#!/usr/bin/env python3 """ 🚀 Enhanced GAIA Agent Interface - Full API Integration Complete Gradio interface for GAIA benchmark with API connectivity and scoring """ import os import gradio as gr import json from datetime import datetime from gaia_agent import ModularGAIAAgent import requests import inspect import pandas as pd agent = ModularGAIAAgent() # (Keep Constants as is) # --- Constants --- DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" # --- Basic Agent Definition --- # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------ class BasicAgent: def __init__(self): print("BasicAgent (GAIA Modular Agent) initialized.") self.agent = ModularGAIAAgent() def __call__(self, question: str) -> str: print(f"Agent received question (first 50 chars): {question[:50]}...") try: answer, trace = self.agent.answer_question({"task_id": "manual", "question": question, "file_name": ""}) print(f"Agent returning answer: {answer}") return answer except Exception as e: print(f"Agent error: {e}") return f"AGENT ERROR: {e}" def run_and_submit_all( profile: gr.OAuthProfile | None): """ Fetches all questions, runs the BasicAgent 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 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 ( modify this part to create your agent) try: agent = BasicAgent() 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) agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" print(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 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") if not task_id or question_text is None: print(f"Skipping item with missing task_id or question: {item}") continue 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, "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) # 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 def show_help(): return ( "# Agent Capabilities\n" "- Multi-modal QA (text, audio, image, code, table, YouTube/video)\n" "- File download and analysis from API\n" "- Advanced video QA: object detection, captioning, ASR\n" "- Secure code execution\n" "- Robust error handling and logging\n" "- GAIA-compliant output\n" "\nSee README.md for full details." ) def submit_answers(username, agent_code_url): # Placeholder for submission logic return f"Submission for {username} with code {agent_code_url} (not implemented in demo)" def show_leaderboard(): # Placeholder for leaderboard logic return "Leaderboard feature coming soon." demo = gr.Blocks(title="GAIA Benchmark Agent", theme=gr.themes.Soft()) with demo: gr.Markdown(""" # 🤖 GAIA Benchmark Agent Multi-modal, multi-step reasoning agent for the Hugging Face GAIA benchmark. """) with gr.Tabs(): with gr.TabItem("API Q&A"): api_btn = gr.Button("Run on API Questions", variant="primary") api_output = gr.Textbox(label="Answers and Reasoning Trace", lines=20) api_btn.click(run_api_questions, outputs=api_output) with gr.TabItem("Manual Input"): manual_q = gr.Textbox(label="Enter your question", lines=3) manual_btn = gr.Button("Answer", variant="primary") manual_a = gr.Textbox(label="Answer") manual_trace = gr.Textbox(label="Reasoning Trace", lines=5) manual_btn.click(run_manual_question, inputs=manual_q, outputs=[manual_a, manual_trace]) with gr.TabItem("Submission/Leaderboard"): username = gr.Textbox(label="Hugging Face Username") code_url = gr.Textbox(label="Agent Code URL") submit_btn = gr.Button("Submit Answers", variant="primary") submit_out = gr.Textbox(label="Submission Result") submit_btn.click(submit_answers, inputs=[username, code_url], outputs=submit_out) leaderboard_btn = gr.Button("Show Leaderboard") leaderboard_out = gr.Textbox(label="Leaderboard") leaderboard_btn.click(show_leaderboard, outputs=leaderboard_out) with gr.TabItem("Agent Help"): help_md = gr.Markdown(show_help()) 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("ℹī¸ 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("ℹī¸ 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)