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| import os | |
| import gradio as gr | |
| import requests | |
| import pandas as pd | |
| from transformers import BartTokenizer, BartForConditionalGeneration | |
| import torch | |
| from smolagents import ToolCallingAgent | |
| from audio_transcriber import AudioTranscriptionTool | |
| from image_analyzer import ImageAnalysisTool | |
| from wikipedia_searcher import WikipediaSearcher | |
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" | |
| SYSTEM_PROMPT = ( | |
| "You are an agent solving the GAIA benchmark and must provide exact answers.\n" | |
| "Rules:\n" | |
| "1. Return only the exact requested answer: no explanation.\n" | |
| "2. For yes/no, return 'Yes' or 'No'.\n" | |
| "3. For dates, use the exact requested format.\n" | |
| "4. For numbers, use only the number.\n" | |
| "5. For names, use the exact name from sources.\n" | |
| "6. If the question has a file, download it using the task ID.\n" | |
| "Examples:\n" | |
| "- '42'\n" | |
| "- 'Arturo Nunez'\n" | |
| "- 'Yes'\n" | |
| "- 'October 5, 2001'\n" | |
| "- 'Buenos Aires'\n" | |
| "Never say 'the answer is...'. Only return the answer.\n" | |
| ) | |
| class LocalBartModel: | |
| def __init__(self): | |
| self.tokenizer = BartTokenizer.from_pretrained("facebook/bart-base") | |
| self.model = BartForConditionalGeneration.from_pretrained("facebook/bart-base") | |
| self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| self.model.to(self.device) | |
| self.model.eval() | |
| def generate(self, inputs, **generate_kwargs): | |
| # inputs must be dict with input_ids and attention_mask | |
| if not isinstance(inputs, dict): | |
| raise ValueError(f"Expected dict input but got {type(inputs)}") | |
| input_ids = inputs.get("input_ids") | |
| attention_mask = inputs.get("attention_mask") | |
| if input_ids is None or attention_mask is None: | |
| raise ValueError("input_ids and attention_mask are required in inputs dict") | |
| input_ids = input_ids.to(self.device) | |
| attention_mask = attention_mask.to(self.device) | |
| with torch.no_grad(): | |
| outputs = self.model.generate( | |
| input_ids=input_ids, | |
| attention_mask=attention_mask, | |
| **generate_kwargs | |
| ) | |
| return outputs | |
| def __call__(self, prompt): | |
| if not isinstance(prompt, str): | |
| raise ValueError(f"LocalBartModel expects a string prompt, got {type(prompt)}") | |
| inputs = self.tokenizer(prompt, return_tensors="pt") | |
| output_ids = self.generate( | |
| inputs, | |
| max_length=100, | |
| num_beams=5, | |
| early_stopping=True | |
| ) | |
| output_text = self.tokenizer.decode(output_ids[0], skip_special_tokens=True) | |
| return output_text.strip() | |
| class GaiaAgent: | |
| def __init__(self): | |
| print("Gaia Agent Initialized") | |
| self.model = LocalBartModel() | |
| self.tools = [ | |
| AudioTranscriptionTool(), | |
| ImageAnalysisTool(), | |
| WikipediaSearcher() | |
| ] | |
| self.agent = ToolCallingAgent( | |
| tools=self.tools, | |
| model=self.model | |
| ) | |
| def __call__(self, question: str) -> str: | |
| print(f"Agent received question (first 50 chars): {question[:50]}...") | |
| full_prompt = f"{SYSTEM_PROMPT}\nQUESTION:\n{question}" | |
| try: | |
| result = self.agent.run(full_prompt) | |
| print(f"Raw result from agent: {result}") | |
| # Handle different result types robustly | |
| if isinstance(result, dict) and "answer" in result: | |
| return str(result["answer"]).strip() | |
| elif isinstance(result, str): | |
| return result.strip() | |
| elif isinstance(result, list): | |
| # Try to extract assistant content from list | |
| for item in reversed(result): | |
| if isinstance(item, dict) and item.get("role") == "assistant" and "content" in item: | |
| return item["content"].strip() | |
| return "ERROR: Unexpected list format" | |
| else: | |
| return "ERROR: Unexpected result type" | |
| except Exception as e: | |
| print(f"Exception during agent run: {e}") | |
| return f"AGENT ERROR: {e}" | |
| 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 = GaiaAgent() | |
| except Exception as e: | |
| print(f"Error initializing agent: {e}") | |
| return f"Error initializing agent: {e}", None | |
| agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" | |
| print(f"Agent code URL: {agent_code}") | |
| 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") | |
| if not task_id: | |
| continue | |
| try: | |
| submitted_answer = agent(item.get("question", "")) | |
| answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer}) | |
| results_log.append({ | |
| "Task ID": task_id, | |
| "Question": item.get("question", ""), | |
| "Submitted Answer": submitted_answer | |
| }) | |
| except Exception as e: | |
| error_msg = f"AGENT ERROR: {e}" | |
| results_log.append({ | |
| "Task ID": task_id, | |
| "Question": item.get("question", ""), | |
| "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 | |
| } | |
| 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.')}" | |
| ) | |
| 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 define your agent and tools. | |
| 2. Log in to your Hugging Face account using the button below. | |
| 3. Click 'Run Evaluation & Submit All Answers' to test your agent and submit results. | |
| """) | |
| 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 = os.getenv("SPACE_HOST") | |
| space_id = os.getenv("SPACE_ID") | |
| if space_host: | |
| print(f"✅ SPACE_HOST found: {space_host}") | |
| print(f" Runtime URL should be: https://{space_host}.hf.space") | |
| else: | |
| print("ℹ️ SPACE_HOST not found.") | |
| if space_id: | |
| print(f"✅ SPACE_ID found: {space_id}") | |
| print(f" Repo URL: https://huggingface.co/spaces/{space_id}") | |
| else: | |
| print("ℹ️ SPACE_ID not found.") | |
| print("-"*(60 + len(" App Starting ")) + "\n") | |
| demo.launch(debug=True, share=False) | |