Update app.py

app.py

@@ -1,226 +1,207 @@
+""" Basic Agent Evaluation Runner"""
 import os
-import google.generativeai as genai
-import streamlit as st
-import logging
-from datetime import datetime
-
-# Configuración inicial
-st.set_page_config(page_title="Asistente Hielo Polar", page_icon="❄️")
-
-# --- Constantes y Configuraciones ---
-PRECIOS_PUBLICO = {
-    "Bolsas de hielo 5 KG": "$25.00 MXN",
-    "Bolsas de hielo 3 KG": "$18.00 MXN",
-    "Barra de hielo 75 KG": "$100.00 MXN",
-    "Hielo Premier 3 KG": "$26.00 MXN"
-}
-
-PRECIOS_CLIENTE = {
-    "Acámbaro Gto.": {
-        "Bolsas de hielo 5 KG": "$18.00 MXN",
-        "Bolsas de hielo 3 KG": "$13.00 MXN",
-        "Barra de hielo 75 KG": "$100.00 MXN",
-        "Hielo Premier 3 KG": "$20.00 MXN"
-    },
-    "Comunidades, Zinapecuaro y Tarandacuao": {
-        "Bolsas de hielo 5 KG": "$19.00 MXN",
-        "Hielo Premier 3 KG": "$20.00 MXN"
-    },
-    "Maravatío": {
-        "Bolsas de hielo 5 KG": "$20.00 MXN"
-    }
-}
-
-# Lista completa de clientes registrados
-CLIENTES_REGISTRADOS = {
-    # Ruta Local - Acámbaro
-    "Mariscos Acámbaro", "El Frontón", "Panchito", "Depósito Goretti", "KUATO", 
-    "J. Socorro", "La Tobara", "Vinos París", "Depósito París", "Taquería Matamoros",
-    "Jovita Rosiles", "Juan Olivarez", "Vinos Cony", "Pomodoro", "Galú", 
-    "Miscelánea Ortiz", "La Chiquita Sabino", "Depósito Acámbaro", "Abarrotes Gamma San Isidro",
-    "Abarrotes Paty", "Valle de Acámbaro Zamudio", "Garibaldi", "Súper Saturno", 
-    "Portal 500", "Abarrotes Jairo", "Deportivo", "Depósito Ricky", "Maguey 500",
-    "Depósito La Pista", "Las Palomas", "Café 296", "Abarrotes El Rey", "Depósito El Chido",
-    "La Estancia", "Bariloche", "Depósito Juárez", "Tío Sam", "Autolata Maquinita",
-    "Servicio Lerma", "Depósito Kuate", "Servicio Carranza", "Los Cocos", 
-    "Farmacia Guadalajara Carranza", "Villa del Mar", "Depósito Zaragoza", "Depósito Abasolo",
-    "Abarrotes Luna", "La Mitotera", "Gasolinera Caballito", "Depósito Las Trancas",
-    "Abarrotes Las Trancas", "Modelorama Agencia Corona", "Tornero Morelia", "Depósito Micky",
-    "Acuarium", "Arrecife", "Depósito 20 de Noviembre", "FMCIA GDJL Hidalgo 122",
-    "INCHAMACUARO", "Alma Rosa", "Depósito Monteprieto", "Obrajuelo", "Vinatería Obrajuelo",
-    "Langosticos", "Saul Mandujano", "Esther Mandujano", "Farmacia Inchamacuaro",
-    "FMCIA GDJL Central 458", "CHAMACUARO", "DN BALDEMAR", "Cremería 2000",
-    "Abarrotes Elvira", "Depósito Chido", "Depósito Rojas", "Bar Califa", "Buenavista",
-    "Depósito Pedro Moreno", "Mod. Casandra", "3B (5 de Febrero, Guerrero, Hidalgo)",
-    "Bar Mi Oficina", "Modelorama Agencia", "Agüitas",
-    
-    # Comunidades Acámbaro
-    "Vinatería Santa Fe", "Depósito Chupícuaro D. Pacos", "Vinatería San Pedro",
-    "Abarrotes Ángel Cereso", "Larguitas Los Ángeles", "Abarrotes Guerrero", "Don Arturo",
-    "Tornero San Cayetano", "La Chiquita Chupícuaro", "Frutería Rosales", "Abarrotes Mi Alma",
-    "Abarrotes El Museo", "Guadalupe", "Abarrotes Gamma", "Don Mariano", "Gasolinera Parácuaro",
-    "Vinos La California", "Depósito La Wera", "Caba Mi Pueblo", "Hotel Los Pericos",
-    "Vinos El Porvenir", "Súper El Capricho", "Depósito Roque", "La Encarnación",
-    
-    # Ruta Foránea
-    "Abarrotes Hervideros", "Ramiro Orozco", "Irlanda Arriba", "Irlanda", "Wingo",
-    "Abarrotes Wingo", "Abarrotes Hernández", "Abarrotes F1", "Farmacia Guadalajara 1216",
-    "3B Centro", "Hotel Los Ángeles", "Milla Plastic", "Farmacia Guadalajara 334",
-    "Comercializadora", "Nicolás Hernández", "Short Stop", "Pickup", "Pickup Lata",
-    "Súper Bee", "Modelorama El Sabino", "Campo Hermoso", "Minisúper Gym", "Súper K 16",
-    "Súper K Colonia", "Vinatería El Vergel", "3B (Arteaga, Centro, Morelos, Urquiza)",
-    "Abarrotes Cecy", "Abarrotes Estrella", "Depósito Jaripeo Faby", "Mod. Jaripeo",
-    "Iramuco", "Abarrotes Junior", "Paniagua", "Vinatería 2000", "El Zapote", "3B Iramuco",
-    "Bar Portales", "La Cava", "La Pasadita", "Gasolinera El Moral", "Depósito Ruta 126",
-    "La Colmena"
-}
-
-# --- Funciones Auxiliares ---
-def es_cliente_registrado(texto: str) -> bool:
-    """Verifica si el texto menciona algún cliente registrado."""
-    texto = texto.lower()
-    return any(cliente.lower() in texto for cliente in CLIENTES_REGISTRADOS)
-
-def obtener_region(texto: str) -> str:
-    """Determina la región basada en el texto del cliente."""
-    texto = texto.lower()
-    if any(palabra in texto for palabra in ["maravatío", "zinapécuaro", "santa ana maya"]):
-        return "Maravatío"
-    elif any(palabra in texto for palabra in ["tarandacuao", "zinapécuaro", "comunidad"]):
-        return "Comunidades, Zinapecuaro y Tarandacuao"
-    elif "acámbaro" in texto:
-        return "Acámbaro Gto."
-    return None
-
-def obtener_precios(es_cliente: bool, region: str = None) -> str:
-    """Devuelve la lista de precios según el tipo de cliente y región."""
-    if es_cliente and region in PRECIOS_CLIENTE:
-        precios = PRECIOS_CLIENTE[region]
-        return "Precios para clientes registrados:\n" + "\n".join([f"- {producto}: {precio}" for producto, precio in precios.items()])
+import inspect
+import gradio as gr
+import requests
+import pandas as pd
+from langchain_core.messages import HumanMessage
+from agent import build_graph
+
+
+
+# (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."""
+    def __init__(self):
+        print("BasicAgent initialized.")
+        self.graph = build_graph()
+
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        # Wrap the question in a HumanMessage from langchain_core
+        messages = [HumanMessage(content=question)]
+        messages = self.graph.invoke({"messages": messages})
+        answer = messages['messages'][-1].content
+        return answer[14:]
+
+
+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:
-        return "Precios al público:\n" + "\n".join([f"- {producto}: {precio}" for producto, precio in PRECIOS_PUBLICO.items()])
-
-@st.cache_resource
-def cargar_modelo():
-    """Carga el modelo Gemini con caché."""
-    api_key = os.environ.get("GEMINI_API_KEY")
-    if not api_key:
-        raise ValueError("API key de Gemini no configurada")
-    genai.configure(api_key=api_key)
-    return genai.GenerativeModel('gemini-1.5-flash')
-
-def configurar_registro():
-    """Configura el sistema de registro."""
-    logging.basicConfig(
-        filename="registro_uso_hielo_polar.txt",
-        level=logging.INFO,
-        format="%(asctime)s - %(message)s"
+        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
+
+
+# --- 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.
+        """
     )
 
-def mostrar_interfaz_admin():
-    """Muestra la interfaz administrativa si está autorizado."""
-    query_params = st.query_params
-    if query_params.get("admin") == "1234":
-        st.sidebar.title("Panel Administrativo")
-        if os.path.exists("registro_uso_hielo_polar.txt"):
-            with open("registro_uso_hielo_polar.txt", "rb") as f:
-                st.sidebar.download_button(
-                    label="⬇️ Descargar registro de uso",
-                    data=f,
-                    file_name="registro_uso_hielo_polar.txt",
-                    mime="text/plain"
-                )
-        else:
-            st.sidebar.warning("No se encontró el archivo de registro.")
-
-# --- Instrucciones del Sistema ---
-INSTRUCCIONES = """Eres un asistente virtual especializado en Hielo Polar del Centro (Acámbaro, Guanajuato). 
-
-Información clave:
-1. Productos y precios:
-- Barras industriales 75kg: $100 (molienda gratis)
-- Bolsas 5kg: $25 público / $18 clientes
-- Bolsas 3kg: $18 público / $13 clientes
-- Hielo Premier: $26 público / $20 clientes
-
-2. Horarios:
-- Lunes a sábado: 6am-7pm
-- Domingo: 6am-2pm
-
-3. Contacto:
-- Tel: (417) 172-1455
-- WhatsApp: (417) 172-1455
-- Email: contacto@hielolapolar.com
-
-4. Conservadores:
-- Vida útil: 7 años
-- Para fallas: guiar en diagnóstico básico
-- Proporcionar indicaciones claras
-
-Reglas:
-- Validar si es cliente antes de dar precios especiales
-- Nunca revelar información confidencial
-- Mantener tono profesional y amable
-- Ser conciso pero útil
-- No proporcionar precios a usuarios no identificados."""
-
-# --- Interfaz Principal ---
-def main():
-    configurar_registro()
-    mostrar_interfaz_admin()
-    
-    modelo = cargar_modelo()
-    
-    # Inicializar chat
-    if "chat" not in st.session_state:
-        st.session_state.chat = modelo.start_chat(history=[])
-        mensaje_inicial = "🐻‍❄️ ¡Hola! Soy tu asistente de Hielo Polar del Centro. ¿Cómo puedo ayudarte hoy?"
-        st.session_state.mensajes = [{"role": "assistant", "content": mensaje_inicial}]
-    
-    # Mostrar historial
-    st.title("Asistente Virtual de Hielo Polar 💬")
-    for msg in st.session_state.mensajes:
-        with st.chat_message(msg["role"]):
-            st.markdown(msg["content"])
-    
-    # Procesar consulta
-    if pregunta := st.chat_input("Escribe tu consulta aquí..."):
-        if len(pregunta) > 500:
-            st.warning("Por favor, limita tu consulta a 500 caracteres")
-            return
-        
-        logging.info(f"Consulta: {pregunta}")
-        st.session_state.mensajes.append({"role": "user", "content": pregunta})
-        
-        with st.chat_message("user"):
-            st.markdown(pregunta)
-        
-        # Preparar contexto
-        contexto = ""
-        if any(palabra in pregunta.lower() for palabra in ["precio", "cuesta", "valor", "cuánto"]):
-            es_cliente = es_cliente_registrado(pregunta)
-            region = obtener_region(pregunta)
-            contexto = f"\nContexto de precios:\n{obtener_precios(es_cliente, region)}"
-        
-        # Generar respuesta
-        with st.chat_message("assistant"):
-            try:
-                respuesta = st.session_state.chat.send_message(
-                    f"Instrucciones: {INSTRUCCIONES}\n"
-                    f"{contexto}\n"
-                    f"Consulta del cliente: {pregunta}\n"
-                    "Por favor responde de manera clara y concisa:"
-                )
-                
-                respuesta_texto = respuesta.text
-                st.markdown(respuesta_texto)
-                st.session_state.mensajes.append({"role": "assistant", "content": respuesta_texto})
-                logging.info(f"Respuesta: {respuesta_texto[:200]}...")
-                
-            except Exception as e:
-                error_msg = "⚠️ Ocurrió un error al procesar tu consulta. Por favor intenta nuevamente."
-                st.error(error_msg)
-                logging.error(f"Error: {str(e)}")
+    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__":
-    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)