tmp/{task_id}"
@@ -44,7 +42,6 @@ def get_file_content_as_text(task_id: str) -> str:
     Args:
         task_id: the ID of the task to get the file content for.
     """
-    # Implement your file content retrieval logic here
     data = requests.get(f"{DEFAULT_API_URL}/files/{task_id}")
     if data.status_code == 200:
         return data.text
@@ -59,12 +56,11 @@ def load_hf_model(modelName: str):
     :param modelName: Name of the model
     :return: model
     """
-    load_dotenv()  # Lädt automatisch .env im Projektordner
+    load_dotenv()
 
     # for local usage, we might use a hf token to log in
     # hf_token = os.getenv("hugging_face")
-    # login(token=hf_token)  # Authentifizierung bei Hugging Face
-    # Modell initialisieren
+    # login(token=hf_token)  # Login at hugging face
     model = HfApiModel(model_id=modelName)
     return model
 
@@ -75,7 +71,6 @@ def load_ollama_model(modelName: str):
     :param modelName: Name of the model
     :return: model (via OpenAI compatible API)
     """
-    # Modell initialisieren
     model = OpenAIServerModel(model_id=modelName, api_base="http://localhost:11434/v1")
     return model
 
@@ -85,21 +80,18 @@ def load_lmStudio_model(modelName: str):
     :param modelName: Name of the model
     :return: model, accessible through the OpenAI compatible API
     """
-    # Modell initialisieren
-    #model = LiteLLMModel(model_id=modelName, api_base="http://localhost:1234")
     model = OpenAIServerModel(model_id=modelName, api_base="http://localhost:1234/v1")
     return model
 
 def load_gemini_model(model_name: str):
     """
-    Loads 
+    Loads the gemini model
     :return: model
     """
     try:
         print(f"Gemini API Key: {os.getenv('GEMINI_API_KEY')}")
         model = LiteLLMModel(model_id=f"gemini/{model_name}",
                          api_key=os.getenv("GEMINI_API_KEY"))
-        #model = GeminiModel(api_key=os.getenv("GEMINI_API_KEY"))
         return model
     except Exception as e:
         print("Error loading Gemini model:", e)
@@ -108,7 +100,6 @@ def load_gemini_model(model_name: str):
 
 
 def get_agent(model_name:str, model_type:str) -> Optional[CodeAgent]:
-    # Modell initialisieren
     
     match model_type:
         case "hugging face":
@@ -123,31 +114,11 @@ def get_agent(model_name:str, model_type:str) -> Optional[CodeAgent]:
             print("Model type not supported.")
             return None
     
-    #model = load_lmStudio_model("gemma-3-4b-it")
-    #model = load_gemini_model()
-    #mopip del = HfApiModel()
-    #model=InferenceClientModel(model_id="meta-llama/Meta-Llama-3.1-8B-Instruct")
-    #model = TransformersModel(model_id="HuggingFaceTB/SmolLM-135M-Instruct")
     # Tools laden
     web_search_tool = DuckDuckGoSearchTool()
     final_answer_tool = FinalAnswerTool()
     visit_webpage_tool = VisitWebpageTool()
-    
-    #speech_to_text_tool = SpeechToTextTool()
-    #transcript_tool = load_tool("maguid28/TranscriptTool", trust_remote_code=True)
-
-    #mcp_tool_collection = ToolCollection.from_mcp(server_parameters, trust_remote_code=True)
-    #with ToolCollection.from_mcp(server_parameters, trust_remote_code=True) as tool_collection:
-    #    mcp_tool_agent = CodeAgent(tools=[*tool_collection.tools], add_base_tools=True)
-
-    #server_parameters = StdioServerParameters(
-    #    command="uv",
-    #    args=["--quiet", "pubmedmcp@0.1.3"],
-    #    env={"UV_PYTHON": "3.12", **os.environ},
-    #)
-    #
-    #with ToolCollection.from_mcp(server_parameters, trust_remote_code=True) as tool_collection:
-    #    mcp_agent = CodeAgent(tools=[*tool_collection.tools], model=model, add_base_tools=True)
+
 
     variation_agent = CodeAgent(
         model=model,