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| import os | |
| import gradio as gr | |
| import requests | |
| import pandas as pd | |
| from smolagents import ToolCallingAgent, OpenAIServerModel | |
| 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 GaiaAgent: | |
| def __init__(self): | |
| print("Gaia Agent Initialized") | |
| # Initialize the OpenAI GPT-3.5-turbo model via smolagents OpenAIServerModel | |
| self.model = OpenAIServerModel( | |
| model_name="gpt-3.5-turbo", | |
| api_key=os.getenv("OPENAI_API_KEY") # Make sure you set this in your environment | |
| ) | |
| # Initialize the tools | |
| self.tools = [ | |
| AudioTranscriptionTool(), | |
| ImageAnalysisTool(), | |
| WikipediaSearcher() | |
| ] | |
| # Create the agent with tools and model | |
| 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}") | |
| if isinstance(result, dict) and "answer" in result: | |
| return str(result["answer"]).strip() | |
| elif isinstance(result, str): | |
| return result.strip() | |
| elif isinstance(result, 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 | |
| question_text = item.get("question", "") | |
| # Download associated file if any (mp3 or jpeg) according to GAIA benchmark task | |
| file_url = item.get("file_url") | |
| local_file_path = None | |
| if file_url: | |
| try: | |
| ext = file_url.split(".")[-1].lower() | |
| if ext in ["mp3", "wav", "jpeg", "jpg", "png"]: | |
| local_file_path = f"./temp_{task_id}.{ext}" | |
| with requests.get(file_url, stream=True) as r: | |
| r.raise_for_status() | |
| with open(local_file_path, "wb") as f: | |
| for chunk in r.iter_content(chunk_size=8192): | |
| f.write(chunk) | |
| print(f"Downloaded file for task {task_id} to {local_file_path}") | |
| # Append info about the file path to the question so the agent knows to use it | |
| question_text += f"\n\nFile path: {local_file_path}" | |
| except Exception as e: | |
| print(f"Failed to download file for task {task_id}: {e}") | |
| 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 | |
| }) | |
| # Cleanup downloaded file | |
| if local_file_path: | |
| try: | |
| os.remove(local_file_path) | |
| except Exception: | |
| pass | |
| 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) | |