""" Enhanced Multi-LLM Agent Evaluation Runner with Agno Integration""" import os import gradio as gr import requests import pandas as pd from langchain_core.messages import HumanMessage from veryfinal import build_graph # --- Constants --- DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" # --- Enhanced Agent Definition --- class EnhancedMultiLLMAgent: """A multi-provider LangGraph agent with Agno-style reasoning capabilities.""" def __init__(self): print("Enhanced Multi-LLM Agent with Agno Integration initialized.") try: self.graph = build_graph(provider="groq") print("Enhanced Multi-LLM Graph built successfully.") except Exception as e: print(f"Error building graph: {e}") self.graph = None def __call__(self, question: str) -> str: print(f"Agent received question (first 50 chars): {question[:50]}...") if self.graph is None: return "Error: Agent not properly initialized" # CRITICAL FIX: Always pass the complete state expected by the graph state = { "messages": [HumanMessage(content=question)], "query": question, # This was the critical missing field "agent_type": "", "final_answer": "", "perf": {}, "agno_resp": "", "tools_used": [], "reasoning": "", "confidence": "" } # CRITICAL FIX: Always provide the required config with thread_id config = {"configurable": {"thread_id": f"eval_{hash(question)}"}} try: result = self.graph.invoke(state, config) # Handle different response formats if isinstance(result, dict): if 'messages' in result and result['messages']: answer = result['messages'][-1].content elif 'final_answer' in result: answer = result['final_answer'] else: answer = str(result) else: answer = str(result) # Extract final answer if present if "FINAL ANSWER:" in answer: return answer.split("FINAL ANSWER:")[-1].strip() else: return answer.strip() except Exception as e: error_msg = f"Error: {str(e)}" print(error_msg) return error_msg def run_and_submit_all(profile: gr.OAuthProfile | None): """ Fetches all questions, runs the Enhanced Multi-LLM Agent on them, submits all answers, and displays the results. """ 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" # 1. Instantiate Agent try: agent = EnhancedMultiLLMAgent() if agent.graph is None: return "Error: Failed to initialize agent properly", None except Exception as e: print(f"Error instantiating agent: {e}") return f"Error initializing agent: {e}", None agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "No space ID available" print(f"Agent code URL: {agent_code}") # 2. Fetch Questions 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 Exception as e: print(f"Error fetching questions: {e}") return f"Error fetching questions: {e}", None # 3. Run your Agent results_log = [] answers_payload = [] print(f"Running Enhanced Multi-LLM agent with Agno integration on {len(questions_data)} questions...") for i, item in enumerate(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 print(f"Processing question {i+1}/{len(questions_data)}: {task_id}") try: submitted_answer = agent(question_text) answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer}) results_log.append({ "Task ID": task_id, "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text, "Submitted Answer": submitted_answer[:200] + "..." if len(submitted_answer) > 200 else submitted_answer }) except Exception as e: error_msg = f"AGENT ERROR: {e}" print(f"Error running agent on task {task_id}: {e}") answers_payload.append({"task_id": task_id, "submitted_answer": error_msg}) results_log.append({ "Task ID": task_id, "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text, "Submitted Answer": error_msg }) 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) # 4. Prepare Submission submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload} status_update = f"Enhanced Multi-LLM Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..." print(status_update) # 5. Submit print(f"Submitting {len(answers_payload)} answers to: {submit_url}") try: response = requests.post(submit_url, json=submission_data, timeout=60) response.raise_for_status() result_data = response.json() final_status = ( 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.") results_df = pd.DataFrame(results_log) return final_status, results_df except Exception as e: status_message = f"Submission Failed: {e}" print(status_message) results_df = pd.DataFrame(results_log) return status_message, results_df # --- Build Gradio Interface using Blocks --- with gr.Blocks() as demo: gr.Markdown("# Enhanced Multi-LLM Agent with Agno Integration") 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 your agent, submit answers, and see the score. **Enhanced Agent Features:** - **Multi-LLM Support**: Groq (Llama-3 8B/70B, DeepSeek), Google Gemini, NVIDIA NIM - **Agno Integration**: Systematic reasoning with step-by-step analysis - **Intelligent Routing**: Automatically selects best provider based on query complexity - **Enhanced Tools**: Mathematical operations, web search, Wikipedia integration - **Question-Answering**: Optimized for evaluation tasks with proper formatting - **Error Handling**: Robust fallback mechanisms and comprehensive logging **Routing Examples:** - Standard: "What is the capital of France?" → Llama-3 8B - Complex: "Analyze quantum computing principles" → Llama-3 70B - Search: "Find information about Mercedes Sosa" → Search-Enhanced - Agno: "agno llama-70: Systematic analysis of AI ethics" → Agno Llama-3 70B - Provider-specific: "google: Explain machine learning" → Google Gemini """ ) gr.LoginButton() run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary") 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 + " Enhanced Multi-LLM Agent with Agno Starting " + "-"*30) demo.launch(debug=True, share=False)