""" NOTE: - If USE_RATE_LIMITER env variable is True, the agent will use a rate limiter to avoid hitting API limits. """ import os import gradio as gr import requests import inspect import pandas as pd from agent import build_agent from langchain_core.messages import HumanMessage from langfuse.langchain import CallbackHandler langfuse_handler = CallbackHandler() # (Keep Constants as is) # --- Constants --- DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" questions_url = f"{DEFAULT_API_URL}/questions" submit_url = f"{DEFAULT_API_URL}/submit" files_url = f"{DEFAULT_API_URL}/files/" # Needs task_id # --- Basic Agent Definition --- class SuperAgent: def __init__(self): print("SuperAgent initialized.") self.agent = build_agent(provider="google") # Change to "hf" for HuggingFace self.recursion_limit = os.getenv("RECURSION_LIMIT", "25") def __call__(self, data: dict) -> str: """ Args: data (str): A string containing the question to be answered. Schema: { task_id: str, question: str, file_name: str, } """ # Quick validation of input data (TODO: Use pydantic for schema) required_keys = ["question", "task_id", "file_name"] if not all(k in data for k in required_keys): raise ValueError("Input data must contain 'question', 'task_id', and 'file_name'.") task_id, question, file_name = data["task_id"], data["question"], data["file_name"] print(f"Agent received question (first 50 chars): {question[:50]}...") # Build HumanMessage content = [ {"type": "text", "text": question} ] if file_name != "": file_url = f"{files_url}{task_id}" if file_name.endswith((".png", ".jpg", ".jpeg")): content.append({"type": "image_url", "image_url": {"url": file_url}}) elif file_name.endswith((".py")): # For code files, we can just send the text content try: response = requests.get(file_url, timeout=15) response.raise_for_status() code_content = response.text content.append({"type": "text", "text": code_content}) except Exception as e: print(f"Error fetching code file: {e}") elif file_name.endswith((".xlsx", ".xls")): content.append({"type": "text", "text": "Excel file url: " + file_url}) elif file_name.endswith((".mp3", ".wav")): content.append({"type": "text", "text": "Audio file url: " + file_url}) else: raise ValueError(f"Unsupported file type for file: {file_name}") human_msg = HumanMessage(content=content) try: answer = self.agent.invoke( {"messages": [human_msg]}, config={"callbacks": [langfuse_handler], "recursion_limit": self.recursion_limit} ) # for message in answer["messages"]: # message.pretty_print() # Result already printed inside assistant() node except Exception as e: print(f"Error: {e}") return answer["messages"][-1].content def run_and_submit_all( profile: gr.OAuthProfile | None): """ Fetches all questions, runs the SuperAgent 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 # 1. Instantiate Agent ( modify this part to create your agent) try: agent = SuperAgent() 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 # Read excluded task IDs from file excluded_tasks = set() with open("excluded_tasks.txt", "r") as f: for line in f: task_id = line.strip() if task_id: excluded_tasks.add(task_id) # 3. Run your Agent results_log = [] answers_payload = [] print(f"Running agent on {len(questions_data)} questions...") for idx, item in enumerate(questions_data): task_id = item.get("task_id") question_text = item.get("question") print(f"[{idx+1}/{len(questions_data)}]", end=" ") if not task_id or question_text is None: print(f"Skipping item with missing task_id or question: {item}") continue # Skip excluded tasks if task_id in excluded_tasks: print(f"Skipping excluded task: {task_id}") continue try: submitted_answer = agent(item) 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 the answer for debugging # print the timestamp print(f"Task ID: {task_id}, Submitted Answer: {submitted_answer[:50]}") 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 # Read the app description from markdown file with open("description.md", "r", encoding="utf-8") as f: description_md = f.read() # --- Build Gradio Interface using Blocks --- with gr.Blocks() as demo: gr.Markdown("# Super Agent Evaluation Runner") gr.Markdown(description_md) gr.LoginButton() run_button = gr.Button("Run Evaluation & Submit All Answers") 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, 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=True)