app.py

import gradio as gr
from huggingface_hub import InferenceClient

import csv
import json

import matplotlib.pyplot as plt
import tempfile

# Parámetros generales
r = 0.3  # Tasa de crecimiento
K = 1000  # Capacidad de carga
T = 20    # Tiempo total de simulación

# Función logística
def logistic_growth(N, r, K):
    return r * N * (1 - N / K)

# Función para simular el crecimiento
def simulate_population(t_values, initial_population, num_simulations):
    population = np.zeros((len(t_values), num_simulations))
    population[0] = initial_population
    for t in range(1, len(t_values)):
        for sim in range(num_simulations):
            population[t, sim] = population[t-1, sim] + logistic_growth(population[t-1, sim], r, K)
    return population

# Función para la interfaz de Gradio
def app(num_simulations, initial_population):
    num_simulations = int(num_simulations)
    initial_population = int(initial_population)
    
    t_values = np.linspace(0, T, 100)
    results = simulate_population(t_values, initial_population, num_simulations)

    # Configuración de la visualización
    fig, axes = plt.subplots(nrows=num_simulations, ncols=1, figsize=(10, 8), sharex=True)

    if num_simulations == 1:
        axes = [axes]

    for i, ax in enumerate(axes):
        ax.plot(t_values, results[:, i], label=f'Simulación {i+1}', alpha=0.7)
        ax.set_title(f'Simulación {i+1}')
        ax.set_xlabel('Tiempo')
        ax.set_ylabel('Población')
        ax.legend()
        ax.grid(True)
        ax.set_ylim(0, 1200)  # Ajustar límites del eje y si es necesario

    # Guardar la última figura en un archivo temporal
    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png')
    temp_file.close()
    fig.savefig(temp_file.name)
    plt.close(fig)

    return [temp_file.name, results.tolist()]  # Devolver el nombre del archivo temporal y los resultados como lista

def buscar_en_csv_y_generar_json(archivo_csv, valor_busqueda):
    resultados = []

    with open(archivo_csv, mode='r', encoding='utf-8') as file:
        reader = csv.reader(file)
        
        for fila in reader:
            linea_completa = ','.join(fila)
            if valor_busqueda in linea_completa:
                resultados.append(fila)

    if resultados:
        return json.dumps(resultados, indent=4, ensure_ascii=False)
    else:
        return json.dumps({"mensaje": "No se encontraron coincidencias."}, indent=4, ensure_ascii=False)


"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")


def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response

"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
css = "#component-2 {height: 350px}"
def search(term):

    return buscar_en_csv_y_generar_json("proyectos_empresas_full.csv", term)
    
with gr.Blocks(title="SPAIN WIND ENERGY LOBBY") as app:
#with gr.Blocks(theme='gradio/soft') as demo:
#with gr.Blocks(title="Sophia, Torah Codes") as app:
    #with gr.Row():
    """
    gr.ChatInterface(
        respond,
        additional_inputs=[
            #gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
            #gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
            #gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
            #gr.Slider(
            #    minimum=0.1,
            #    maximum=1.0,
            #    value=0.95,
            #    step=0.05,
            #    label="Top-p (nucleus sampling)",
            #),
        ],
    )
    """
#    with gr.Row():

    with gr.Row():
        gr.Plot(label="MW por promotor")
        gr.Plot(label="Ubicación por promotor")
        gr.Plot(label="Potencia promotor por ubicaciún")

    with gr.Row():
        to_convert = gr.Textbox(value="Forestalia",label="Search",scale=4)
        search_els = gr.Button("Search",scale=1)
    with gr.Row():
        #els_results = gr.JSON(label="Results")
        results = gr.JSON()
        search_els.click(
            search, 
            inputs=[to_convert],
            outputs= results
        )   




if __name__ == "__main__":
    app.launch()