import os import gradio as gr import requests import inspect import pandas as pd import json import copy from basic_agent import ToolAgent from tools import ( smart_read_file, search_and_extract, search_and_extract_from_wikipedia, aggregate_information, extract_clean_text_from_url, youtube_search_tool, load_youtube_transcript, get_audio_from_youtube, image_query_tool, transcribe_audio, ) TOOLS = [ smart_read_file, search_and_extract, search_and_extract_from_wikipedia, aggregate_information, extract_clean_text_from_url, youtube_search_tool, load_youtube_transcript, get_audio_from_youtube, image_query_tool, transcribe_audio, ] tool_names = [tool.name if hasattr(tool, "name") else str(tool) for tool in TOOLS] print(json.dumps(tool_names, indent=2)) DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" TOOL_USE_SYS_PROMPT = """ You are a helpful AI assistant operating in a structured reasoning and action loop using the ReAct pattern. Your reasoning loop consists of: - Question: the input task you must solve - Thought: Reflect on the task and decide what to do next. - Action: Choose one of the following actions: - Solve it directly using your own knowledge - Break the problem into smaller steps - Use a tool to get more information - Action Input: Provide input for the selected action - Observation: Record the result of the action and/or aggregate information from previous observations (summarize, count, analyse, ...). (Repeat Thought/Action/Action Input/Observation as needed) Terminate your loop with: - Thought: I now know the final answer - Final Answer: [your best answer to the original question] **General Execution Rules:** - If you can answer using only your trained knowledge, do so directly without using tools. - If the question involves image content, use the `image_query_tool`: - Action: image_query_tool - Action Input: 'image_path': [image_path], 'question': [user's question about the image] **Tool Use Constraints:** - Never use any tool more than **3 consecutive times** without either: - Aggregating the information received so far: you can call the `summarize_search_results` tool and analyze the tool outputs to answer the question. - If you need more information, use a different tool or break the problem down further, but do not return a final answer yet. - Do not exceed **5 total calls** to *search-type tools* per query (such as `search_and_extract`, `search_and_extract_from_wikipedia`, `extract_clean_text_from_url`). - Do not ask the user for additional clarification or input. Work with only what is already provided. **If you are unable to answer:** - If neither your knowledge nor tool outputs yield useful information: - Use the output tools the best you can to answer the question, even if it's not perfect. If not, say: > Final Answer: I could not find any useful information to answer your query. - If the question is unanswerable due to lack of input (e.g., missing attachment) or is fundamentally outside your scope, say: > Final Answer: I don't have the ability to answer this query: [brief reason] Always aim to provide the **best and most complete** answer based on your trained knowledge and the tools available. """ def run_and_submit_all( profile: gr.OAuthProfile | None): """ Fetches all questions, runs the ToolAgent 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, gr.update(interactive=False) api_url = DEFAULT_API_URL questions_url = f"{api_url}/questions" files_url = "{}/files/{}" # GET /files/{task_id} submit_url = f"{api_url}/submit" # 1. Instantiate Agent ( modify this part to create your agent) try: agent = ToolAgent( tools=TOOLS, backstory=TOOL_USE_SYS_PROMPT ) agent.initialize() except Exception as e: print(f"Error instantiating agent: {e}") return f"Error initializing agent: {e}", None, gr.update(interactive=False) # 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, gr.update(interactive=False) 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, gr.update(interactive=False) 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, gr.update(interactive=False) except Exception as e: print(f"An unexpected error occurred fetching questions: {e}") return f"An unexpected error occurred fetching questions: {e}", None, gr.update(interactive=False) # 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") question_level = item.get("Level") question_file_name = item.get("file_name", None) print("\nquestion level: ", question_level) print("task_id: ", task_id) print("question file_name: ", question_file_name) if question_file_name: file_url = files_url.format(api_url, task_id) print("file_url: ", file_url) file_response = requests.get(file_url, timeout=15) file_response.raise_for_status() print("file_response: ", file_response.content[0:50]) save_path = os.path.join("/tmp", question_file_name) print("save_path: ", save_path) with open(save_path, "wb") as f: f.write(file_response.content) print(f"✅ file saved to: {save_path}") found= False metadata = {} for root, dirs, files in os.walk("/"): if question_file_name in files: file_path = os.path.join(root, question_file_name) print("file found at: ", file_path) metadata = {'image_path': file_path} if '.png' in question_file_name else {'file_path': file_path} found=True if question_file_name and not found: print("FileNotFoundError: try making an api request to .files/ or ./static in the hf.space target (or check it manually first)") break if not task_id or question_text is None: print(f"Skipping item with missing task_id or question: {item}") continue try: q_data = {'query': question_text, 'metadata': metadata} submitted_answer = agent(q_data) # todo: send more data (files) 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), gr.update(interactive=False) # 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() log_file_dict = copy.deepcopy(results_log) log_file_dict.append({'result_data': result_data}) with open("results_log.json", "w") as results_session_file: json.dump(log_file_dict, results_session_file) 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, gr.update(interactive=True) 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, gr.update(interactive=False) 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, gr.update(interactive=False) 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, gr.update(interactive=False) 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, gr.update(interactive=False) def download_log(): return "results_log.json" # Gradio App with gr.Blocks() as demo: gr.Markdown("# Basic Agent Evaluation Runner") gr.LoginButton() run_button = gr.Button("Run Evaluation & Submit All Answers") download_button = gr.Button("Download Evaluation Log", interactive=False) 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, download_button] ) file_output = gr.File(label="Download Log File", visible=True) download_button.click( fn=download_log, outputs=file_output ) 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)