import os import gradio as gr import requests import pandas as pd from smolagents import ( CodeAgent, OpenAIServerModel, GoogleSearchTool, ) from tools import read_image, transcribe_audio, run_video, search_wikipedia, read_code DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" model_id = "gpt-4o-mini" class BasicAgent: def __init__(self, model_id=model_id): model = OpenAIServerModel(model_id=model_id, temperature=0.1) google_search = GoogleSearchTool() self.agent = CodeAgent( model=model, tools=[ read_image, transcribe_audio, read_code, run_video, search_wikipedia, google_search, ], additional_authorized_imports=["numpy", "pandas"], max_steps=20, ) add_sys_prompt = f"""\n\nIf a file_url is available or an url is given in question statement, then request and use the content to answer the question. \ If a code file, such as .py file, is given, do not attempt to execute it but rather open it as a text file and analyze the content. \ When a tabluar file, such as csv, tsv, xlsx, is given, read it using pandas. Make sure you provide the answer in accordance with the instruction provided in the question. Do not return the result of tool as a final_answer. Do Not add any additional information, explanation, unnecessary words or symbols. The answer is likely as simple as one word.""" self.agent.prompt_templates["system_prompt"] += add_sys_prompt def __call__(self, question: str) -> str: answer = self.agent.run(question) return answer 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" 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) 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 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_name = item.get("file_name") if file_name: file_url = f"{DEFAULT_API_URL}/files/{task_id}" else: file_url = "No URL provided" extension = file_name.split(".")[-1] question_text += f"\n\nfile_url : {file_url} \nfile_extension : {extension}" 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) 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) 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 with gr.Blocks() as demo: gr.Markdown("# Evaluation") 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}") 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(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)