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Update app.py
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app.py
CHANGED
@@ -4,6 +4,58 @@ from huggingface_hub import InferenceClient
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import csv
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import json
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def buscar_en_csv_y_generar_json(archivo_csv, valor_busqueda):
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resultados = []
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@@ -65,7 +117,7 @@ For information on how to customize the ChatInterface, peruse the gradio docs: h
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css = "#component-2 {height: 350px}"
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def search(term):
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return buscar_en_csv_y_generar_json("
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with gr.Blocks(title="SPAIN WIND ENERGY LOBBY") as app:
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#with gr.Blocks(theme='gradio/soft') as demo:
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@@ -88,7 +140,15 @@ with gr.Blocks(title="SPAIN WIND ENERGY LOBBY") as app:
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],
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"""
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with gr.Row():
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to_convert = gr.Textbox(value="Forestalia",label="Search",scale=4)
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search_els = gr.Button("Search",scale=1)
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@@ -105,4 +165,4 @@ with gr.Blocks(title="SPAIN WIND ENERGY LOBBY") as app:
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if __name__ == "__main__":
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app.launch()
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import csv
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import json
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import matplotlib.pyplot as plt
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import tempfile
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# Par谩metros generales
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r = 0.3 # Tasa de crecimiento
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K = 1000 # Capacidad de carga
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T = 20 # Tiempo total de simulaci贸n
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# Funci贸n log铆stica
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def logistic_growth(N, r, K):
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return r * N * (1 - N / K)
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# Funci贸n para simular el crecimiento
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def simulate_population(t_values, initial_population, num_simulations):
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population = np.zeros((len(t_values), num_simulations))
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population[0] = initial_population
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for t in range(1, len(t_values)):
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for sim in range(num_simulations):
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population[t, sim] = population[t-1, sim] + logistic_growth(population[t-1, sim], r, K)
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return population
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# Funci贸n para la interfaz de Gradio
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def app(num_simulations, initial_population):
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num_simulations = int(num_simulations)
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initial_population = int(initial_population)
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t_values = np.linspace(0, T, 100)
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results = simulate_population(t_values, initial_population, num_simulations)
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# Configuraci贸n de la visualizaci贸n
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fig, axes = plt.subplots(nrows=num_simulations, ncols=1, figsize=(10, 8), sharex=True)
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if num_simulations == 1:
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axes = [axes]
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for i, ax in enumerate(axes):
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ax.plot(t_values, results[:, i], label=f'Simulaci贸n {i+1}', alpha=0.7)
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ax.set_title(f'Simulaci贸n {i+1}')
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ax.set_xlabel('Tiempo')
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ax.set_ylabel('Poblaci贸n')
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ax.legend()
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ax.grid(True)
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ax.set_ylim(0, 1200) # Ajustar l铆mites del eje y si es necesario
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# Guardar la 煤ltima figura en un archivo temporal
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temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png')
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temp_file.close()
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fig.savefig(temp_file.name)
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plt.close(fig)
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return [temp_file.name, results.tolist()] # Devolver el nombre del archivo temporal y los resultados como lista
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def buscar_en_csv_y_generar_json(archivo_csv, valor_busqueda):
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resultados = []
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css = "#component-2 {height: 350px}"
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def search(term):
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return buscar_en_csv_y_generar_json("proyectos_empresas_full.csv", term)
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with gr.Blocks(title="SPAIN WIND ENERGY LOBBY") as app:
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#with gr.Blocks(theme='gradio/soft') as demo:
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],
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"""
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with gr.Row():
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gr.Interface(
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fn=app,
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inputs=["number", "number"],
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outputs=["image", "json"], # Output de tipo imagen y json (para la lista de plots)
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title="Simulaci贸n de crecimiento poblacional con bifurcaci贸n",
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description="Esta aplicaci贸n simula el crecimiento poblacional de una especie con bifurcaciones seg煤n el modelo log铆stico. Ajusta el n煤mero de bifurcaciones y la poblaci贸n inicial para explorar diferentes escenarios."
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)
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with gr.Row():
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to_convert = gr.Textbox(value="Forestalia",label="Search",scale=4)
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search_els = gr.Button("Search",scale=1)
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if __name__ == "__main__":
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app.launch()
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