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| import os | |
| import gradio as gr | |
| import requests | |
| import pandas as pd | |
| from transformers import Tool, HfAgent | |
| from datetime import datetime | |
| import random | |
| import wikipediaapi | |
| import wolframalpha | |
| # --- Constants --- | |
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" | |
| # --- Enhanced Agent Definition --- | |
| class EnhancedAgent: | |
| def __init__(self): | |
| print("EnhancedAgent initialized with tools.") | |
| # Initialize tools | |
| self.tools = { | |
| "calculator": self.calculator, | |
| "time": self.get_current_time, | |
| "wikipedia": self.wikipedia_search, | |
| "random_choice": self.random_choice | |
| } | |
| # Initialize external APIs (would need proper API keys in production) | |
| self.wiki_wiki = wikipediaapi.Wikipedia('en') | |
| self.wolfram_client = wolframalpha.Client('YOUR_WOLFRAM_APP_ID') # Replace with actual ID | |
| def calculator(self, expression: str) -> str: | |
| """Evaluate mathematical expressions""" | |
| try: | |
| return str(eval(expression)) | |
| except: | |
| return "Error: Could not evaluate the expression" | |
| def get_current_time(self) -> str: | |
| """Get current UTC time""" | |
| return datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S UTC") | |
| def wikipedia_search(self, term: str) -> str: | |
| """Search Wikipedia for information""" | |
| page = self.wiki_wiki.page(term) | |
| if page.exists(): | |
| return page.summary[:500] # Return first 500 chars of summary | |
| return f"No Wikipedia page found for '{term}'" | |
| def random_choice(self, items: str) -> str: | |
| """Randomly select from a list of comma-separated items""" | |
| try: | |
| options = [x.strip() for x in items.split(",")] | |
| return f"I choose: {random.choice(options)}" | |
| except: | |
| return "Error: Please provide comma-separated options" | |
| def __call__(self, question: str) -> str: | |
| print(f"Agent processing question: {question[:100]}...") | |
| # Simple question classification and routing | |
| question_lower = question.lower() | |
| # Math questions | |
| if any(word in question_lower for word in ["calculate", "what is", "how much is", "+", "-", "*", "/"]): | |
| try: | |
| # Extract math expression | |
| expr = question.replace("?", "").replace("what is", "").replace("calculate", "").strip() | |
| return self.tools["calculator"](expr) | |
| except: | |
| pass | |
| # Time questions | |
| if any(word in question_lower for word in ["time", "current time", "what time is it"]): | |
| return self.tools["time"]() | |
| # Wikipedia questions | |
| if any(word in question_lower for word in ["who is", "what is a", "tell me about", "explain"]): | |
| # Extract search term | |
| term = question.replace("?", "").replace("who is", "").replace("what is a", "").replace("tell me about", "").strip() | |
| return self.tools["wikipedia"](term) | |
| # Random choice questions | |
| if " or " in question_lower and not any(word in question_lower for word in ["who", "what", "when", "where", "why", "how"]): | |
| return self.tools["random_choice"](question.replace("?", "").replace(" or ", ",")) | |
| # Fallback to HF Agent for complex questions | |
| try: | |
| agent = HfAgent( | |
| "https://api-inference.huggingface.co/models/bigcode/starcoder", | |
| max_new_tokens=150, | |
| temperature=0.5 | |
| ) | |
| return agent.run(question) | |
| except: | |
| return "I couldn't find an answer to that question." | |
| def run_and_submit_all(profile: gr.OAuthProfile | None): | |
| """ | |
| Fetches all questions, runs the EnhancedAgent 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 | |
| try: | |
| agent = EnhancedAgent() | |
| 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) | |
| # 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("# Enhanced Agent Evaluation Runner") | |
| gr.Markdown( | |
| """ | |
| **Instructions:** | |
| 1. Log in to your Hugging Face account using the button below. | |
| 2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run the enhanced agent, submit answers, and see the score. | |
| This agent includes: | |
| - Math calculation capabilities | |
| - Time lookup | |
| - Wikipedia integration | |
| - Random choice selection | |
| - Fallback to HF's StarCoder model for complex questions | |
| """ | |
| ) | |
| gr.LoginButton() | |
| 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") | |
| if space_id_startup: | |
| print(f"✅ SPACE_ID found: {space_id_startup}") | |
| print(f" Repo URL: https://huggingface.co/spaces/{space_id_startup}") | |
| print("-"*(60 + len(" App Starting ")) + "\n") | |
| print("Launching Gradio Interface for Enhanced Agent Evaluation...") | |
| demo.launch(debug=True, share=False) |