import os import gradio as gr import requests import inspect import pandas as pd from agent import build_graph from langchain_core.messages import HumanMessage # (Keep Constants as is) # --- Constants --- DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" # --- Agent Definition --- # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------ class GAIAAgent: def __init__(self): print("GAIAAgent initialized - building LangGraph agent...") self.graph = build_graph(provider="vertexai") print("LangGraph agent built successfully.") def __call__(self, question: str) -> str: print(f"Agent received question (first 50 chars): {question[:50]}...") try: # Invoke the graph with the question result = self.graph.invoke({"messages": [HumanMessage(content=question)]}) # Extract the final answer from the last message messages = result.get("messages", []) if messages: last_message = messages[-1].content # Look for FINAL ANSWER in the response if "FINAL ANSWER:" in last_message: answer = last_message.split("FINAL ANSWER:")[-1].strip() else: answer = last_message print(f"Agent returning answer: {answer[:100]}...") return answer else: return "No response generated" except Exception as e: print(f"Error running agent: {e}") return f"Error: {str(e)}" def run_and_submit_all(): """ Fetches all questions, runs the GAIAAgent on them, submits all answers, and displays the results. """ # --- Determine HF Space Runtime URL and Repo URL --- space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code # For local testing, use a default username username = os.getenv("HF_USERNAME", "local_user") print(f"Running as: {username}") api_url = DEFAULT_API_URL questions_url = f"{api_url}/questions" submit_url = f"{api_url}/submit" # 1. Instantiate Agent ( modify this part to create your agent) try: agent = GAIAAgent() except Exception as e: print(f"Error instantiating agent: {e}") return f"Error initializing agent: {e}", None # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public) if space_id: agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" else: agent_code = "local_development" print(f"Agent code location: {agent_code}") # 2. Fetch Questions and Download Associated Files 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.") # Download files for questions that have them files_url = f"{api_url}/files" for item in questions_data: task_id = item.get("task_id") file_name = item.get("file_name", "") if file_name: # If there's a file associated with this question print(f"Downloading file for task {task_id}: {file_name}") try: file_response = requests.get(f"{files_url}/{task_id}", timeout=30) file_response.raise_for_status() # Determine file extension from content type or file_name content_type = file_response.headers.get('content-type', '') if not file_name: if 'image' in content_type: file_name = f"{task_id}.png" elif 'audio' in content_type: file_name = f"{task_id}.mp3" elif 'excel' in content_type or 'spreadsheet' in content_type: file_name = f"{task_id}.xlsx" elif 'python' in content_type or 'text' in content_type: file_name = f"{task_id}.py" else: file_name = f"{task_id}.bin" # Save the file with open(file_name, 'wb') as f: f.write(file_response.content) # Add file path to the item item['file_path'] = file_name print(f" Downloaded: {file_name} ({len(file_response.content)} bytes)") except requests.exceptions.RequestException as e: print(f" Error downloading file for {task_id}: {e}") item['file_path'] = None except requests.exceptions.RequestException as e: print(f"Error fetching questions: {e}") return f"Error fetching questions: {e}", None except requests.exceptions.JSONDecodeError as e: print(f"Error decoding JSON response from questions endpoint: {e}") print(f"Response text: {response.text[:500]}") return f"Error decoding server response for 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 # 3. Run your Agent 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") file_path = item.get("file_path", None) if not task_id or question_text is None: print(f"Skipping item with missing task_id or question: {item}") continue # Add file path information to the question if a file exists if file_path: enhanced_question = f"{question_text}\n\nFile available at: {file_path}" else: enhanced_question = question_text try: print(f"Processing task {task_id}...") submitted_answer = agent(enhanced_question) 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}) print(f" Answer: {submitted_answer[:100]}..." if len(submitted_answer) > 100 else f" 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) # 4. Prepare Submission submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload} status_update = f"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 requests.exceptions.HTTPError as e: error_detail = f"Server responded with status {e.response.status_code}." try: error_json = e.response.json() error_detail += f" Detail: {error_json.get('detail', e.response.text)}" except requests.exceptions.JSONDecodeError: error_detail += f" Response: {e.response.text[:500]}" status_message = f"Submission Failed: {error_detail}" print(status_message) results_df = pd.DataFrame(results_log) return status_message, results_df except requests.exceptions.Timeout: status_message = "Submission Failed: The request timed out." print(status_message) results_df = pd.DataFrame(results_log) return status_message, results_df except requests.exceptions.RequestException as e: status_message = f"Submission Failed: Network error - {e}" print(status_message) results_df = pd.DataFrame(results_log) return status_message, results_df except Exception as e: status_message = f"An unexpected error occurred during submission: {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("# GAIA Agent Evaluation Runner") gr.Markdown( """ **Instructions:** 1. Your agent is configured to use Google VertexAI Gemini model 2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score. 3. Note: This can take some time as the agent processes all questions. --- **Setup:** - Model: Gemini 2.5 Pro (VertexAI) - Tools: Wikipedia, Web Search (Tavily), ArXiv, Math operations - Vector Store: ChromaDB (for similar question retrieval) """ ) run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary") status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False) # Removed max_rows=10 from DataFrame constructor results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True) run_button.click( fn=run_and_submit_all, inputs=[], outputs=[status_output, results_table] ) if __name__ == "__main__": print("\n" + "-"*30 + " App Starting " + "-"*30) # Check for SPACE_HOST and SPACE_ID at startup for information space_host_startup = os.getenv("SPACE_HOST") space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup if space_host_startup: print(f"✅ SPACE_HOST found: {space_host_startup}") print(f" Runtime URL should be: https://{space_host_startup}.hf.space") else: print("ℹ️ SPACE_HOST environment variable not found (running locally?).") if space_id_startup: # Print repo URLs if SPACE_ID is found print(f"✅ SPACE_ID found: {space_id_startup}") print(f" Repo URL: https://huggingface.co/spaces/{space_id_startup}") print(f" Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main") else: print("ℹ️ SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.") print("-"*(60 + len(" App Starting ")) + "\n") print("Launching Gradio Interface for Basic Agent Evaluation...") demo.launch(debug=True, share=False)