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graph_generator
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# ---------------------------------------------------------------------------------
# Aplicaci贸n principal para cargar el modelo, generar prompts y explicar los datos
# ---------------------------------------------------------------------------------

import streamlit as st  # type: ignore
import os
import re
import pandas as pd  # type: ignore
from dotenv import load_dotenv  # type: ignore # Para cambios locales
from supabase import create_client, Client  # type: ignore
from transformers import pipeline

from pandasai import SmartDataframe  # type: ignore
from pandasai.llm.starcoder import Starcoder # type: ignore

# ---------------------------------------------------------------------------------
# Funciones auxiliares
# ---------------------------------------------------------------------------------

# Funci贸n para extracci贸n de c贸digo Python del output del modelo
def extract_code(llm_output):
    code_match = re.search(r"```python\n(.*?)\n```", llm_output, re.DOTALL)
    if code_match:
        return code_match.group(1)
    return None

# Funci贸n para generar prompts de gr谩ficos comparativos
# Ejemplo de prompt generado:
# generate_graph_prompt("Germany", "France", "fertility rate", 2020, 2030)
def generate_graph_prompt(country1, country2, metric, start_year, end_year):
    prompt = f"""

    You have access to a database of European countries with data on {metric}, labor force participation, population, and their predictions for future years.

    Generate Python code using matplotlib to create a line graph showing the trend of {metric} for {country1} and {country2} from {start_year} to {end_year}.

    Also, provide a concise explanation of what this graph represents for an end user who might not be familiar with the data.

    """
    return prompt

# ---------------------------------------------------------------------------------
# Configuraci贸n de conexi贸n a Supabase
# ---------------------------------------------------------------------------------

# Cargar variables de entorno desde archivo .env
load_dotenv()

# Conectar las credenciales de Supabase (ubicadas en "Secrets" en Streamlit)
SUPABASE_URL = os.getenv("SUPABASE_URL")
SUPABASE_KEY = os.getenv("SUPABASE_KEY")

# Crear cliente Supabase
supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)

# Funci贸n para cargar datos de una tabla de Supabase
# Tablas posibles: fertility, geo data, labor, population, predictions
def load_data(table):
    try:
        if supabase:
            response = supabase.from_(table).select("*").execute()
            print(f"Response object: {response}")  # Inspeccionar objeto completo
            print(f"Response type: {type(response)}")  # Verificar tipo de objeto

            # Acceder a atributos relacionados a error o data
            if hasattr(response, 'data'):
                print(f"Response data: {response.data}")
                return pd.DataFrame(response.data)
            elif hasattr(response, 'status_code'):
                print(f"Response status code: {response.status_code}")
            elif hasattr(response, '_error'):  # Versiones antiguas
                print(f"Older error attribute: {response._error}")
                st.error(f"Error fetching data: {response._error}")
                return pd.DataFrame()
            else:
                st.info("Response object does not have 'data' or known error attributes. Check the logs.")
                return pd.DataFrame()

        else:
            st.error("Supabase client not initialized. Check environment variables.")
            return pd.DataFrame()
    except Exception as e:
        st.error(f"An error occurred during data loading: {e}")
        return pd.DataFrame()

# ---------------------------------------------------------------------------------
# Cargar datos iniciales
# ---------------------------------------------------------------------------------

# # Cargar datos desde la tabla "labor"
data = load_data("labor")

# TODO: La idea es luego usar todas las tablas, cuando ya funcione.
# Se puede si el modelo funciona con las gr谩ficas, sino que toca mejorarlo 
# porque ser铆an consultas m谩s complejas.
# labor_data = load_data("labor")
# fertility_data = load_data("fertility")
# population_data = load_data("population")
# predictions_data = load_data("predictions")

# ---------------------------------------------------------------------------------
# Inicializar modelo LLM
# ---------------------------------------------------------------------------------

# # Pendiente cambiar Keys dependiendo del modelo que escojamos
# model_name = "google/flan-t5-small"  # Probando modelos
# generator = pipeline("text-generation", model=model_name)

# ---------------------------------------------------------------------------------
# Inicializar PandasAI con StarCoder
# ---------------------------------------------------------------------------------

# Definir el modelo StarCoder desde Hugging Face
huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
llm = Starcoder(api_token=huggingface_token)

sdf = SmartDataframe(data, config={"llm": llm}) # DataFrame PandasAI-ready.

# ---------------------------------------------------------------------------------
# Configuraci贸n de la app en Streamlit
# ---------------------------------------------------------------------------------

# T铆tulo de la app
st.title("_Europe GraphGen_  :blue[Graph generator] :flag-eu:")

# Entrada de usuario para describir el gr谩fico
user_input = st.text_input("What graphics do you have in mind")
generate_button = st.button("Generate")

# Manejo de evento de bot贸n
if generate_button and user_input:
    # if data.empty and supabase is not None:
    #     st.warning("Successfully connected to Supabase, but no data was loaded (either the table is empty or there was a query issue). Check the error message above if any.")
    # elif not data.empty:
    #     st.success("Successfully connected to Supabase and loaded data!")
    #     st.dataframe(data.head())  # Mostrar una peque帽a muestra del DataFrame
    # elif supabase is None:
    #     st.error("Failed to initialize Supabase client. Check environment variables in Settings.")
    # else:
    #     st.info("Attempted to load data. Check for any error messages above.")

    # Procesar el input del usuario con PandasAI
    if generate_button and user_input:
        st.dataframe(data.head())
        
        with st.spinner('Generating answer...'):
            try:
                answer = sdf.chat(user_input)
                st.write(answer)
            except Exception as e:
                st.error(f"Error generating answer: {e}")


# TODO: Output estructurado si vemos que es necesario.