Delete multi_agent.py

multi_agent.py

@@ -1,168 +0,0 @@
-from smolagents import (
-    CodeAgent,
-    InferenceClientModel,
-    VisitWebpageTool,
-    WebSearchTool,
-    WikipediaSearchTool,
-    PythonInterpreterTool,
-    FinalAnswerTool,
-    tool,
-)
-from groq import Groq
-from typing import Dict, Any
-import os
-import time
-
-
-# ---- TOOLS ----
-@tool
-def image_process(image_file: str) -> Dict[str, str]:
-    """
-    Extract text from an image file using OCR and return OCR text and base64 encoding.
-
-    Args:
-        image_file: Path to the image file
-
-    Returns:
-        Dict with keys 'ocr_text' and 'base64_image'
-    """
-    try:
-        import pytesseract
-        from PIL import Image
-        from smolagents.utils import encode_image_base64
-
-        image = Image.open(image_file)
-        base64_img = encode_image_base64(image)
-        text = pytesseract.image_to_string(image)
-        return {"ocr_text": text, "base64_image": base64_img}
-    except Exception as e:
-        return {"ocr_text": "", "base64_image": "", "error": str(e)}
-
-
-# ---- GROQ MODEL WRAPPER ----
-class LLMResponse:
-    def __init__(self, content: str, token_usage: int | None = None):
-        self.content = content
-        self.token_usage = token_usage
-
-
-class GroqModel:
-    def __init__(self, model_name=""):
-        self.model_name = model_name
-        self.client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
-
-    def __call__(self, prompt, max_tokens=8096):
-        if isinstance(prompt, str):
-            messages = [{"role": "user", "content": prompt}]
-        else:
-            messages = prompt
-
-        response = None
-        for attempt in range(3):
-            try:
-                response = self.client.chat.completions.create(
-                    messages=messages,
-                    model=self.model_name,
-                    stream=False,
-                    max_tokens=max_tokens,
-                )
-                break
-            except Exception as e:
-                msg = str(e).lower()
-                if "rate limit" in msg and attempt < 2:
-                    wait = 10 * (attempt + 1)
-                    time.sleep(wait)
-                    continue
-                raise
-
-        if response is None:
-            response = self.client.chat.completions.create(
-                messages=messages,
-                model=self.model_name,
-                stream=False,
-                max_tokens=max_tokens,
-            )
-
-        content = response.choices[0].message.content
-        token_usage = None
-        if hasattr(response, "usage") and response.usage is not None:
-            token_usage = response.usage.total_tokens
-
-        return LLMResponse(content, token_usage)
-
-    def generate(self, prompt, max_tokens=8096, **kwargs):
-        # For compatibility with agent frameworks
-        return self.__call__(prompt, max_tokens=max_tokens)
-
-
-# ---- MULTI-AGENT SYSTEM ----
-class MultyAgentSystem:
-    def __init__(self):
-        deepseek_model = GroqModel("deepseek-r1-distill-llama-70b")
-        qwen_model = GroqModel("qwen-qwq-32b")
-
-        # --- Web agent definition ---
-        self.web_agent = CodeAgent(
-            model=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=qwen_model,
-            tools=[PythonInterpreterTool(), image_process],
-            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 handle all math, code, and data manipulation. Use numpy, math, and available libraries. For image or chess tasks, use pytesseract, PIL, or chess as required."
-            ),
-            additional_authorized_imports=[
-                "numpy",
-                "math",
-                "pytesseract",
-                "PIL",
-                "chess",
-            ],
-        )
-
-        # --- Manager agent definition ---
-        self.manager_agent = CodeAgent(
-            model=deepseek_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, 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=5,
-            verbosity_level=2,
-            max_steps=20,
-        )
-
-    def __call__(self, question, **kwargs):
-
-        return self.manager_agent(question, **kwargs)