import os import gradio as gr import requests import pandas as pd import google.generativeai as genai from smolagents import CodeAgent, DuckDuckGoSearchTool from smolagents.models.base import BaseModel # Define the system prompt SYSTEM_PROMPT = """You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with just the answer — no prefixes like "FINAL ANSWER:". Your answer should be a number OR as few words as possible OR a comma-separated list of numbers and/or strings. If you're asked for a number, don’t use commas or units like $ or %, unless specified. If you're asked for a string, don’t use articles or abbreviations (e.g. for cities), and write digits in plain text unless told otherwise.""" DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" # Gemini model wrapper class GeminiFlashModel(BaseModel): def __init__(self, model_name="gemini-1.5-flash", api_key=None): self.model_name = model_name self.api_key = api_key or os.getenv("GOOGLE_API_KEY") if not self.api_key: raise ValueError("GOOGLE_API_KEY is not set in environment variables.") genai.configure(api_key=self.api_key) self.model = genai.GenerativeModel(model_name) def generate(self, messages, stop_sequences=None, **kwargs): # Insert system prompt if missing if isinstance(messages, list): if not any(m["role"] == "system" for m in messages): messages = [{"role": "system", "content": SYSTEM_PROMPT}] + messages else: raise TypeError("Expected 'messages' to be a list of message dicts") # Convert messages to a single string (Gemini expects plain prompt) prompt = "\n".join([f"{m['role'].capitalize()}: {m['content']}" for m in messages]) try: response = self.model.generate_content(prompt) return response.text.strip() except Exception as e: return f"GENERATION ERROR: {e}" # Agent using Gemini-1.5-flash class MyAgent: def __init__(self): self.model = GeminiFlashModel(model_name="gemini-1.5-flash") self.agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=self.model) def __call__(self, question: str) -> str: return self.agent.run(question) # Evaluation & submission flow def run_and_submit_all(profile: gr.OAuthProfile | None): space_id = os.getenv("SPACE_ID") if profile: username = 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 = MyAgent() except Exception as e: return f"Error initializing agent: {e}", None agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" 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: return "Fetched questions list is empty or invalid format.", None print(f"Fetched {len(questions_data)} questions.") except Exception as e: return f"Error fetching questions: {e}", None results_log = [] answers_payload = [] 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: 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: error_msg = f"AGENT ERROR: {e}" results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": error_msg}) if not answers_payload: 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 } 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.')}" ) results_df = pd.DataFrame(results_log) return final_status, results_df except requests.exceptions.HTTPError as e: try: detail = e.response.json().get("detail", e.response.text) except Exception: detail = e.response.text[:500] return f"Submission Failed: {detail}", pd.DataFrame(results_log) except requests.exceptions.Timeout: return "Submission Failed: The request timed out.", pd.DataFrame(results_log) except Exception as e: return f"An unexpected error occurred during submission: {e}", pd.DataFrame(results_log) # Gradio UI with gr.Blocks() as demo: gr.Markdown("# Basic Agent Evaluation Runner") gr.Markdown(""" **Instructions:** 1. Clone this space and modify it to define your agent's logic. 2. Log in with Hugging Face. 3. Click 'Run Evaluation & Submit All Answers' to run and submit. """) 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" + "="*10 + " App Startup " + "="*10) space_host = os.getenv("SPACE_HOST") space_id = os.getenv("SPACE_ID") if space_host: print(f"✅ SPACE_HOST: {space_host} -> https://{space_host}.hf.space") else: print("ℹ️ SPACE_HOST not set.") if space_id: print(f"✅ SPACE_ID: {space_id}") else: print("ℹ️ SPACE_ID not set.") demo.launch(debug=True, share=False)