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| import os | |
| import gradio as gr | |
| import requests | |
| import inspect | |
| import pandas as pd | |
| import json | |
| import copy | |
| from basic_agent import ToolAgent | |
| from tools import ( | |
| smart_read_file, | |
| search_and_extract, | |
| search_and_extract_from_wikipedia, | |
| aggregate_information, | |
| extract_clean_text_from_url, | |
| youtube_search_tool, | |
| load_youtube_transcript, | |
| get_audio_from_youtube, | |
| image_query_tool, | |
| transcribe_audio, | |
| ) | |
| TOOLS = [ | |
| smart_read_file, | |
| search_and_extract, | |
| search_and_extract_from_wikipedia, | |
| aggregate_information, | |
| extract_clean_text_from_url, | |
| youtube_search_tool, | |
| load_youtube_transcript, | |
| get_audio_from_youtube, | |
| image_query_tool, | |
| transcribe_audio, | |
| ] | |
| tool_names = [tool.name if hasattr(tool, "name") else str(tool) for tool in TOOLS] | |
| print(json.dumps(tool_names, indent=2)) | |
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" | |
| TOOL_USE_SYS_PROMPT = """ | |
| You are a helpful AI assistant operating in a structured reasoning and action loop using the ReAct pattern. | |
| Your reasoning loop consists of: | |
| - Question: the input task you must solve | |
| - Thought: Reflect on the task and decide what to do next. | |
| - Action: Choose one of the following actions: | |
| - Solve it directly using your own knowledge | |
| - Break the problem into smaller steps | |
| - Use a tool to get more information | |
| - Action Input: Provide input for the selected action | |
| - Observation: Record the result of the action and/or aggregate information from previous observations (summarize, count, analyse, ...). | |
| (Repeat Thought/Action/Action Input/Observation as needed) | |
| Terminate your loop with: | |
| - Thought: I now know the final answer | |
| - Final Answer: [your best answer to the original question] | |
| **General Execution Rules:** | |
| - If you can answer using only your trained knowledge, do so directly without using tools. | |
| - If the question involves image content, use the `image_query_tool`: | |
| - Action: image_query_tool | |
| - Action Input: 'image_path': [image_path], 'question': [user's question about the image] | |
| **Tool Use Constraints:** | |
| - Never use any tool more than **3 consecutive times** without either: | |
| - Aggregating the information received so far: you can call the `summarize_search_results` tool and analyze the tool outputs to answer the question. | |
| - If you need more information, use a different tool or break the problem down further, but do not return a final answer yet. | |
| - Do not exceed **5 total calls** to *search-type tools* per query (such as `search_and_extract`, `search_and_extract_from_wikipedia`, `extract_clean_text_from_url`). | |
| - Do not ask the user for additional clarification or input. Work with only what is already provided. | |
| **If you are unable to answer:** | |
| - If neither your knowledge nor tool outputs yield useful information: | |
| - Use the output tools the best you can to answer the question, even if it's not perfect. | |
| If not, say: | |
| > Final Answer: I could not find any useful information to answer your query. | |
| - If the question is unanswerable due to lack of input (e.g., missing attachment) or is fundamentally outside your scope, say: | |
| > Final Answer: I don't have the ability to answer this query: [brief reason] | |
| Always aim to provide the **best and most complete** answer based on your trained knowledge and the tools available. | |
| """ | |
| def run_and_submit_all( profile: gr.OAuthProfile | None): | |
| """ | |
| Fetches all questions, runs the ToolAgent 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, gr.update(interactive=False) | |
| api_url = DEFAULT_API_URL | |
| questions_url = f"{api_url}/questions" | |
| files_url = "{}/files/{}" # GET /files/{task_id} | |
| submit_url = f"{api_url}/submit" | |
| # 1. Instantiate Agent ( modify this part to create your agent) | |
| try: | |
| agent = ToolAgent( | |
| tools=TOOLS, | |
| backstory=TOOL_USE_SYS_PROMPT | |
| ) | |
| agent.initialize() | |
| except Exception as e: | |
| print(f"Error instantiating agent: {e}") | |
| return f"Error initializing agent: {e}", None, gr.update(interactive=False) | |
| # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public) | |
| agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" | |
| print(agent_code) | |
| # 2. Fetch Questions | |
| 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, gr.update(interactive=False) | |
| 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, gr.update(interactive=False) | |
| 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, gr.update(interactive=False) | |
| except Exception as e: | |
| print(f"An unexpected error occurred fetching questions: {e}") | |
| return f"An unexpected error occurred fetching questions: {e}", None, gr.update(interactive=False) | |
| # 3. Run your Agent | |
| 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") | |
| question_level = item.get("Level") | |
| question_file_name = item.get("file_name", None) | |
| print("\nquestion level: ", question_level) | |
| print("task_id: ", task_id) | |
| print("question file_name: ", question_file_name) | |
| if question_file_name: | |
| file_url = files_url.format(api_url, task_id) | |
| print("file_url: ", file_url) | |
| file_response = requests.get(file_url, timeout=15) | |
| file_response.raise_for_status() | |
| print("file_response: ", file_response.content[0:50]) | |
| save_path = os.path.join("/tmp", question_file_name) | |
| print("save_path: ", save_path) | |
| with open(save_path, "wb") as f: | |
| f.write(file_response.content) | |
| print(f"✅ file saved to: {save_path}") | |
| found= False | |
| metadata = {} | |
| for root, dirs, files in os.walk("/"): | |
| if question_file_name in files: | |
| file_path = os.path.join(root, question_file_name) | |
| print("file found at: ", file_path) | |
| metadata = {'image_path': file_path} if '.png' in question_file_name else {} | |
| found=True | |
| if question_file_name and not found: | |
| print("FileNotFoundError: try making an api request to .files/ or ./static in the hf.space target (or check it manually first)") | |
| break | |
| if not task_id or question_text is None: | |
| print(f"Skipping item with missing task_id or question: {item}") | |
| continue | |
| try: | |
| q_data = {'query': question_text, 'metadata': metadata} | |
| submitted_answer = agent(q_data) # todo: send more data (files) | |
| 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), gr.update(interactive=False) | |
| # 4. Prepare Submission | |
| 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) | |
| # 5. Submit | |
| 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() | |
| log_file_dict = copy.deepcopy(results_log) | |
| log_file_dict.append({'result_data': result_data}) | |
| with open("results_log.json", "w") as results_session_file: | |
| json.dump(log_file_dict, results_session_file) | |
| 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, gr.update(interactive=True) | |
| 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, gr.update(interactive=False) | |
| 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, gr.update(interactive=False) | |
| 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, gr.update(interactive=False) | |
| 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, gr.update(interactive=False) | |
| def download_log(): | |
| return "results_log.json" | |
| # Gradio App | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Basic Agent Evaluation Runner") | |
| gr.LoginButton() | |
| run_button = gr.Button("Run Evaluation & Submit All Answers") | |
| download_button = gr.Button("Download Evaluation Log", interactive=False) | |
| 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, download_button] | |
| ) | |
| file_output = gr.File(label="Download Log File", visible=True) | |
| download_button.click( | |
| fn=download_log, | |
| outputs=file_output | |
| ) | |
| 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) |