import os import getpass import regex as re import gradio as gr import requests import pandas as pd import base64 from typing import TypedDict, Annotated from langchain_core.messages import AnyMessage, SystemMessage, HumanMessage from langchain_community.tools import DuckDuckGoSearchRun from langchain.tools import Tool from langgraph.graph import START, StateGraph from langgraph.graph.message import add_messages from langgraph.prebuilt import ToolNode, tools_condition from langchain_mistralai import ChatMistralAI import getpass import os if "Mistral" not in os.environ: os.environ["Mistral"] = getpass.getpass("Enter your Mistral API key: ") print("Loading LLM...") chat = ChatMistralAI( model="mistral-large-latest", mistral_api_key = os.getenv("Mistral") ) print(f"Model {chat.model} downloaded!") # (Keep Constants as is) # --- Constants --- DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" def get_file_path(task_id: str, question) -> str: """Retrieves reference file path.""" if question['task_id'] == task_id: return question['file_path'] def get_ref_content(path: str) -> str | object: """Retrieves content from the reference path provided.""" with open(path, "rb") as f: file = f.read() return file def search_web(topic: str) -> str: """Retrieves information about the topic.""" results = DuckDuckGoSearchRun().invoke(topic) if results: return "\n\n".join([doc.text for doc in results[:2]]) else: return "No matching content found." def extract_text_from_image(img_path: str) -> str: """Extracts text from image""" try: # Read image and encode as base64 with open(img_path, "rb") as image_file: image_bytes = image_file.read() image_base64 = base64.b64encode(image_bytes).decode("utf-8") return image_base64 except Exception as e: # A butler should handle errors gracefully error_msg = f"Error extracting text: {str(e)}" print(error_msg) return "" # Initialize the tool get_file_path_tool = Tool( name="file_path_retriever", func=get_file_path, description="Retrieves path to the reference file." ) get_content_tool = Tool( name="ref_content_retriever", func=get_ref_content, description="Retrieves reference file content." ) search_web_tool = Tool( name="search_web_retriever", func=search_web, description="Retrieves online info about a specific topic." ) extract_text_tool = Tool( name="extract_text_retriever", func=extract_text_from_image, description="Retrieves text from an image." ) tools = [get_file_path_tool, get_content_tool] chat_with_tools = chat.bind_tools(tools, parallel_tool_calls=False) # Generate the AgentState and Agent graph class AgentState(TypedDict): messages: Annotated[list[AnyMessage], add_messages] def assistant(state: AgentState): return { "messages": chat.invoke(state["messages"]), } # The graph builder = StateGraph(AgentState) # Define nodes: these do the work builder.add_node("assistant", assistant) builder.add_node("tools", ToolNode([get_file_path_tool, get_content_tool, extract_text_tool, search_web_tool])) # Define edges: these determine how the control flow moves builder.add_edge(START, "assistant") builder.add_conditional_edges( "assistant", # If the latest message requires a tool, route to tools # Otherwise, provide a direct response tools_condition ) builder.add_edge("tools", "assistant") alfred = builder.compile() system_prompt = SystemMessage( content="You are a general AI assistant. \ I will ask you a question. Report your thoughts shortly, \ and finish your answer with the following template: \ FINAL ANSWER: [YOUR FINAL ANSWER]. \ YOUR FINAL ANSWER should be a number OR as few words as possible \ OR a comma separated list of numbers and/or strings. \ If you are asked for a number, use only digits in your final answer. \ Don't use comma nor brackets to write your number neither use units such as $ or percent sign \ unless specified otherwise. \ If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), \ and write the digits in plain text unless specified otherwise. \ If you are asked for a comma separated list, apply the above rules \ depending of whether the element to be put in the list is a number or a string. \ If the question refers to an external content and there is no reference file attached, \ perform a web search and retrieve relevant information from the internet. \ Make sure that each final answer is preceded with 'FINAL ANSWER:'. " ) class BasicAgent: def __init__(self): print("BasicAgent initialized.") def __call__(self, question: str) -> str: print(f"Agent received question (first 50 chars): {question[:50]}...") message = HumanMessage(content=question) print(message) answer = alfred.invoke(input={"messages": [system_prompt, message]},config={"recursion_limit": 3})['messages'][-1].content print(answer) #answer = "".join(re.findall(r'(FINAL ANSWER:.*)', answer, flags=re.M)) answer = answer.replace('FINAL ANSWER: ', '') answer = answer.replace('[', '') nswer = answer.replace('*', '') fixed_answer = answer.replace(']', '') print(f"Agent returning fixed answer: {fixed_answer}") return fixed_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 questions_url = f"{api_url}/questions" submit_url = f"{api_url}/submit" # 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 ( useful 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 # 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") 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) # 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 separate 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)