import os import gradio as gr import requests import pandas as pd # --- Constants --- DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" # --- Agent Definition --- def build_agent(): """Build and return the smolagents CodeAgent with Groq backend.""" from smolagents import CodeAgent, LiteLLMModel, DuckDuckGoSearchTool, WikipediaSearchTool, VisitWebpageTool, tool @tool def download_task_file(task_id: str) -> str: """Download a file associated with a GAIA task and return its local path. Use this when a question mentions or implies there is an attached file. Args: task_id: The task ID whose file should be downloaded. Returns: The local file path where the file was saved, or an error message. """ url = f"{DEFAULT_API_URL}/files/{task_id}" try: resp = requests.get(url, timeout=30) if resp.status_code == 404: return "No file found for this task." resp.raise_for_status() # Try to determine file extension from Content-Disposition or Content-Type content_disp = resp.headers.get("content-disposition", "") if "filename=" in content_disp: filename = content_disp.split("filename=")[-1].strip().strip('"') else: ct = resp.headers.get("content-type", "") ext_map = { "image/png": ".png", "image/jpeg": ".jpg", "image/gif": ".gif", "application/pdf": ".pdf", "text/plain": ".txt", "text/csv": ".csv", "application/json": ".json", "audio/mpeg": ".mp3", "audio/wav": ".wav", "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet": ".xlsx", } ext = next((v for k, v in ext_map.items() if k in ct), ".bin") filename = f"task_{task_id}{ext}" path = f"/tmp/{filename}" with open(path, "wb") as f: f.write(resp.content) return path except Exception as e: return f"Error downloading file: {e}" openai_api_key = os.getenv("OPENAI_API_KEY") if not openai_api_key: raise ValueError("OPENAI_API_KEY environment variable not set. Add it as a Secret in your HF Space settings.") model = LiteLLMModel( model_id="openai/gpt-4o", api_key=openai_api_key, temperature=0.0, ) agent = CodeAgent( tools=[ DuckDuckGoSearchTool(), WikipediaSearchTool(), VisitWebpageTool(), download_task_file, ], model=model, additional_authorized_imports=[ "requests", "json", "re", "math", "datetime", "csv", "io", "os", "pathlib", "PIL", "PIL.Image", "pandas", "openpyxl", ], max_steps=15, ) return agent class BasicAgent: def __init__(self): print("Initializing agent (loading smolagents + Groq)...") self._agent = build_agent() print("Agent ready.") def __call__(self, question: str) -> str: print(f"Question: {question[:100]}...") system_note = ( "You are a precise research assistant solving GAIA benchmark questions. " "Your answers are graded by EXACT STRING MATCH, so formatting is critical.\n\n" "Rules:\n" "- Reply with ONLY the answer, nothing else. No explanation, no 'FINAL ANSWER:' prefix.\n" "- Numbers: use digits (e.g. 42, 3.14). No units unless the question asks for them.\n" "- Lists: comma-separated on one line unless the question specifies otherwise.\n" "- Names/strings: exact spelling, match the question's expected format.\n" "- If a file is attached to the question, use the download_task_file tool first.\n" "- Search the web and visit pages to verify facts before answering.\n" "- Think step by step, but output ONLY the final answer." ) full_prompt = f"{system_note}\n\nQuestion: {question}" try: result = self._agent.run(full_prompt) answer = str(result).strip() print(f"Answer: {answer[:100]}") return answer except Exception as e: print(f"Agent error: {e}") return f"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. """ 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 = BasicAgent() 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" 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: return "Fetched questions list is empty or invalid format.", None print(f"Fetched {len(questions_data)} questions.") except Exception as e: return f"Error fetching questions: {e}", None # 3. Run 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: return "Agent did not produce any answers to submit.", pd.DataFrame(results_log) # 4. Submit submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload} 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.") return final_status, pd.DataFrame(results_log) 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 Exception: error_detail += f" Response: {e.response.text[:500]}" print(f"Submission Failed: {error_detail}") return f"Submission Failed: {error_detail}", pd.DataFrame(results_log) except Exception as e: print(f"Unexpected error during submission: {e}") return f"An unexpected error occurred during submission: {e}", pd.DataFrame(results_log) # --- Gradio Interface --- with gr.Blocks() as demo: gr.Markdown("# GAIA Agent Evaluation Runner") gr.Markdown( """ **Instructions:** 1. Make sure `GROQ_API_KEY` is set as a Secret in your HF Space settings. 2. Log in with your Hugging Face account below. 3. Click **Run Evaluation & Submit All Answers** — the agent will answer all 20 GAIA questions and submit. --- *Note: This can take several minutes as the agent processes each question.* """ ) 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 + " App Starting " + "-" * 30) space_host_startup = os.getenv("SPACE_HOST") space_id_startup = os.getenv("SPACE_ID") if space_host_startup: print(f"✅ SPACE_HOST found: {space_host_startup}") else: print("ℹ️ SPACE_HOST not found (running locally?).") if space_id_startup: print(f"✅ SPACE_ID found: {space_id_startup}") else: print("ℹ️ SPACE_ID not found (running locally?).") print("-" * (60 + len(" App Starting ")) + "\n") print("Launching Gradio Interface...") demo.launch(debug=True, share=False)