import asyncio import inspect import json import os import time from typing import Any, Dict, List, Optional import gradio as gr import pandas as pd import requests from dotenv import load_dotenv from langchain_community.chat_models import ChatHuggingFace from langchain_community.llms import HuggingFaceEndpoint from langchain_core.messages import AIMessage, HumanMessage from langchain_core.tools import StructuredTool from tools import (absolute, add, divide, exponential, floor_divide, get_current_time_in_timezone, logarithm, modulus, multiply, power, roman_calculator_converter, square_root, subtract, web_search) # --- Constants --- DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" MAX_AGENT_ITERATIONS = 15 MAX_CONCURRENT_REQUESTS = 5 # Limit concurrent requests to avoid overwhelming the API load_dotenv() HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN") or os.getenv("HF_TOKEN") # Quick test to see if tokens are available. print(f"Available env vars: {[k for k in os.environ.keys() if 'TOKEN' in k or 'HF' in k]}") # Global cache for answers answer_cache = {} class ImprovedAgent: def __init__(self): if not HUGGINGFACEHUB_API_TOKEN: raise ValueError("Missing Hugging Face API token. Please set HUGGINGFACEHUB_API_TOKEN.") print("ImprovedAgent initialized.") # Initialize LLM with better parameters self.llm = HuggingFaceEndpoint( repo_id="Qwen/Qwen2.5-Coder-32B-Instruct", huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN, temperature=0.1, # Lower temperature for more consistent responses max_new_tokens=1024, timeout=30, ) self.chat = ChatHuggingFace(llm=self.llm, verbose=False) # Initialize tools self.tools = [ multiply, add, subtract, power, divide, modulus, square_root, floor_divide, absolute, logarithm, exponential, web_search, roman_calculator_converter, get_current_time_in_timezone ] self.chat_with_tools = self.chat.bind_tools(self.tools) print(f"Total tools available: {len(self.tools)}") # Create tool mapping for easier access self.tool_map = {tool.name: tool for tool in self.tools} def _extract_tool_calls(self, response) -> List[Dict]: """Extract tool calls from the response""" tool_calls = [] if hasattr(response, 'tool_calls') and response.tool_calls: for tool_call in response.tool_calls: tool_calls.append({ 'name': tool_call['name'], 'args': tool_call['args'] }) return tool_calls def _execute_tool_calls(self, tool_calls: List[Dict]) -> List[str]: """Execute tool calls and return results""" results = [] for tool_call in tool_calls: tool_name = tool_call['name'] tool_args = tool_call['args'] if tool_name in self.tool_map: try: tool = self.tool_map[tool_name] result = tool.invoke(tool_args) results.append(f"Tool {tool_name} result: {result}") except Exception as e: results.append(f"Tool {tool_name} error: {str(e)}") else: results.append(f"Unknown tool: {tool_name}") return results async def answer(self, question: str) -> str: """Improved answer method with better error handling and tool usage""" print(f"Processing question: {question[:100]}...") try: # Create system prompt for better instruction following system_prompt = """You are a helpful AI assistant with access to various tools. When answering questions, use the appropriate tools when needed and provide clear, concise answers. If you need to perform calculations, use the math tools available. If you need current information, use the web search tool. Always provide a final answer after using tools.""" messages = [ HumanMessage(content=f"{system_prompt}\n\nQuestion: {question}") ] # Initial response response = await asyncio.to_thread(self.chat_with_tools.invoke, messages) # Handle tool calls if present max_iterations = 3 iteration = 0 while iteration < max_iterations: tool_calls = self._extract_tool_calls(response) if not tool_calls: break # Execute tool calls tool_results = self._execute_tool_calls(tool_calls) # Add tool results to conversation messages.append(AIMessage(content=response.content)) messages.append(HumanMessage(content=f"Tool results: {'; '.join(tool_results)}. Please provide a final answer based on these results.")) # Get next response response = await asyncio.to_thread(self.chat_with_tools.invoke, messages) iteration += 1 # Extract final answer final_answer = response.content.strip() # Clean up the response - remove any tool call artifacts if "Tool " in final_answer and "result:" in final_answer: # Try to extract just the final answer part lines = final_answer.split('\n') for line in reversed(lines): if line.strip() and not line.startswith('Tool ') and not 'result:' in line: final_answer = line.strip() break return final_answer except Exception as e: print(f"Error in answer method: {e}") return f"Error processing question: {str(e)}" def answer_sync(self, question: str) -> str: """Synchronous version of answer method""" try: return asyncio.run(self.answer(question)) except Exception as e: print(f"Error in sync answer: {e}") return f"Error: {str(e)}" async def process_questions_batch(agent, questions_batch, semaphore): """Process a batch of questions with rate limiting""" results = [] async def process_single_question(task_id, question): async with semaphore: try: # Check cache first cache_key = f"{task_id}_{hash(question)}" if cache_key in answer_cache: print(f"Using cached answer for task {task_id}") return task_id, question, answer_cache[cache_key], None answer = await agent.answer(question) # Cache the result answer_cache[cache_key] = answer return task_id, question, answer, None except Exception as e: print(f"Error processing task {task_id}: {e}") return task_id, question, None, str(e) # Create semaphore for rate limiting tasks = [] for item in questions_batch: task_id = item.get("task_id") question_text = item.get("question") if task_id and question_text is not None: tasks.append(process_single_question(task_id, question_text)) if tasks: results = await asyncio.gather(*tasks, return_exceptions=True) return results async def run_agent_async_improved(agent, questions_data): """Improved async processing with batching and caching""" results_log, answers_payload = [], [] # Create semaphore for rate limiting semaphore = asyncio.Semaphore(MAX_CONCURRENT_REQUESTS) # Process questions in batches batch_size = 10 batches = [questions_data[i:i + batch_size] for i in range(0, len(questions_data), batch_size)] print(f"Processing {len(questions_data)} questions in {len(batches)} batches...") for i, batch in enumerate(batches): print(f"Processing batch {i+1}/{len(batches)} ({len(batch)} questions)...") try: batch_results = await process_questions_batch(agent, batch, semaphore) for result in batch_results: if isinstance(result, Exception): print(f"Batch processing error: {result}") continue task_id, question, answer, error = result if error: print(f"Error in task {task_id}: {error}") results_log.append({ "Task ID": task_id, "Question": question[:100] + "..." if len(question) > 100 else question, "Submitted Answer": f"ERROR: {error}" }) else: answers_payload.append({"task_id": task_id, "submitted_answer": answer}) results_log.append({ "Task ID": task_id, "Question": question[:100] + "..." if len(question) > 100 else question, "Submitted Answer": answer[:200] + "..." if len(answer) > 200 else answer }) # Small delay between batches to be respectful if i < len(batches) - 1: await asyncio.sleep(1) except Exception as e: print(f"Error processing batch {i+1}: {e}") # Continue with next batch continue return results_log, answers_payload def cache_answers(profile: gr.OAuthProfile | None): """Cache answers without submitting""" if not profile: return "Please log in to Hugging Face first.", None username = profile.username print(f"Caching answers for user: {username}") # Fetch questions api_url = DEFAULT_API_URL questions_url = f"{api_url}/questions" try: response = requests.get(questions_url, timeout=15) response.raise_for_status() questions_data = response.json() if not questions_data: return "No questions found.", None print(f"Fetched {len(questions_data)} questions for caching.") # Initialize agent try: agent = ImprovedAgent() except Exception as e: print(f"Full error details: {e}") return f"Error initializing agent: {e}", None # Process questions results_log, answers_payload = asyncio.run(run_agent_async_improved(agent, questions_data)) # Store in global cache with username answer_cache[f"user_{username}"] = answers_payload status = f"Cached {len(answers_payload)} answers for user {username}. Ready to submit!" results_df = pd.DataFrame(results_log) return status, results_df except Exception as e: print(f"Error caching answers: {e}") return f"Error caching answers: {e}", None def submit_cached_answers(profile: gr.OAuthProfile | None): """Submit previously cached answers""" if not profile: return "Please log in to Hugging Face first.", None username = profile.username cache_key = f"user_{username}" if cache_key not in answer_cache: return "No cached answers found. Please run 'Cache Answers' first.", None answers_payload = answer_cache[cache_key] if not answers_payload: return "No answers to submit.", None # Get space info space_id = os.getenv("SPACE_ID") agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "Unknown" # Submit api_url = DEFAULT_API_URL submit_url = f"{api_url}/submit" submission_data = { "username": username.strip(), "agent_code": agent_code, "answers": answers_payload } try: print(f"Submitting {len(answers_payload)} cached answers...") 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.')}" ) # Clear cache after successful submission if cache_key in answer_cache: del answer_cache[cache_key] return final_status, None except Exception as e: print(f"Submission error: {e}") return f"Submission failed: {e}", None 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 ( 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: # Using the retry function instead of direct request response = make_request_with_retry(questions_url) 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: # Using the retry function for submission as well response = make_request_with_retry(submit_url, method="post", json_data=submission_data, timeout=60) 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)