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from smolagents import DuckDuckGoSearchTool, HfApiModel, load_tool, CodeAgent, PythonInterpreterTool, VisitWebpageTool, \
Tool
import hashlib
import json
from transformers import AutoTokenizer, AutoModelForCausalLM
import os
class ModelMathTool(Tool):
name = "math_model"
description = "Answers advanced math questions using a pretrained math model."
inputs = {
"problem": {
"type": "string",
"description": "Math problem to solve.",
}
}
output_type = "string"
def __init__(self, model_id="Qwen/Qwen2.5-Math-7B"):
print(f"Loading math model: {model_id}")
self.tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
self.model = HfApiModel(model_id=model_id, max_tokens=512)
def forward(self, problem: str) -> str:
print(f"[MathModelTool] Question: {problem}")
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, token=tok)
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) |