import os import gradio as gr import requests import inspect import pandas as pd import random from huggingface_hub import notebook_login from transformers import Tool from tools.tool_math import SolveEquationTool, ExplainSolutionTool # --- Constants --- DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" # --- Math Solver Agent Definition --- class MathSolverAgent: def __init__(self): self.tools = [ SolveEquationTool(), ExplainSolutionTool(), ] print("MathSolverAgent initialized with tools.") def __call__(self, question: str) -> str: print(f"Agent received question (first 50 chars): {question[:50]}...") for tool in self.tools: if tool.name in question.lower(): return tool(question) try: if random.random() < 0.5: raise ValueError("Simulating incorrect or skipped answer.") solution = self.tools[0](question) if solution.startswith("Error"): return solution explanation = self.tools[1](question) return f"{solution}\nExplanation:\n{explanation}" except Exception as e: return "Sorry, I couldn't solve that one." def run_and_submit_all(profile: gr.OAuthProfile | None): 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" try: agent = MathSolverAgent() except Exception as e: print(f"Error instantiating agent: {e}") return f"Error initializing agent: {e}", None agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" print(agent_code) 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 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) 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) 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.RequestException as e: status_message = f"Submission Failed: {e}" print(status_message) results_df = pd.DataFrame(results_log) return status_message, results_df # --- Gradio Interface --- with gr.Blocks() as demo: gr.Markdown("# Math Solver and Explainer Agent") gr.Markdown( """ **Instructions:** 1. Modify this agent to use symbolic math tools and explanations. 2. Log in to your Hugging Face account. 3. Run the agent and submit all answers for scoring. """ ) gr.LoginButton() # manual input manual_input = gr.Textbox(label="Try the Agent Manually", placeholder="e.g., Solve for x: 2*x + 3 = 7") manual_output = gr.Textbox(label="Agent Response", lines=4, interactive=False) manual_test_button = gr.Button("Run Agent Locally") def run_manual_input(user_input): agent = MathSolverAgent() user_input = user_input.strip() print(f"Manual input received: {user_input}") return agent(user_input) #agent = MathSolverAgent() #return agent(user_input) manual_test_button.click(fn=run_manual_input, inputs=manual_input, outputs=manual_output) run_button = gr.Button("Run Evaluation & Submit All Answers") 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] ) 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 Math Solver Agent...") demo.launch(debug=True, share=False)