import os import gradio as gr import requests import pandas as pd import google.generativeai as genai from smolagents import CodeAgent, DuckDuckGoSearchTool # System prompt used by the agent 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" # Generation result wrapper to match smolagents expectations class GenerationResult: def __init__(self, content, token_usage=None, input_tokens=0, output_tokens=0): self.content = content self.token_usage = token_usage or {} self.input_tokens = input_tokens self.output_tokens = output_tokens # Gemini model wrapper class GeminiFlashModel: def __init__(self, model_id="gemini-1.5-flash", api_key=None): genai.configure(api_key=api_key or os.getenv("GEMINI_API_KEY")) self.model = genai.GenerativeModel(model_id) self.system_prompt = SYSTEM_PROMPT def generate(self, messages, stop_sequences=None, **kwargs): if not isinstance(messages, list) or not all(isinstance(m, dict) for m in messages): raise TypeError("Expected 'messages' to be a list of dicts") if not any(m.get("role") == "system" for m in messages): messages = [{"role": "system", "content": self.system_prompt}] + messages prompt = "" for m in messages: role = m["role"].capitalize() content = m["content"] prompt += f"{role}: {content}\n" try: response = self.model.generate_content(prompt) text_output = response.text.strip() if hasattr(response, "text") else str(response) return GenerationResult( content=text_output, token_usage={"input_tokens": 0, "output_tokens": 0}, input_tokens=0, output_tokens=0 ) except Exception as e: return GenerationResult( content=f"GENERATION ERROR: {e}", token_usage={"input_tokens": 0, "output_tokens": 0}, input_tokens=0, output_tokens=0 ) # Agent wrapper class MyAgent: def __init__(self): self.model = GeminiFlashModel(model_id="gemini-1.5-flash") self.agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=self.model) def __call__(self, question: str) -> str: return self.agent.run(question) # Main evaluation function 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.", None questions_url = f"{DEFAULT_API_URL}/questions" submit_url = f"{DEFAULT_API_URL}/submit" try: agent = MyAgent() except Exception as e: return f"Error initializing agent: {e}", None try: response = requests.get(questions_url, timeout=15) response.raise_for_status() questions_data = response.json() 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: results_log.append({ "Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}" }) if not answers_payload: return "Agent did not return any answers.", pd.DataFrame(results_log) submission_data = { "username": profile.username.strip(), "agent_code": f"https://huggingface.co/spaces/{space_id}/tree/main", "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"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.')}" ) return final_status, pd.DataFrame(results_log) except Exception as e: return f"Submission failed: {e}", pd.DataFrame(results_log) # Gradio UI setup with gr.Blocks() as demo: gr.Markdown("# Basic Agent Evaluation Runner") gr.Markdown(""" **Instructions:** 1. Clone this space and configure your Gemini API key. 2. Log in to Hugging Face. 3. Run your agent on evaluation tasks and submit answers. """) gr.LoginButton() run_button = gr.Button("Run Evaluation & Submit All Answers") status_output = gr.Textbox(label="Submission Result", lines=5, interactive=False) results_table = gr.DataFrame(label="Results", wrap=True) run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table]) if __name__ == "__main__": print("🔧 App starting...") demo.launch(debug=True, share=False)