import os import gradio as gr import requests import pandas as pd from smolagents import Tool, CodeAgent, Model # Import internal modules from config import ( DEFAULT_API_URL, USE_LLAMACPP, LLAMACPP_CONFIG ) from tools.tool_manager import ToolManager from utils.llama_cpp_model import LlamaCppModel class GaiaToolCallingAgent: """Tool-calling agent specifically designed for the GAIA system.""" def __init__(self, local_model=None): print("GaiaToolCallingAgent initialized.") self.tool_manager = ToolManager() self.name = "tool_agent" self.description = "A specialized agent that uses various tools to answer questions" self.local_model = local_model if not self.local_model: try: from utils.llama_cpp_model import LlamaCppModel self.local_model = LlamaCppModel( max_tokens=512 ) except Exception as e: print(f"Couldn't initialize local model in tool agent: {e}") self.local_model = None def run(self, query: str) -> str: print(f"Processing query: {query}") tools = self.tool_manager.get_tools() context_info = [] for tool in tools: try: if self._should_use_tool(tool, query): print(f"Using tool: {tool.name}") result = tool.forward(query) if result: context_info.append(f"{tool.name} Results:\n{result}") except Exception as e: print(f"Error using {tool.name}: {e}") full_context = "\n\n".join(context_info) if context_info else "" if full_context and self.local_model: try: prompt = f""" Based on the following information, please provide a comprehensive answer to the question: "{query}" CONTEXT INFORMATION: {full_context} Answer: """ response = self.local_model.generate(prompt) return response except Exception as e: print(f"Error generating response with local model: {e}") return full_context else: if not full_context: return "I couldn't find any relevant information to answer your question." return full_context def __call__(self, query: str) -> str: print(f"Tool agent received query: {query}") return self.run(query) def _should_use_tool(self, tool: Tool, query: str) -> bool: query_lower = query.lower() patterns = { "web_search": ["current", "latest", "recent", "who", "what", "when", "where", "how"], "web_content": ["content", "webpage", "website", "page"], "youtube_video": ["youtube.com", "youtu.be"], "wikipedia_search": ["wikipedia", "wiki", "article"], "gaia_retriever": ["gaia", "agent", "ai", "artificial intelligence"] } if tool.name not in patterns: return True return any(pattern in query_lower for pattern in patterns.get(tool.name, [])) def download_model_if_needed(model_path, model_url): if not os.path.exists(model_path): print(f"Downloading model from {model_url}...") os.makedirs(os.path.dirname(model_path), exist_ok=True) with requests.get(model_url, stream=True) as response: response.raise_for_status() with open(model_path, "wb") as f: for chunk in response.iter_content(chunk_size=8192): f.write(chunk) print("Download complete.") def create_manager_agent() -> CodeAgent: try: from config import USE_LLAMACPP, LLAMACPP_CONFIG if USE_LLAMACPP: # Use TheBloke's model with auto-download model_path = LLAMACPP_CONFIG.get("model_path") or "./models/llama-2-7b.Q4_0.gguf" model_url = "https://huggingface.co/TheBloke/Llama-2-7B-GGUF/resolve/main/llama-2-7b.Q4_0.gguf" download_model_if_needed(model_path, model_url) model = LlamaCppModel( model_path=model_path, n_ctx=LLAMACPP_CONFIG.get("n_ctx", 2048), n_gpu_layers=LLAMACPP_CONFIG.get("n_gpu_layers", 0), temperature=LLAMACPP_CONFIG.get("temperature", 0.7) ) print(f"Using LlamaCpp model from {model_path}") else: from smolagents import StubModel model = StubModel() print("Using StubModel as fallback") except Exception as e: print(f"Error setting up model: {e}") try: model = LlamaCppModel() print("Using fallback LlamaCpp model configuration") except Exception as e2: from smolagents import StubModel model = StubModel() print(f"Using StubModel due to error: {e2}") tool_agent = GaiaToolCallingAgent(local_model=model) manager_agent = CodeAgent( model=model, tools=[], managed_agents=[tool_agent], additional_authorized_imports=[ "json", "pandas", "numpy", "re", "requests", "bs4" ], planning_interval=3, verbosity_level=2, max_steps=10 ) print("Manager agent created with local model") return manager_agent def create_agent(): try: print("Initializing GAIA agent system...") return create_manager_agent() except Exception as e: print(f"Error creating GAIA agent: {e}") return None def run_and_submit_all(profile: gr.OAuthProfile | None): space_id = os.getenv("SPACE_ID") if profile: username = f"{profile.username}" print(f"User logged in: {username}") else: print("User not logged in.") return "Please Login to Hugging Face with the button.", None api_url = DEFAULT_API_URL questions_url = f"{api_url}/questions" submit_url = f"{api_url}/submit" try: print("Initializing GAIA agent system...") agent = create_agent() if not agent: return "Error: Could not initialize agent.", None print("GAIA agent initialization complete.") except Exception as e: print(f"Error initializing agent: {e}") return f"Error initializing agent: {e}", None print(f"Fetching questions from: {questions_url}") try: response = requests.get(questions_url, timeout=15) response.raise_for_status() questions_data = response.json() if not questions_data: print("Fetched questions list is empty.") return "Fetched questions list is empty or invalid format.", None print(f"Fetched {len(questions_data)} questions.") except requests.exceptions.RequestException as e: print(f"Error fetching questions: {e}") return f"Error fetching questions: {e}", None except Exception as e: print(f"An unexpected error occurred fetching questions: {e}") return f"An unexpected error occurred fetching questions: {e}", None results_log = [] answers_payload = [] print(f"Running agent on {len(questions_data)} questions...") for item in questions_data: task_id = item.get("task_id") question_text = item.get("question") if not task_id or question_text is None: print(f"Skipping item with missing task_id or question: {item}") continue try: response = agent.run(f"Answer this question concisely: {question_text}") if isinstance(response, dict): submitted_answer = response.get("answer", str(response)) else: submitted_answer = str(response) answers_payload.append({ "task_id": task_id, "submitted_answer": submitted_answer }) results_log.append({ "Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer }) except Exception as e: print(f"Error running agent on task {task_id}: {e}") results_log.append({ "Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}" }) if not answers_payload: print("Agent did not produce any answers to submit.") return "Agent did not produce any answers to submit.", pd.DataFrame(results_log) agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" submission_data = { "username": username.strip(), "agent_code": agent_code, "answers": answers_payload } print(f"Submitting {len(answers_payload)} answers to API...") try: response = requests.post(submit_url, json=submission_data, timeout=60) response.raise_for_status() result_data = response.json() status_message = ( f"Submission Successful!\n" f"User: {result_data.get('username')}\n" f"Overall Score: {result_data.get('score', 'N/A')}% " f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n" f"Message: {result_data.get('message', 'No message received.')}" ) print("Submission successful.") return status_message, pd.DataFrame(results_log) except Exception as e: status_message = f"Submission Failed: {str(e)}" print(f"Error during submission: {e}") return status_message, pd.DataFrame(results_log) with gr.Blocks() as demo: gr.Markdown("# GAIA Agent Evaluation Runner") gr.Markdown(""" **Instructions:** 1. Log in to your Hugging Face account using the button below. 2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run the agent, and see the score. """) gr.LoginButton() run_button = gr.Button("Run Evaluation & Submit All Answers") status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False) results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True) run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table]) if __name__ == "__main__": print("\n" + "-"*30 + " GAIA Agent Starting " + "-"*30) demo.launch(debug=True, share=False)