import os import gradio as gr import requests import inspect import yaml import pandas as pd from typing import Annotated, Optional from typing_extensions import TypedDict from langgraph.graph import StateGraph, START, END from langgraph.graph.message import add_messages from langchain_openai import ChatOpenAI from langgraph.prebuilt import create_react_agent from langchain_community.tools import DuckDuckGoSearchRun,DuckDuckGoSearchResults from langchain_core.messages import AnyMessage, SystemMessage, HumanMessage from langchain_community.agent_toolkits.openapi.toolkit import RequestsToolkit from langchain_community.utilities.requests import TextRequestsWrapper from langchain.agents import AgentExecutor, load_tools from langchain_community.utilities import GoogleSerperAPIWrapper from langchain_community.tools.riza.command import ExecPython os.environ["SERPER_API_KEY"] = "..." os.environ["RIZA_API_KEY"] = "..." # (Keep Constants as is) # --- Constants --- DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" vision_llm = ChatOpenAI(model="qwen2.5-vl-7b-instruct", base_url="http://172.16.216.190:1234/v1") def extract_text(img_path: str) -> str: """ Extract text from an image file using a multimodal model. Master Wayne often leaves notes with his training regimen or meal plans. This allows me to properly analyze the contents. """ all_text = "" 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") # Prepare the prompt including the base64 image data message = [ HumanMessage( content=[ { "type": "text", "text": ( "Extract all the text from this image. " "Return only the extracted text, no explanations." ), }, { "type": "image_url", "image_url": { "url": f"data:image/png;base64,{image_base64}" }, }, ] ) ] # Call the vision-capable model response = vision_llm.invoke(message) # Append extracted text all_text += response.content + "\n\n" return all_text.strip() except Exception as e: # A butler should handle errors gracefully error_msg = f"Error extracting text: {str(e)}" print(error_msg) return "" # --- Basic Agent Definition --- class State(TypedDict): # Messages have the type "list". The `add_messages` function # in the annotation defines how this state key should be updated # (in this case, it appends messages to the list, rather than overwriting them) messages: Annotated[list, add_messages] input_file: Optional[str] # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------ class BasicAgent: def __init__(self): # model = ChatOpenAI( # # model="qwen3-30b-a3b-mlx", # model="meta-llama-3.1-8b-instruct", # base_url="http://192.168.1.82:1234/v1", # temperature=0, # api_key="not-needed" # ) toolkit = RequestsToolkit( requests_wrapper=TextRequestsWrapper(headers={}), allow_dangerous_requests=True, ) tools = [extract_text, ExecPython()] + toolkit.get_tools() + load_tools(["google-serper"]) self.agent = create_react_agent( model="gemini-2.0-flash", tools=tools ) print("BasicAgent initialized.") def __call__(self, question: str, file: str, taskId: str): print(f"Agent received question (first 100 chars): {question[:100]}...") if file : question = question + f" You can donwload the file associated at {DEFAULT_API_URL}/files/{taskId}" result = self.agent.invoke({"messages": [HumanMessage(content=question)]}) answer = result['messages'][-1].content 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. """ os.environ["HF_TOKEN"] = "..." # --- 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" # questions_url = f"{api_url}/random-question" 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") question_file = item.get("file_name") 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, question_file, task_id) answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer}) print(f"Question: {item}, 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)