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| import os | |
| import gradio as gr | |
| import requests | |
| import inspect | |
| import pandas as pd | |
| import yaml | |
| from smolagents import CodeAgent, WebSearchTool, InferenceClientModel, DuckDuckGoSearchTool, Tool, VisitWebpageTool | |
| from tools import visit_webpage, analyze_image # transcribe_audio, analyze_video | |
| # This is also a possibility | |
| #from langchain.agents import load_tools | |
| #search_tool = Tool.from_langchain(load_tools(["serpapi"])[0]) | |
| # (Keep Constants as is) | |
| # --- Constants --- | |
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" | |
| # --- Agent Definition --- | |
| # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------ | |
| class CoderAgent: | |
| """Coder agent that is running the Qwen2.5 coder model and can generate and execute python code. It can import the pandas library for data analysis and manipulation.""" | |
| def __init__(self): | |
| model=InferenceClientModel(model_id="Qwen/Qwen2.5-Coder-32B-Instruct") | |
| self.agent = CodeAgent( | |
| tools=[ | |
| #visit_webpage, | |
| VisitWebpageTool(), | |
| WebSearchTool(), | |
| analyze_image | |
| ], | |
| model=model, | |
| max_steps=10, | |
| name="coder_agent", | |
| description="Uses a qwen coder model to generate and execute blocks of python code in order to call tools or perform calculations.", | |
| additional_authorized_imports=[ | |
| "geopandas", | |
| "plotly", | |
| "shapely", | |
| "json", | |
| "pandas", | |
| "numpy", | |
| ], | |
| ) | |
| print("ManagedAgent CoderAgent initialized.") | |
| def __call__(self, prompt: str) -> str: | |
| agent_answer = self.coder_agent.run(prompt) | |
| print(f"Managed agent answer: {agent_answer}") | |
| return agent_answer | |
| class MasterAgent: | |
| def __init__(self): | |
| #model=InferenceClientModel("deepseek-ai/DeepSeek-R1", provider="together", max_tokens=8096) | |
| model=InferenceClientModel(model_id="Qwen/Qwen2.5-Coder-32B-Instruct") | |
| try: | |
| coder = CoderAgent() | |
| except Exception as e: | |
| print(f"Error instantiating agent: {e}") | |
| return f"Error initializing agent: {e}", None | |
| print("Starting instantiation of master") | |
| self.master_agent = CodeAgent( | |
| tools=[ | |
| VisitWebpageTool(), | |
| WebSearchTool(), | |
| analyze_image, | |
| ], | |
| model=model, | |
| add_base_tools=True, | |
| planning_interval=5, | |
| max_steps=15, | |
| managed_agents=[coder.agent], | |
| additional_authorized_imports=[ | |
| "geopandas", | |
| "plotly", | |
| "shapely", | |
| "json", | |
| "pandas", | |
| "numpy", | |
| ], | |
| ) | |
| print("MasterAgent initialized.") | |
| def __call__(self, question: str, attached_file: str) -> str: | |
| """""" | |
| print(f"Agent received question (first 50 chars): {question[:50]}...") | |
| additional_args = {} | |
| if attached_file: | |
| additional_args = { | |
| "attached_file": attached_file | |
| } | |
| question = question + f" The file in attached_file can be accessed locally at this path 'file_cache/{attached_file}' or as a public URL at f{DEFAULT_API_URL}/files/f{os.path.splitext(attached_file)[0]}" | |
| agent_answer = self.master_agent.run(question, additional_args=additional_args) | |
| print(f"Agent answer: {agent_answer}") | |
| return agent_answer | |
| def run_and_submit_all( profile: gr.OAuthProfile | None): | |
| """ | |
| Fetches all questions, runs the MasterAgent on them, submits all answers, | |
| and displays the results. | |
| """ | |
| # --- Determine HF Space Runtime URL and Repo URL --- | |
| space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code | |
| 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" | |
| questions_url = f"{api_url}/random-question" | |
| submit_url = f"{api_url}/submit" | |
| # 1. Instantiate Agent ( modify this part to create your agent) | |
| try: | |
| agent = MasterAgent() | |
| except Exception as e: | |
| print(f"Error instantiating agent: {e}") | |
| return f"Error initializing agent: {e}", None | |
| # In the case of an app running as a hugging Face space, this link points toward your codebase ( useful 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()] # REMOVE BRACKETS WHEN SWITCHING TO ALL QUESTIONS | |
| 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 | |
| # 3. Run your Agent | |
| results_log = [] | |
| answers_payload = [] | |
| print(f"Running agent on {len(questions_data)} questions...") | |
| cache_dir = "file_cache" | |
| if not os.path.exists(cache_dir): | |
| os.makedirs(cache_dir) | |
| answers_dir = "answers_cache" | |
| if not os.path.exists(answers_dir): | |
| os.makedirs(answers_dir) | |
| for item in questions_data: | |
| task_id = item.get("task_id") | |
| question_text = item.get("question") | |
| submitted_answer = None | |
| task_answer_path = os.path.join(answers_dir, task_id + '.yaml') | |
| if os.path.exists(task_answer_path): | |
| print(f"Found existing answer for task {task_id}, loading") | |
| with open(task_answer_path, 'r') as stream: | |
| task_answer = yaml.safe_load(stream) | |
| try: | |
| submitted_answer = task_answer.get("submitted_answer") | |
| except Exception as e: | |
| print(f"Existing answer for task {task_id} is invalid.") | |
| attached_file = item.get("file_name") | |
| if attached_file != "": | |
| local_file_path = os.path.join(cache_dir, attached_file) | |
| if not os.path.exists(local_file_path): | |
| file_name_no_ext = os.path.splitext(attached_file)[0] # e.g., 'document' from 'document.pdf' | |
| download_url = f"{api_url}/files/{file_name_no_ext}" | |
| try: | |
| print(f"Downloading from {download_url}") | |
| response = requests.get(download_url, stream=True) | |
| response.raise_for_status() # Raises an HTTPError for bad responses | |
| with open(local_file_path, 'wb') as f: | |
| for chunk in response.iter_content(chunk_size=8192): | |
| if chunk: | |
| f.write(chunk) | |
| print(f"File downloaded and cached: {local_file_path}") | |
| except requests.exceptions.HTTPError as e: | |
| print(f"HTTP error downloading {download_url}: {e}") | |
| except requests.exceptions.RequestException as e: | |
| print(f"Error downloading {download_url}: {e}") | |
| except OSError as e: | |
| print(f"Error saving file to {local_file_path}: {e}") | |
| if not task_id or question_text is None: | |
| print(f"Skipping item with missing task_id or question: {item}") | |
| continue | |
| try: | |
| if not submitted_answer: | |
| submitted_answer = agent(question_text, attached_file) | |
| 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}) | |
| with open(task_answer_path, 'w') as file: | |
| yaml.dump({"task_id": task_id, "question_text": question_text, "submitted_answer": submitted_answer}, file) | |
| 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) | |
| # 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() | |
| 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 | |
| # --- Build Gradio Interface using Blocks --- | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Agent Evaluation Runner") | |
| gr.Markdown( | |
| """ | |
| **Instructions:** | |
| 1. Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ... | |
| 2. Log in to your Hugging Face account using the button below. This uses your HF username for submission. | |
| 3. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score. | |
| --- | |
| **Disclaimers:** | |
| Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions). | |
| This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async. | |
| """ | |
| ) | |
| gr.LoginButton() | |
| run_button = gr.Button("Run Evaluation & Submit All Answers") | |
| status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False) | |
| # Removed max_rows=10 from DataFrame constructor | |
| 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) | |
| # Check for SPACE_HOST and SPACE_ID at startup for information | |
| space_host_startup = os.getenv("SPACE_HOST") | |
| space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup | |
| 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 repo URLs if SPACE_ID is found | |
| 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 Agent Evaluation...") | |
| demo.launch(debug=True, share=False) |