import os import gradio as gr import requests import inspect import pandas as pd from agent import AmbiguityClassifier import json # (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 ------ class BasicAgent: """A langgraph agent that detects and classifies ambiguities in user stories.""" def __init__(self): print("BasicAgent initialized.") self.analizar_historia = AmbiguityClassifier() def __call__(self, question: str) -> str: print(f"Agent received question (first 50 chars): {question[:50]}...") try: resultado = self.analizar_historia(question) # Formatear la respuesta respuesta = [] if resultado["tiene_ambiguedad"]: respuesta.append("Se encontraron las siguientes ambigüedades:") if resultado["ambiguedad_lexica"]: respuesta.append("\nAmbigüedades léxicas:") for amb in resultado["ambiguedad_lexica"]: respuesta.append(f"- {amb}") if resultado["ambiguedad_sintactica"]: respuesta.append("\nAmbigüedades sintácticas:") for amb in resultado["ambiguedad_sintactica"]: respuesta.append(f"- {amb}") respuesta.append(f"\nScore de ambigüedad: {resultado['score_ambiguedad']}") respuesta.append("\nSugerencias de mejora:") for sug in resultado["sugerencias"]: respuesta.append(f"- {sug}") else: respuesta.append("No se encontraron ambigüedades en la historia de usuario.") respuesta.append(f"Score de ambigüedad: {resultado['score_ambiguedad']}") return "\n".join(respuesta) except Exception as e: error_msg = f"Error analizando la historia: {str(e)}" print(error_msg) return error_msg 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("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 # Inicializar el clasificador classifier = AmbiguityClassifier() def analyze_user_story(user_story: str) -> str: """Analiza una historia de usuario y retorna los resultados formateados.""" if not user_story.strip(): return "Por favor, ingrese una historia de usuario para analizar." # Analizar la historia result = classifier(user_story) # Formatear resultados output = [] output.append(f"📝 Historia analizada:\n{user_story}\n") output.append(f"🎯 Score de ambigüedad: {result['score_ambiguedad']}") if result['ambiguedad_lexica']: output.append("\n📚 Ambigüedades léxicas encontradas:") for amb in result['ambiguedad_lexica']: output.append(f"• {amb}") if result['ambiguedad_sintactica']: output.append("\n🔍 Ambigüedades sintácticas encontradas:") for amb in result['ambiguedad_sintactica']: output.append(f"• {amb}") if result['sugerencias']: output.append("\n💡 Sugerencias de mejora:") for sug in result['sugerencias']: output.append(f"• {sug}") return "\n".join(output) def analyze_multiple_stories(user_stories: str) -> str: """Analiza múltiples historias de usuario separadas por líneas.""" if not user_stories.strip(): return "Por favor, ingrese al menos una historia de usuario para analizar." stories = [s.strip() for s in user_stories.split('\n') if s.strip()] all_results = [] for i, story in enumerate(stories, 1): result = classifier(story) story_result = { "historia": story, "score": result['score_ambiguedad'], "ambiguedades_lexicas": result['ambiguedad_lexica'], "ambiguedades_sintacticas": result['ambiguedad_sintactica'], "sugerencias": result['sugerencias'] } all_results.append(story_result) return json.dumps(all_results, indent=2, ensure_ascii=False) # Crear la interfaz with gr.Blocks(title="Detector de Ambigüedades en Historias de Usuario") as demo: gr.Markdown(""" # 🔍 Detector de Ambigüedades en Historias de Usuario Esta herramienta analiza historias de usuario en busca de ambigüedades léxicas y sintácticas, proporcionando sugerencias para mejorarlas. ## 📝 Instrucciones: 1. Ingrese una historia de usuario en el campo de texto 2. Haga clic en "Analizar" 3. Revise los resultados y las sugerencias de mejora """) with gr.Tab("Análisis Individual"): input_text = gr.Textbox( label="Historia de Usuario", placeholder="Como usuario quiero...", lines=3 ) analyze_btn = gr.Button("Analizar") output = gr.Textbox( label="Resultados del Análisis", lines=10 ) analyze_btn.click( analyze_user_story, inputs=[input_text], outputs=[output] ) with gr.Tab("Análisis Múltiple"): input_stories = gr.Textbox( label="Historias de Usuario (una por línea)", placeholder="Como usuario quiero...\nComo administrador necesito...", lines=5 ) analyze_multi_btn = gr.Button("Analizar Todas") output_json = gr.JSON(label="Resultados del Análisis") analyze_multi_btn.click( analyze_multiple_stories, inputs=[input_stories], outputs=[output_json] ) gr.Markdown(""" ## 🚀 Ejemplos de Uso Pruebe con estas historias de usuario: - Como usuario quiero un sistema rápido y eficiente para gestionar mis tareas - El sistema debe permitir exportar varios tipos de archivos - Como administrador necesito acceder fácilmente a los reportes """) 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)