Add new tools and functionalities for audio transcription, code execution, document handling, image processing, and mathematical operations
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| """ Basic Agent Evaluation Runner""" | |
| import os | |
| import inspect | |
| import gradio as gr | |
| import requests | |
| import pandas as pd | |
| from langchain_core.messages import HumanMessage | |
| from agents.agent import build_graph | |
| from api_integration import GAIAApiClient | |
| import tempfile | |
| import mimetypes # Added for MIME type detection | |
| import base64 # Added for base64 encoding images | |
| # (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: | |
| """A langgraph agent.""" | |
| def __init__(self): | |
| print("BasicAgent initialized.") | |
| self.graph = build_graph() | |
| def __call__(self, messages: list) -> str: # Modified to accept a list of messages | |
| print(f"Agent received messages: {messages}") | |
| # Ensure messages are in the correct format for the graph | |
| processed_messages = self.graph.invoke({"messages": messages}) | |
| # The final answer should be in the 'content' of the last message | |
| raw_answer = processed_messages['messages'][-1].content | |
| # Attempt to find "FINAL ANSWER:" and extract text after it | |
| final_answer_marker = "FINAL ANSWER:" | |
| marker_index = raw_answer.rfind(final_answer_marker) # Use rfind to get the last occurrence | |
| if marker_index != -1: | |
| # Extract the text after "FINAL ANSWER: " | |
| extracted_answer = raw_answer[marker_index + len(final_answer_marker):].strip() | |
| # If there's a newline after the extracted answer, take only the first line | |
| # This handles cases where the LLM might add extra explanations after the marker on a new line | |
| first_line_of_extracted_answer = extracted_answer.split('\\n')[0].strip() | |
| if first_line_of_extracted_answer: # Ensure it's not empty after stripping | |
| print(f"Extracted answer: {first_line_of_extracted_answer}") | |
| return first_line_of_extracted_answer | |
| else: # If the first line is empty, it might be that the answer is just the marker itself (unlikely but handle) | |
| print(f"Warning: Extracted answer after '{final_answer_marker}' is empty. Returning raw answer part after marker if any, or full raw answer.") | |
| # Fallback to extracted_answer if first_line was empty but extracted_answer was not | |
| return extracted_answer if extracted_answer else raw_answer | |
| # Fallback if "FINAL ANSWER:" is not found or extraction results in empty string | |
| print(f"Warning: '{final_answer_marker}' not found in agent's output or extraction failed. Returning raw answer: {raw_answer}") | |
| return raw_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. | |
| """ | |
| # --- 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 | |
| submit_url = f"{api_url}/submit" | |
| gaia_client = GAIAApiClient(api_url=api_url) | |
| # 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 using GAIAApiClient from: {api_url}") | |
| try: | |
| questions_data = gaia_client.get_questions() | |
| 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 via GAIAApiClient: {e}") | |
| return f"Error fetching questions: {e}", None | |
| except Exception as e: | |
| print(f"An unexpected error occurred fetching questions via GAIAApiClient: {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") | |
| original_file_name = item.get("file_name") | |
| content_parts = [{"type": "text", "text": question_text}] | |
| downloaded_file_path_for_log = None # For logging purposes | |
| if task_id and original_file_name: | |
| print(f"Question {task_id} has an associated file: {original_file_name}. Attempting to download.") | |
| try: | |
| file_bytes = gaia_client.get_file(task_id) | |
| if file_bytes: | |
| temp_dir = tempfile.gettempdir() | |
| safe_original_filename = "".join(c if c.isalnum() or c in ('.', '_', '-') else '_' for c in original_file_name) | |
| temp_file_name = f"task_{task_id}_{safe_original_filename}" | |
| downloaded_file_path = os.path.join(temp_dir, temp_file_name) | |
| downloaded_file_path_for_log = downloaded_file_path | |
| with open(downloaded_file_path, "wb") as f_out: | |
| f_out.write(file_bytes) | |
| print(f"File for task {task_id} downloaded to: {downloaded_file_path}") | |
| # Determine MIME type and construct message part | |
| mime_type, _ = mimetypes.guess_type(downloaded_file_path) | |
| if mime_type and mime_type.startswith("image/"): | |
| base64_image = base64.b64encode(file_bytes).decode('utf-8') | |
| content_parts.append({ | |
| "type": "image_url", | |
| "image_url": { | |
| "url": f"data:{mime_type};base64,{base64_image}" | |
| } | |
| }) | |
| current_question_for_log = f"{question_text}\n\n[System Note: Image file {original_file_name} ({mime_type}) was processed and included directly in the message.]" | |
| # elif mime_type and mime_type.startswith("audio/"): | |
| # # For audio, tools might expect a path or raw bytes. | |
| # # For now, let's add a note with the path, assuming tools can handle it. | |
| # # This part might need adjustment based on specific audio tool capabilities. | |
| # content_parts.append({ | |
| # "type": "text", # Or a custom type if LangGraph/tools support it | |
| # "text": f"[System Note: An audio file '{original_file_name}' is available at: {downloaded_file_path}]" | |
| # }) | |
| # current_question_for_log = f"{question_text}\n\n[System Note: Audio file {original_file_name} available at {downloaded_file_path}]" | |
| else: # For other file types (text, csv, py, etc.) | |
| # Add a system note with the file path. Tools will need to be able | |
| # to read the file from this path. | |
| content_parts.append({ | |
| "type": "text", | |
| "text": f"[System Note: An associated file '{original_file_name}' ({mime_type if mime_type else 'unknown type'}) has been downloaded. It is available at: {downloaded_file_path}]" | |
| }) | |
| current_question_for_log = f"{question_text}\n\n[System Note: File {original_file_name} ({mime_type if mime_type else 'unknown type'}) available at {downloaded_file_path}]" | |
| else: | |
| print(f"Warning: File indicated for task {task_id} ('{original_file_name}'), but download returned no content.") | |
| content_parts.append({"type": "text", "text": f"[System Note: A file ('{original_file_name}') was indicated for this question, but the download attempt returned no content.]"}) | |
| current_question_for_log = f"{question_text}\n\n[System Note: File {original_file_name} download returned no content.]" | |
| except Exception as e_file: | |
| print(f"Error downloading or processing file '{original_file_name}' for task {task_id}: {e_file}") | |
| content_parts.append({"type": "text", "text": f"[System Note: An error occurred while trying to download/process the associated file ('{original_file_name}') for this question: {e_file}]"}) | |
| current_question_for_log = f"{question_text}\n\n[System Note: Error with file {original_file_name}: {e_file}]" | |
| else: | |
| current_question_for_log = question_text # No file associated | |
| if not task_id or question_text is None: | |
| print(f"Skipping item with missing task_id or question: {item}") | |
| continue | |
| try: | |
| # The agent now expects a list of content parts | |
| human_message = HumanMessage(content=content_parts) | |
| submitted_answer = agent([human_message]) # Pass as a list of messages | |
| answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer}) | |
| results_log.append({"Task ID": task_id, "Question": current_question_for_log, "File Path": downloaded_file_path_for_log if downloaded_file_path_for_log else "N/A", "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": current_question_for_log, "File Path": downloaded_file_path_for_log if downloaded_file_path_for_log else "N/A", "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 | |
| # --- Build Gradio Interface using Blocks --- | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Basic Agent Evaluation Runner") | |
| gr.Markdown( | |
| """ | |
| **Instructions:** | |
| 1. Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ... | |
| 2. Log in to your Hugging Face account using the button below. This uses your HF username for submission. | |
| 3. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score. | |
| --- | |
| **Disclaimers:** | |
| Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions). | |
| This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async. | |
| """ | |
| ) | |
| 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=False) |