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| import os | |
| import gradio as gr | |
| import requests | |
| import inspect | |
| import pandas as pd | |
| #import smolagents #to test | |
| from smolagents import CodeAgent, InferenceClientModel, DuckDuckGoSearchTool, HfApiModel, load_tool, tool | |
| from huggingface_hub import InferenceClient | |
| import json | |
| api_url = "https://agents-course-unit4-scoring.hf.space" | |
| questions_url = f"{api_url}/questions" | |
| submit_url = f"{api_url}/submit" | |
| class BasicAgent: | |
| def __init__(self): | |
| print("BasicAgent initialized.") | |
| def __call__(self, question: str) -> str: | |
| print(f"Agent received question (first 50 chars): {question[:50]}...") | |
| fixed_answer = "This is a default answer." | |
| print(f"Agent returning fixed answer: {fixed_answer}") | |
| return fixed_answer | |
| def load_questions_from_file(filepath="questions.json"): | |
| try: | |
| with open(filepath, "r", encoding="utf-8") as f: | |
| questions_data = json.load(f) | |
| if not questions_data: | |
| print("Loaded file is empty.") | |
| return "Loaded file is empty.", None | |
| print(f"Loaded {len(questions_data)} questions from file.") | |
| return "Loaded questions successfully.", questions_data | |
| except FileNotFoundError: | |
| print("File not found. Please run the API fetch first.") | |
| return "File not found.", None | |
| except json.JSONDecodeError as e: | |
| print(f"Error decoding JSON: {e}") | |
| return f"Error decoding JSON: {e}", None | |
| except Exception as e: | |
| print(f"Unexpected error: {e}") | |
| return f"Unexpected error: {e}", None | |
| #set up | |
| #token | |
| #Model | |
| #Agent | |
| # | |
| def run_and_submit_one(): | |
| # 1. Instantiate Agent ( modify this part to create your agent) | |
| try: | |
| agent = BasicAgent() | |
| except Exception as e: | |
| print(f"Error instantiating agent: {e}") | |
| return f"Error initializing agent: {e}", None | |
| # 2. Fetch Questions by loading from local json | |
| status_message, questions_data = load_questions_from_file() | |
| if questions_data is not None and len(questions_data) > 0: | |
| first_question = questions_data[0] | |
| print("First question object:", first_question) | |
| #To test | |
| question_text = first_question.get("question") | |
| task_id = first_question.get("task_id") | |
| print(f"\nTask ID: {task_id}") | |
| print(f"Question: {question_text}") | |
| else: | |
| print("No data found.") | |
| # 3. Run your Agent | |
| results_log = [] | |
| answers_payload = [] | |
| 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) | |
| run_and_submit_one() | |
| #Client setup | |
| #token = HF_TOKEN | |
| #model_repo_id = chat_completion eller question_answering | |
| # llm = LLMFunction.from_huggingface_inference_api( | |
| # repo_id="google/flan-t5-base", # | |
| # token="HF_TOKEN " | |
| # ) | |
| # agent = CodeAgent(llm=llm) | |
| # response = agent("Translate 'How are you?' to German.") | |
| # print(response) | |
| #---- | |
| # client = InferenceClient( | |
| # provider="hf-inference", | |
| # api_key=os.environ["HF_TOKEN"], | |
| # ) | |
| # completion = client.chat.completions.create( | |
| # model="tiiuae/falcon-rw-1b", | |
| # messages=[ | |
| # { | |
| # "role": "user", | |
| # "content": "What is the capital of France?" | |
| # } | |
| # ], | |
| # ) | |
| # completion = client.chat.completions.create( | |
| # model="sarvamai/sarvam-m", | |
| # messages=[ | |
| # { | |
| # "role": "user", | |
| # "content": "What is the capital of France?" | |
| # } | |
| # ], | |
| # ) | |
| # (Keep Constants as is) | |
| # --- Constants --- | |
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" | |
| # --- Basic Agent Definition --- | |
| # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------ | |
| #Tools | |
| #Model | |
| model = InferenceClientModel() #Default | |
| #Agent | |
| code_agent = CodeAgent(tools=[], model=model) | |
| #code_agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=model) | |
| class BasicAgent: | |
| def __init__(self): | |
| print("BasicAgent initialized.") | |
| def __call__(self, question: str) -> str: | |
| print(f"Agent received question (first 50 chars): {question[:50]}...") | |
| fixed_answer = "This is a default answer." | |
| print(f"Agent returning fixed answer: {fixed_answer}") | |
| return fixed_answer | |
| class BasicCodeAgent: | |
| def __init__(self): | |
| model = InferenceClientModel() #Cause of | |
| # model = HfApiModel( | |
| # max_tokens=2096, | |
| # temperature=0.5, | |
| # model_id='Qwen/Qwen2.5-Coder-32B-Instruct',# it is possible that this model may be overloaded | |
| # custom_role_conversions=None, | |
| # ) | |
| self.agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=model) | |
| print("BasicCodeAgent initialized.") | |
| def __call__(self, question: str) -> str: | |
| print(f"BasicCodeAgent received question (first 50 chars): {question[:50]}...") | |
| fixed_answer = "This is a default answer." | |
| #answer = self.agent.run(question) | |
| print(f"BasicCodeAgent returning fixed answer: {fixed_answer}") | |
| 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. | |
| # """ | |
| # # --- Determine HF Space Runtime URL and Repo URL --- | |
| # space_id = os.getenv("/Synnove/Final_Assignment_Template") # 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 = "https://agents-course-unit4-scoring.hf.space" | |
| # questions_url = f"{api_url}/questions" | |
| # submit_url = f"{api_url}/submit" | |
| # # 1. Instantiate Agent ( modify this part to create your agent) | |
| # try: | |
| # #agent = BasicAgent() | |
| # agent = BasicCodeAgent() | |
| # 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 ( 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 | |
| # 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...") | |
| # print(questions_data) | |
| # for item in questions_data: | |
| # task_id = item.get("task_id") | |
| # question_text = item.get("question") | |
| # 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) | |
| # # 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 run_and_submit_allxception 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("# Basic 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 Basic Agent Evaluation...") | |
| # demo.launch(debug=True, share=False) |