Spaces:
Sleeping
Sleeping
| import os | |
| import gradio as gr | |
| import requests | |
| import pandas as pd | |
| from smolagents import ( | |
| CodeAgent, | |
| OpenAIServerModel, | |
| GoogleSearchTool, | |
| ) | |
| from tools import read_image, transcribe_audio, run_video, search_wikipedia, read_code | |
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" | |
| model_id = "gpt-4o-mini" | |
| class BasicAgent: | |
| def __init__(self, model_id=model_id): | |
| model = OpenAIServerModel(model_id=model_id, temperature=0.1) | |
| google_search = GoogleSearchTool() | |
| self.agent = CodeAgent( | |
| model=model, | |
| tools=[ | |
| read_image, | |
| transcribe_audio, | |
| read_code, | |
| run_video, | |
| search_wikipedia, | |
| google_search, | |
| ], | |
| additional_authorized_imports=["numpy", "pandas"], | |
| max_steps=20, | |
| ) | |
| add_sys_prompt = f"""\n\nIf a file_url is available or an url is given in question statement, then request and use the content to answer the question. \ | |
| If a code file, such as .py file, is given, do not attempt to execute it but rather open it as a text file and analyze the content. \ | |
| When a tabluar file, such as csv, tsv, xlsx, is given, read it using pandas. | |
| Make sure you provide the answer in accordance with the instruction provided in the question. Do not return the result of tool as a final_answer. | |
| Do Not add any additional information, explanation, unnecessary words or symbols. The answer is likely as simple as one word.""" | |
| self.agent.prompt_templates["system_prompt"] += add_sys_prompt | |
| def __call__(self, question: str) -> str: | |
| answer = self.agent.run(question) | |
| return answer | |
| def run_and_submit_all(profile: gr.OAuthProfile | None): | |
| """ | |
| Fetches all questions, runs the BasicAgent on them, submits all answers, | |
| and displays the results. | |
| """ | |
| space_id = os.getenv("SPACE_ID") | |
| if profile: | |
| username = f"{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 = 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" | |
| 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") | |
| file_name = item.get("file_name") | |
| if file_name: | |
| file_url = f"{DEFAULT_API_URL}/files/{task_id}" | |
| else: | |
| file_url = "No URL provided" | |
| extension = file_name.split(".")[-1] | |
| question_text += f"\n\nfile_url : {file_url} \nfile_extension : {extension}" | |
| 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(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: | |
| 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}", | |
| } | |
| ) | |
| if not answers_payload: | |
| print("Agent did not produce any answers to submit.") | |
| 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, | |
| } | |
| 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.") | |
| results_df = pd.DataFrame(results_log) | |
| 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) | |
| results_df = pd.DataFrame(results_log) | |
| return status_message, results_df | |
| except requests.exceptions.Timeout: | |
| status_message = "Submission Failed: The request timed out." | |
| print(status_message) | |
| results_df = pd.DataFrame(results_log) | |
| return status_message, results_df | |
| except requests.exceptions.RequestException as e: | |
| status_message = f"Submission Failed: Network error - {e}" | |
| print(status_message) | |
| results_df = pd.DataFrame(results_log) | |
| return status_message, results_df | |
| except Exception as e: | |
| status_message = f"An unexpected error occurred during submission: {e}" | |
| print(status_message) | |
| results_df = pd.DataFrame(results_log) | |
| return status_message, results_df | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Evaluation") | |
| 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("ℹ️ 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( | |
| "ℹ️ 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) | |