app.py

# ---------------------------------------------------------------------------------
# Aplicación 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 pandasai import SmartDataframe  # type: ignore
from pandasai import SmartDatalake  # type: ignore # Porque ya usamos más de un df (más de una tabla de nuestra db)
from pandasai.llm.local_llm import LocalLLM  # type: ignore
from pandasai import Agent
import matplotlib.pyplot as plt
import time


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


def generate_graph_prompt(user_query):
    prompt = f"""
            You are a senior data scientist analyzing European labor force data.

            Given the user's request: "{user_query}"

            1. Plot the relevant data using matplotlib:
            - Use pandas indexing with boolean conditions, not .query().
            - For example: df[(df['geo'] == 'Germany') & (df['year'] >= 2018)]
            - Include clear axis labels and a descriptive title.
            - Save the plot as an image file (e.g., temp_chart.png).
            
            2. After plotting, write a *concise analytical summary* of the trend based on those years. The summary should:
            - Use .diff() followed by .idxmax() and .idxmin() to find where the largest change occurs.
            - Use .loc[] to retrieve the corresponding year and value.
            - Calculate percent changes safely (check for divide-by-zero).
            - Avoid using .index() on float values.
            
            3. Store the summary in a variable named explanation.

            4. Return a result dictionary structured as follows:
            result = {{
                "type": "plot",
                "value": "temp_chart.png",
                "explanation": explanation
            }}
            IMPORTANT: Use only the data available in the input DataFrame.
            """
    return prompt


# ---------------------------------------------------------------------------------
# Configuración de conexión 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)


# Función 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
# ---------------------------------------------------------------------------------

labor_data = load_data("labor")
fertility_data = load_data("fertility")
# population_data = load_data("population")
# predictions_data = load_data("predictions") 

# ---------------------------------------------------------------------------------
# Inicializar LLM desde Ollama con PandasAI
# ---------------------------------------------------------------------------------

ollama_llm = LocalLLM(api_base="http://localhost:11434/v1", 
                      model="gemma3:12b",
                      temperature=0.1,  
                      max_tokens=8000)

# lm_studio_llm = LocalLLM(api_base="http://localhost:1234/v1")  # el modelo es gemma-3-12b-it-qat

# sdl = SmartDatalake([labor_data, fertility_data, population_data, predictions_data], config={"llm": ollama_llm}) # DataFrame PandasAI-ready.
# sdl = SmartDatalake([labor_data, fertility_data], config={"llm": ollama_llm})

# agent = Agent([labor_data], config={"llm": lm_studio_llm})
agent = Agent(
    [
        labor_data,
        fertility_data
    ],
    config={
        "llm": ollama_llm,
        "enable_cache": False,
        "enable_filter_extraction": False  # evita errores de parseo
    }
)

# ---------------------------------------------------------------------------------
# Configuración de la app en Streamlit
# ---------------------------------------------------------------------------------

# Título de la app
st.title("Europe GraphGen  :blue[Graph generator] :flag-eu:")

# Entrada de usuario para describir el gráfico
user_input = st.chat_input("What graphics do you have in mind")

if user_input:
    with st.spinner('Generating answer...'):
        try:
            print(f"\nGenerating prompt...\n")
            prompt = generate_graph_prompt(user_input)
            print(f"\nPrompt generated: {prompt}\n")

            start_time = time.time()

            answer = agent.chat(prompt)
            print(f"\nAnswer type: {type(answer)}\n")  # Verificar tipo de objeto
            print(f"\nAnswer content: {answer}\n")  # Inspeccionar contenido de la respuesta
            print(f"\nFull result: {agent.last_result}\n")

            full_result = agent.last_result
            explanation = full_result.get("explanation", "")

            elapsed_time = time.time() - start_time
            print(f"\nExecution time: {elapsed_time:.2f} seconds\n")

            if isinstance(answer, str) and os.path.isfile(answer):
                # Si el output es una ruta válida a imagen
                im = plt.imread(answer)
                st.image(im)
                os.remove(answer)  # Limpiar archivo temporal

                if explanation:
                    st.markdown(f"*Explanation:* {explanation}")
            else:
                # Si no es una ruta válida, mostrar como texto
                st.markdown(str(answer))

        except Exception as e:
            st.error(f"Error generating answer: {e}")