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| import os | |
| import gradio as gr | |
| import requests | |
| import inspect | |
| import pandas as pd | |
| from langchain_openai import ChatOpenAI | |
| from langchain_core.messages import AnyMessage, HumanMessage, AIMessage, SystemMessage | |
| from langgraph.graph import START, StateGraph | |
| from langgraph.prebuilt import ToolNode, tools_condition | |
| from langgraph.graph.message import add_messages | |
| from typing import TypedDict, Annotated | |
| from agent_tools import image_recognition_tool, download_file_tool, reverse_text_tool, hub_stats_tool, web_search_tool | |
| # (Keep Constants as is) | |
| # --- Constants --- | |
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" | |
| # Setting up the llm | |
| llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0) | |
| tools = [web_search_tool, hub_stats_tool, download_file_tool, | |
| image_recognition_tool, reverse_text_tool] | |
| chat_with_tools = llm.bind_tools(tools) | |
| # Defining my agent | |
| class MyAgent(TypedDict): | |
| messages: Annotated[list[AnyMessage], add_messages] | |
| class BasicAgent: | |
| def __init__(self): | |
| print("BasicAgent initialized.") | |
| def __call__(self, question: str) -> str: | |
| print(f"Agent received question (first 50 chars): {question[:50]}...") | |
| # Use the LangGraph agent to process the question | |
| try: | |
| result = my_agent.invoke( | |
| {"messages": [HumanMessage(content=question)]}) | |
| # Get the last message from the result | |
| last_message = result["messages"][-1] | |
| answer = last_message.content | |
| print(f"Agent returning answer: {answer}") | |
| return answer | |
| except Exception as e: | |
| print(f"Error in agent processing: {e}") | |
| return f"Error processing question: {e}" | |
| # set the main system prompt | |
| def assistant(state: MyAgent): | |
| # Add system message to instruct the agent to use the tool | |
| system_message = SystemMessage(content="""You are a general AI assistant. I will ask you a question. | |
| Report your thoughts, and finish your answer with just the answer — no prefixes like "FINAL ANSWER:". | |
| Your answer should be a number OR as few words as possible OR a comma-separated list of numbers and/or strings. | |
| If you're asked for a number, don't use commas or units like $ or %, unless specified. | |
| If you're asked for a string, don't use articles or abbreviations (e.g. for cities), and write digits in plain text unless told otherwise. | |
| Tool Use Guidelines: | |
| 1. Do **not** use any tools outside of the provided tools list. | |
| 2. Always use **only one tool at a time** in each step of your execution. | |
| 3. For HuggingFace Hub information (models, authors, downloads), use **get_hub_stats** tool. | |
| 4. For web searches and current information, use **web_search_tool** . | |
| 5. If the question looks reversed (starts with a period or reads backward), first use **reverse_text_tool** to reverse it, then process the question. | |
| 6. When you need to download files from URLs, use **download_file_tool**. | |
| 7. For image analysis and description, use **image_recognition_tool** (requires OpenAI API key). | |
| 8. Even for complex tasks, assume a solution exists. If one method fails, try another approach using different tools. | |
| 9. Keep responses concise and efficient.""") | |
| # Combine system message with user messages | |
| all_messages = [system_message] + state["messages"] | |
| return { | |
| "messages": [chat_with_tools.invoke(all_messages)], | |
| } | |
| # define the agent graph | |
| builder = StateGraph(MyAgent) | |
| # Define nodes: these do the work | |
| builder.add_node("assistant", assistant) | |
| builder.add_node("tools", ToolNode(tools)) | |
| # Define edges: these determine how the control flow moves | |
| builder.add_edge(START, "assistant") | |
| builder.add_conditional_edges( | |
| "assistant", | |
| tools_condition, | |
| ) | |
| builder.add_edge("tools", "assistant") | |
| my_agent = builder.compile() | |
| # submit | |
| def run_and_submit_all(profile: gr.OAuthProfile | None): | |
| """ | |
| Fetches all questions, runs MyAgent on them, submits all answers, | |
| and displays the results. | |
| """ | |
| # --- Determine HF Space Runtime URL and Repo URL --- | |
| # Get the SPACE_ID for sending link to the code | |
| 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" | |
| # 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 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 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) | |