import os import gradio as gr import requests import inspect import pandas as pd import agents from PIL import Image from io import BytesIO import whisper # (Keep Constants as is) # --- Constants --- DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" # --- Load Agent --- # 1. Instantiate Agent ( modify this part to create your agent) agent = None def select_agent(provider_name:str, model_name: str): """ Selects the agent based on the provided name. :param agent_name: Name of the agent to select. :return: The selected agent instance. """ global agent try: agent = agents.get_agent(model_name=model_name, model_type=provider_name) if agent is None: print(f"Agent not found for provider: {provider_name} and model: {model_name}") agent = BasicAgent() except Exception as e: print(f"Error selecting agent: {e}") agent = BasicAgent() # Update ui to indicate the selected agent print(f"Agent selected: {agent.model}") agent_info_text.value = get_agent_info() return agent def get_agent_info() -> str: global agent if (agent is None): return "No agent selected." try: # Get the agent's class name agent_class_name = agent.__class__.__name__ # Get the agent's model name model_name = agent.model # Get the agent's docstring docstring = inspect.getdoc(agent) # Format the information info = f"Agent Class: {agent_class_name}\nModel Name: {model_name}\nDocstring: {docstring}" return info except Exception as e: print(f"Error getting agent info: {e}") return "Error getting agent info." # --- Basic Agent Definition --- # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------ 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 get_all_questions(): """ Fetches all available questions from the API. """ yield from run_test_on_questions(False, False) def run_test_on_all_questions(): """ Runs tests on all available questions by forwarding yields from run_test_on_questions. """ yield from run_test_on_questions(False, True) def run_test_on_random_question(): """ Runs a single test on a random available question by forwarding yields from run_test_on_questions. """ yield from run_test_on_questions(True, True) def run_test_on_questions(use_random_question: bool, run_agent:bool): """ Fetches all questions, runs the BasicAgent on them, submits all answers, and displays the results. """ global agent api_url = DEFAULT_API_URL questions_url = f"{api_url}/random-question" if use_random_question else f"{api_url}/questions" # 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) info = "# started request" yield info, None # 2. Fetch Questions print(f"Fetching questions from: {questions_url}") try: response = requests.get(questions_url, timeout=15) response.raise_for_status() questions_dataset_raw = response.json() questions_dataset = [questions_dataset_raw] if use_random_question else questions_dataset_raw yield info, None if not questions_dataset: print("Fetched questions list is empty.") yield info +"\n\nFetched questions list is empty or invalid format.", None return print(f"Fetched {len(questions_dataset)} questions.") except requests.exceptions.RequestException as e: print(f"Error fetching questions: {e}") yield f"Error fetching questions: {e}", None return except requests.exceptions.JSONDecodeError as e: print(f"Error decoding JSON response from questions endpoint: {e}") print(f"Response text: {response.text[:500]}") yield f"Error decoding server response for questions: {e}", None return except Exception as e: print(f"An unexpected error occurred fetching questions: {e}") yield f"An unexpected error occurred fetching questions: {e}", None return # 3. Run your Agent results_log = [] answers_payload = [] # loop over all questions for i, questions_data in enumerate(questions_dataset): agent.memory.reset() images = [] task_id = questions_data.get("task_id") question_text = questions_data.get("question") file_name = questions_data.get("file_name") if (file_name != "" and file_name is not None): question_text = question_text + f"\n\nYou can download the correspondig file using the download tool with the task id: {task_id}." fileData = requests.get(f"{DEFAULT_API_URL}/files/{task_id}") # check if file is an image if fileData.headers['Content-Type'] in ['image/png', 'image/jpeg']: image = Image.open(BytesIO(fileData.content)).convert("RGB") images = [image] if fileData.headers['Content-Type'] in ['audio/mpeg', 'audio/wav']: # Load the audio file using Whisper model = whisper.load_model("base") # MP3-Datei von der API abrufen with open("temp_audio.mp3", "wb") as f: f.write(fileData.content) # Transkription durchführen audioContent = model.transcribe("temp_audio.mp3") question_text = question_text + f"\n\nTranscription: {audioContent['text']}" info += f"\n\nRunning agent on question {i+1}/{len(questions_dataset)}:\n - task_id: {task_id}\n - question: {question_text}" yield info, None if not task_id or question_text is None: yield info+ f"\nError in question data: {questions_data}", None return try: submitted_answer = agent.run(question_text, images=images) if run_agent else "-- no agent interaction --" info += f"\n - got answer {submitted_answer}" 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, "FileInfo": file_name}) 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}", "FileInfo": file_name}) if not answers_payload: print("Agent did not produce any answers.") yield info + "\nAgent did not produce any answers.", pd.DataFrame(results_log) return # 5. Submit try: results_df = pd.DataFrame(results_log) yield info + "\nGot an answer from agent", 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) yield status_message, results_df return def run_and_submit_all( profile: gr.OAuthProfile | None): """ Fetches all questions, runs the BasicAgent on them, submits all answers, and displays the results. """ return "We are not there yet", None # --- 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" submit_url = f"{api_url}/submit" # 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 # 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...") 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 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 def fetch_ollama_models() -> list: """ Fetches available models from the Ollama server. :return: List of available models. """ try: response = requests.get("http://localhost:11434/api/tags") response.raise_for_status() data = response.json() return [model["name"] for model in data["models"]] except requests.exceptions.RequestException as e: print(f"Error fetching Ollama models: {e}") return ["None"] def fetch_lmstudio_models() -> list: """ Fetches available models from the LM Studio server. :return: List of available models. """ try: response = requests.get("http://localhost:1234/v1/models") response.raise_for_status() data = response.json() return [model["id"] for model in data["data"]] except requests.exceptions.RequestException as e: print(f"Error fetching LM Studio models: {e}") return ["None"] available_models = ["None"] def update_available_models(provider:str): """ Fetches available models based on the selected provider. :param provider: The selected provider name. :return: Update object for the model dropdown. """ global available_models print(f"Selected provider: {provider}") match provider: case "hugging face": available_models = ["None", "Qwen/Qwen2.5-Coder-32B-Instruct", "Qwen/Qwen2.5-Omni-7B"] case "Ollama": available_models = fetch_ollama_models() case "LMStudio": available_models = fetch_lmstudio_models() case "Gemini": available_models = ["None", "Gemini-2.0-flash-exp", "Gemini-2.0-flash-lite"] case "Anthropic": available_models = ["None", "claude-3"] # just for later options, model name possibly wrong case "OpenAI": available_models = ["None", "gpt-4o", "gpt-3.5-turbo"] # just for later options, model name possibly wrong case "Basic Agent": available_models = ["None"] case _: available_models = ["None"] print(f"Available models for {provider}: {available_models}") return gr.Dropdown(choices=available_models) # --- Build Gradio Interface using Blocks --- with gr.Blocks() as demo: gr.Markdown("# Basic Agent Evaluation Runner") agent_info_text = gr.Text(label="Agent Name", value=get_agent_info(), interactive=False, visible=True) gr.Markdown( """ **Instructions:** Select a provider and then model to generate the agent. """ ) provider_select = gr.Dropdown( label="Select Provider", choices=["Basic Agent", "LMStudio", "Ollama", "hugging face", "Gemini", "Anthropic", "OpenAI"], interactive=True, visible=True, multiselect=False) model_select = gr.Dropdown( label="Select Model", choices=available_models, interactive=True, visible=True, multiselect=False) # changing the provider will change the available models provider_select.input(fn=update_available_models, inputs=provider_select, outputs=[model_select]) # changing a model will update the agent (see select_agent) model_select.change(fn=select_agent, inputs=[provider_select, model_select]) # in case of running on HF space, we support the login button # we somehow need to find out, if this is running on HF space or not gr.LoginButton() run_button = gr.Button("Run Evaluation & Submit All Answers") run_test_button = gr.Button("Run Test on Random Question") run_multiple_tests_button = gr.Button("Run tests on all questions") run_get_questions_button = gr.Button("Get Questions") 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_test_button.click( fn=run_test_on_random_question, outputs=[status_output, results_table] ) run_multiple_tests_button.click( fn=run_test_on_all_questions, outputs=[status_output, results_table] ) run_button.click( fn=run_and_submit_all, outputs=[status_output, results_table] ) run_get_questions_button.click( fn=get_all_questions, 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)