Delete multi_agent.py

multi_agent.py

@@ -1,154 +0,0 @@
-from smolagents import (
-    CodeAgent,
-    VisitWebpageTool,
-    WebSearchTool,
-    WikipediaSearchTool,
-    PythonInterpreterTool,
-    FinalAnswerTool,
-    LiteLLMModel,
-)
-from vision_tool import image_reasoning_tool
-from throttle import consume
-import os
-import time
-
-# ---- TOOLS ----
-
-common = dict(
-    api_key=os.getenv("GROQ_API_KEY"),
-    api_base="https://api.groq.com/openai/v1",
-    flatten_messages_as_text=True,
-)
-
-
-
-# ---- MULTI-AGENT SYSTEM ----
-class MultyAgentSystem:
-    def __init__(self):
-        self.deepseek_model = LiteLLMModel(
-            "groq/deepseek-r1-distill-llama-70b",
-            max_tokens=512,
-            **common,
-        )
-        self.qwen_model = LiteLLMModel("groq/qwen-qwq-32b", **common)
-        self.fallback_model = LiteLLMModel("groq/llama3-70b-8k", **common)
-
-        self.verification_limit = int(os.getenv("VERIFY_WORD_LIMIT", "75"))
-
-        # --- Web agent definition ---
-        self.web_agent = CodeAgent(
-            model=self.qwen_model,
-            tools=[WebSearchTool(), VisitWebpageTool(), WikipediaSearchTool()],
-            name="web_agent",
-            description=(
-                "You are a web browsing agent. Whenever the given {task} involves browsing "
-                "the web or a specific website such as Wikipedia or YouTube, you will use "
-                "the provided tools. For web-based factual and retrieval tasks, be as precise and source-reliable as possible."
-            ),
-            additional_authorized_imports=[
-                "markdownify",
-                "json",
-                "requests",
-                "urllib.request",
-                "urllib.parse",
-                "wikipedia-api",
-            ],
-            verbosity_level=0,
-            max_steps=10,
-        )
-
-        # --- Info agent definition ---
-        self.info_agent = CodeAgent(
-            model=self.qwen_model,
-            tools=[PythonInterpreterTool(), image_reasoning_tool],
-            name="info_agent",
-            description=(
-                "You are an agent tasked with cleaning, parsing, calculating information, and performing OCR if images are provided in the {task}. "
-                "You can also analyze images using a vision model. You handle all math, code, and data manipulation. Use numpy, math, and available libraries. "
-                "For image or chess tasks, use pytesseract, PIL, chess, or the image_reasoning_tool as required."
-            ),
-            additional_authorized_imports=[
-                "numpy",
-                "math",
-                "pytesseract",
-                "PIL",
-                "chess",
-            ],
-        )
-
-        # --- Manager agent definition ---
-        manager_planning_interval = int(os.getenv("MANAGER_PLANNING_INTERVAL", "3"))
-        manager_max_steps = int(os.getenv("MANAGER_MAX_STEPS", "8"))
-
-        # The manager starts with the smaller Qwen model to minimize token usage
-        # and only relies on DeepSeek when verifying critical answers.
-        self.manager_agent = CodeAgent(
-            model=self.qwen_model,
-            tools=[FinalAnswerTool()],
-            managed_agents=[self.web_agent, self.info_agent],
-            name="manager_agent",
-            description=(
-                "You are the manager. Given a {task}, plan which agent to use: "
-                "If web data is needed, delegate to web_agent. If math, parsing, image reasoning, or code is needed, use info_agent. "
-                "After collecting outputs, optionally cross-validate and check correctness, then finalize and submit the best answer using FinalAnswerTool. "
-                "For each task, explicitly explain your planning steps and reasons for choosing which agent, and always prefer the most accurate and complete answer possible."
-            ),
-            additional_authorized_imports=[
-                "json",
-                "pandas",
-                "numpy",
-            ],
-            planning_interval=manager_planning_interval,
-            verbosity_level=2,
-            max_steps=manager_max_steps,
-        )
-
-        # runtime tracking for fallback switching
-        self.total_runtime = 0.0
-        self.first_call_duration = None
-        self.model_switched = False
-
-    def _switch_to_fallback(self):
-        if self.model_switched:
-            return
-        self.manager_agent.model = self.fallback_model
-        self.model_switched = True
-
-    def run(self, question, high_stakes: bool = False, **kwargs):
-        start_time = time.time()
-        print("Generating initial answer with Qwen-32B")
-        initial_answer = self.manager_agent(question, **kwargs)
-        call_duration = time.time() - start_time
-
-        answer = initial_answer
-        if high_stakes or len(initial_answer.split()) > self.verification_limit:
-            print("Verifying answer using DeepSeek-70B")
-            verification_prompt = (
-                "Review the following answer for accuracy and rewrite if needed:"
-                f"\n\n{initial_answer}"
-            )
-            try:
-                max_completion_tokens = kwargs.get("max_completion_tokens", 512)
-                prompt_tokens = len(verification_prompt.split())
-                consume(prompt_tokens + max_completion_tokens)
-                answer = self.deepseek_model(
-                    verification_prompt, max_completion_tokens=max_completion_tokens
-                )
-            except Exception as e:
-                print(f"Verification failed: {e}. Using initial answer.")
-                answer = initial_answer
-
-        if self.first_call_duration is None:
-            self.first_call_duration = call_duration
-            if self.first_call_duration > 30:
-                self._switch_to_fallback()
-
-        self.total_runtime += call_duration
-        if self.total_runtime > 300 and not self.model_switched:
-            self._switch_to_fallback()
-
-        return answer
-
-    def __call__(self, question, high_stakes: bool = False, **kwargs):
-
-        return self.run(question, high_stakes=high_stakes, **kwargs)