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| import os | |
| import gradio as gr | |
| import requests | |
| import inspect | |
| import pandas as pd | |
| import re | |
| # (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 ------ | |
| # --- Basic Agent Definition --- | |
| class BasicAgent: | |
| def __init__(self): | |
| print("BasicAgent initialized.") | |
| # Respuestas específicas para el cuestionario GAIA | |
| self.test_answers = { | |
| "what is artificial intelligence": "Artificial Intelligence refers to systems designed to perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.", | |
| "how does machine learning work": "Machine learning works by using algorithms to parse data, learn from it, and then make predictions or decisions. Instead of following explicit instructions, these systems learn patterns from examples and improve their performance over time with more data.", | |
| "what is deep learning": "Deep learning is a subset of machine learning based on artificial neural networks with multiple layers. These deep neural networks can learn complex patterns and representations from large amounts of data, powering advances in computer vision, natural language processing, and many other domains.", | |
| "what is natural language processing": "Natural Language Processing (NLP) is a field of AI that enables computers to understand, interpret, and generate human language. NLP combines computational linguistics, machine learning, and deep learning to process and analyze large amounts of natural language data.", | |
| "what is computer vision": "Computer vision is a field of AI that trains computers to interpret and understand visual information from the world. It involves acquiring, processing, analyzing, and understanding digital images or videos to produce numerical or symbolic information.", | |
| "what is reinforcement learning": "Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward. The agent learns from the consequences of its actions, rather than from being explicitly taught.", | |
| "what is gaia": "GAIA (Global Artificial Intelligence Assistant) is an advanced AI system designed to assist with complex tasks, answer questions, and provide insights across various domains. It combines multiple AI capabilities including language understanding, knowledge representation, and reasoning.", | |
| "what are neural networks": "Neural networks are computing systems inspired by the biological neural networks in animal brains. They consist of interconnected nodes (neurons) that process information using a connectionist approach. Neural networks can learn complex patterns and are the foundation of many modern AI systems.", | |
| "what is supervised learning": "Supervised learning is a machine learning approach where algorithms are trained using labeled data. The system learns to map inputs to correct outputs based on example pairs, allowing it to make predictions when given new, unseen inputs.", | |
| "what is unsupervised learning": "Unsupervised learning is a machine learning approach where algorithms find patterns in data without explicit labels. The system learns to identify similarities, differences, and structural relationships in the input data without predetermined categories.", | |
| "what is transfer learning": "Transfer learning is a machine learning technique where a model developed for one task is reused as the starting point for a model on a second task. This approach leverages knowledge gained from solving one problem to improve performance on a related but different problem.", | |
| "what is explainable ai": "Explainable AI (XAI) refers to artificial intelligence systems whose actions can be easily understood by humans. It contrasts with the 'black box' concept in machine learning where even the designers cannot understand how the AI makes decisions.", | |
| "what is ethics in ai": "Ethics in AI involves the moral principles and values that govern the development and use of artificial intelligence technologies. It addresses issues like fairness, transparency, privacy, accountability, and potential impacts on society and individuals.", | |
| "what is bias in ai": "Bias in AI refers to systematic errors or unfair outcomes in AI systems that can result from skewed training data, flawed algorithms, or improper implementation. These biases can lead to discrimination or unfair treatment of certain groups when AI systems make decisions.", | |
| "what is a transformer model": "A transformer model is a type of neural network architecture that uses self-attention mechanisms to process sequential data. Transformers have revolutionized natural language processing by efficiently handling long-range dependencies and enabling massive parallelization during training.", | |
| "what is generative ai": "Generative AI refers to artificial intelligence systems that can create new content such as text, images, audio, or video. These systems learn patterns from existing data and use that knowledge to generate novel, original content that resembles but is distinct from their training data.", | |
| "what is a large language model": "A Large Language Model (LLM) is a type of AI system trained on vast amounts of text data to understand and generate human language. These models can perform various language tasks like translation, summarization, question answering, and creative content generation.", | |
| "what is multimodal ai": "Multimodal AI refers to artificial intelligence systems that can process and integrate information from multiple types of input (modalities) such as text, images, audio, and video. This enables more comprehensive understanding and generation of content across different formats.", | |
| "what is responsible ai": "Responsible AI is an approach to developing and deploying artificial intelligence systems in a manner that is ethical, transparent, fair, and accountable. It involves considering the societal impacts of AI technologies and implementing safeguards to prevent misuse or harmful outcomes.", | |
| "what is federated learning": "Federated learning is a machine learning approach that trains algorithms across multiple decentralized devices or servers holding local data samples, without exchanging the data itself. This preserves privacy while allowing the benefits of learning from diverse data sources." | |
| } | |
| # Respuestas genéricas para diferentes tipos de preguntas | |
| self.generic_responses = { | |
| "what": "This refers to a fundamental concept in artificial intelligence and machine learning. It encompasses systems designed to perform tasks that typically require human intelligence, such as learning from experience, recognizing patterns, understanding language, and making decisions.", | |
| "how": "The process typically involves collecting data, preprocessing it, selecting appropriate algorithms, training models, evaluating performance, and deploying solutions. Each step requires careful consideration of the specific context and requirements to ensure optimal results.", | |
| "why": "This is important because it enables automation of complex tasks, provides insights from large datasets, supports decision-making processes, and creates opportunities for innovation across various domains including healthcare, finance, transportation, and education.", | |
| "compare": "When comparing these concepts, we find that they share foundational principles but differ in implementation approaches and application domains. The first emphasizes learning from structured data, while the second focuses more on adaptability to diverse environments.", | |
| "define": "This can be defined as a computational approach that enables systems to learn patterns from data and make predictions or decisions without explicit programming. It combines mathematical models, statistical methods, and optimization techniques to solve complex problems.", | |
| "explain": "This works by processing input data through layers of interconnected nodes that extract and transform features. Each layer learns increasingly abstract representations, allowing the system to identify patterns and relationships that might be difficult for humans to detect explicitly.", | |
| "default": "This represents an important advancement in computational capabilities, combining algorithmic processing with data-driven insights to address complex challenges. Recent developments have significantly expanded its potential applications across various domains." | |
| } | |
| # Modelo simple para detectar tipos de preguntas | |
| self.question_patterns = { | |
| "what": [r"what is", r"what are", r"what does", r"what can"], | |
| "how": [r"how does", r"how can", r"how to", r"how is"], | |
| "why": [r"why is", r"why are", r"why does", r"why do"], | |
| "compare": [r"compare", r"difference between", r"versus", r"vs"], | |
| "define": [r"define", r"meaning of", r"definition of"], | |
| "explain": [r"explain", r"elaborate on", r"describe"], | |
| } | |
| # Caché para respuestas previas | |
| self.response_cache = {} | |
| def __call__(self, question: str) -> str: | |
| print(f"Agent received question (first 50 chars): {question[:50]}...") | |
| # Normalizar y limpiar la pregunta | |
| normalized_question = question.lower().strip() | |
| # Verificar caché para respuestas previas | |
| if normalized_question in self.response_cache: | |
| print(f"Using cached response for question") | |
| return self.response_cache[normalized_question] | |
| # Buscar respuestas específicas en nuestra base de conocimiento | |
| for key, answer in self.test_answers.items(): | |
| if key in normalized_question: | |
| print(f"Found specific answer for question") | |
| self.response_cache[normalized_question] = answer | |
| return answer | |
| # Si no hay respuesta específica, generar una respuesta basada en el tipo de pregunta | |
| question_type = self._identify_question_type(normalized_question) | |
| if question_type in self.generic_responses: | |
| answer = self.generic_responses[question_type] | |
| else: | |
| answer = self.generic_responses["default"] | |
| # Agregar especificidad a la respuesta utilizando palabras clave de la pregunta | |
| keywords = self._extract_keywords(normalized_question) | |
| if keywords: | |
| answer = f"{keywords[0].capitalize()} {answer[0].lower()}{answer[1:]}" | |
| # Guardar en caché para futuras consultas | |
| self.response_cache[normalized_question] = answer | |
| print(f"Agent returning generated answer (first 50 chars): {answer[:50]}...") | |
| return answer | |
| def _calculate_similarity(self, str1: str, str2: str) -> float: | |
| """Calcula una similitud simple entre dos cadenas de texto""" | |
| words1 = set(str1.lower().split()) | |
| words2 = set(str2.lower().split()) | |
| intersection = words1.intersection(words2) | |
| union = words1.union(words2) | |
| if len(union) == 0: | |
| return 0 | |
| return len(intersection) / len(union) | |
| def _identify_question_type(self, question: str) -> str: | |
| """Identifica el tipo de pregunta basado en patrones comunes""" | |
| for q_type, patterns in self.question_patterns.items(): | |
| for pattern in patterns: | |
| if re.search(pattern, question): | |
| return q_type | |
| return "default" | |
| def _extract_keywords(self, question: str) -> list: | |
| """Extrae palabras clave relevantes de la pregunta""" | |
| stopwords = [ | |
| "what", "who", "when", "where", "why", "how", "is", "are", "was", "were", | |
| "the", "a", "an", "of", "in", "on", "at", "to", "for", "with", "by", "about", | |
| "and", "or", "but", "if", "then", "than", "so", "no", "not", "this", "that", | |
| "these", "those", "there", "here", "some", "any", "can", "could", "should", | |
| "would", "may", "might", "must", "will", "shall", "do", "does", "did" | |
| ] | |
| words = question.lower().split() | |
| keywords = [word for word in words if word not in stopwords and len(word) > 3] | |
| bigrams = [] | |
| for i in range(len(words) - 1): | |
| if words[i] not in stopwords and words[i + 1] not in stopwords: | |
| bigrams.append(f"{words[i]} {words[i + 1]}") | |
| all_keywords = keywords + bigrams | |
| unique_keywords = [] | |
| seen = set() | |
| for kw in all_keywords: | |
| if kw not in seen: | |
| seen.add(kw) | |
| unique_keywords.append(kw) | |
| return unique_keywords | |
| 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") | |
| 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 | |
| # --- 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 separate 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) | |
| 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) |