Update tooling.py

tooling.py

@@ -1,5 +1,4 @@
-from smolagents import DuckDuckGoSearchTool, HfApiModel, load_tool, CodeAgent, PythonInterpreterTool, VisitWebpageTool, \
-    Tool
+from smolagents import Tool
 import hashlib
 import json
 from transformers import AutoTokenizer, AutoModelForCausalLM
@@ -29,64 +28,3 @@ class ModelMathTool(Tool):
         response = self.model.__call__(problem)
         return response
 
-
-# (Keep Constants as is)
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-web_search = DuckDuckGoSearchTool()
-python_interpreter = PythonInterpreterTool()
-visit_webpage_tool = VisitWebpageTool()
-model_math_tool = ModelMathTool()
-
-# If the agent does not answer, the model is overloaded, please use another model or the following Hugging Face Endpoint that also contains qwen2.5 coder:
-# model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud'
-
-model = HfApiModel(model_id="HuggingFaceH4/zephyr-7b-beta", max_tokens=512)
-
-
-def get_cache_key(question: str) -> str:
-    return hashlib.sha256(question.encode()).hexdigest()
-
-
-def load_cached_answer(question: str) -> str | None:
-    key = get_cache_key(question)
-    path = f"cache/{key}.json"
-    if os.path.exists(path):
-        with open(path, "r") as f:
-            data = json.load(f)
-            return data.get("answer")
-    return None
-
-
-def cache_answer(question: str, answer: str):
-    key = get_cache_key(question)
-    path = f"cache/{key}.json"
-    with open(path, "w") as f:
-        json.dump({"question": question, "answer": answer}, f)
-
-
-class BasicAgent:
-    def __init__(self):
-        print("BasicAgent initialized.")
-        self.agent = CodeAgent(
-            model=model,
-            tools=[model_math_tool],
-            max_steps=1,
-            verbosity_level=0,
-            grammar=None,
-            planning_interval=3,
-
-        )
-
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        answer = self.agent.run(question)
-        return answer
-
-
-
-agent = BasicAgent()
-
-response = agent.__call__(question="How much is 2*2?")
-print(response)