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| import os | |
| import re | |
| import tempfile | |
| from pathlib import Path | |
| from typing import Optional | |
| import gradio as gr | |
| import pandas as pd | |
| import requests | |
| from smolagents import CodeAgent, DuckDuckGoSearchTool, VisitWebpageTool | |
| try: | |
| from smolagents import OpenAIServerModel | |
| except ImportError: # Older smolagents versions may not expose this class. | |
| OpenAIServerModel = None | |
| try: | |
| from smolagents import LiteLLMModel | |
| except ImportError: | |
| LiteLLMModel = None | |
| from smolagents import InferenceClientModel | |
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" | |
| class BasicAgent: | |
| def __init__(self): | |
| print("BasicAgent initialized.") | |
| self.model = self._build_model() | |
| self.agent = CodeAgent( | |
| tools=[ | |
| DuckDuckGoSearchTool(), | |
| VisitWebpageTool(), | |
| ], | |
| model=self.model, | |
| add_base_tools=True, | |
| additional_authorized_imports=[ | |
| "collections", | |
| "csv", | |
| "datetime", | |
| "fractions", | |
| "itertools", | |
| "json", | |
| "math", | |
| "mutagen", | |
| "numpy", | |
| "os", | |
| "pandas", | |
| "pathlib", | |
| "PIL", | |
| "PIL.Image", | |
| "pypdf", | |
| "re", | |
| "statistics", | |
| "string", | |
| "time", | |
| "unicodedata", | |
| "wave", | |
| "zipfile", | |
| ], | |
| max_steps=int(os.getenv("AGENT_MAX_STEPS", "10")), | |
| verbosity_level=1, | |
| ) | |
| def _build_model(self): | |
| """Prefer non-HF paid routes, then fall back to HF Inference Providers.""" | |
| if os.getenv("OPENAI_API_KEY") and OpenAIServerModel is not None: | |
| print("Using OpenAI-compatible model.") | |
| return OpenAIServerModel( | |
| model_id=os.getenv("OPENAI_MODEL", "gpt-4o-mini"), | |
| api_base=os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"), | |
| api_key=os.environ["OPENAI_API_KEY"], | |
| temperature=0, | |
| max_tokens=4096, | |
| ) | |
| if os.getenv("OPENAI_API_KEY") and LiteLLMModel is not None: | |
| print("Using OpenAI model through LiteLLM.") | |
| return LiteLLMModel( | |
| model_id=os.getenv("OPENAI_MODEL", "gpt-4o-mini"), | |
| api_key=os.environ["OPENAI_API_KEY"], | |
| temperature=0, | |
| max_tokens=4096, | |
| ) | |
| if os.getenv("GROQ_API_KEY") and OpenAIServerModel is not None: | |
| print("Using Groq OpenAI-compatible model.") | |
| return OpenAIServerModel( | |
| model_id=os.getenv("GROQ_MODEL", "llama-3.3-70b-versatile"), | |
| api_base="https://api.groq.com/openai/v1", | |
| api_key=os.environ["GROQ_API_KEY"], | |
| temperature=0, | |
| max_tokens=4096, | |
| flatten_messages_as_text=True, | |
| ) | |
| if os.getenv("GROQ_API_KEY") and LiteLLMModel is not None: | |
| print("Using Groq model through LiteLLM.") | |
| return LiteLLMModel( | |
| model_id=f"groq/{os.getenv('GROQ_MODEL', 'llama-3.3-70b-versatile')}", | |
| "Final answer:", | |
| "Final Answer:", | |
| "FINAL ANSWER:", | |
| "Answer:", | |
| "The answer is", | |
| "The final answer is", | |
| ] | |
| for prefix in prefixes: | |
| if answer.startswith(prefix): | |
| answer = answer[len(prefix):].strip() | |
| answer = answer.strip().strip('"').strip("'").strip() | |
| return answer | |
| def run_and_submit_all(profile: gr.OAuthProfile | None): | |
| space_id = os.getenv("SPACE_ID") | |
| username = profile.username if profile and profile.username else os.getenv("HF_USERNAME", "GionaZardini") | |
| print(f"Using username: {username}") | |
| api_url = DEFAULT_API_URL | |
| questions_url = f"{api_url}/questions" | |
| submit_url = f"{api_url}/submit" | |
| try: | |
| agent = BasicAgent() | |
| 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" if space_id else "" | |
| 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 | |
| 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 | |
| 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.answer_question(question_text, task_id=task_id) | |
| 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}"} | |
| ) | |
| results_df = pd.DataFrame(results_log) | |
| if not answers_payload: | |
| print("Agent did not produce any answers to submit.") | |
| return "Agent did not produce any answers to submit.", results_df | |
| 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.") | |
| 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) | |
| return status_message, results_df | |
| except requests.exceptions.Timeout: | |
| status_message = "Submission Failed: The request timed out." | |
| print(status_message) | |
| return status_message, results_df | |
| except requests.exceptions.RequestException as e: | |
| status_message = f"Submission Failed: Network error - {e}" | |
| print(status_message) | |
| return status_message, results_df | |
| except Exception as e: | |
| status_message = f"An unexpected error occurred during submission: {e}" | |
| print(status_message) | |
| return status_message, results_df | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Basic Agent Evaluation Runner") | |
| gr.Markdown( | |
| """ | |
| **Instructions:** | |
| 1. Clone this space, then modify the code to define your agent's logic, tools, and packages. | |
| 2. Log in to your Hugging Face account using the button below. | |
| 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. | |
| """ | |
| ) | |
| 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") | |
| else: | |
| print("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("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) | |