Update app.py

app.py

@@ -27,7 +27,7 @@ def plot_venn(prior_A, prob_B_given_A, prob_B_given_notA):
 def bayes_app_two_events(prior_A, prob_B_given_A, prob_B_given_notA):
     posterior_A_given_B, prob_B = bayes_two_events(prior_A, prob_B_given_A, prob_B_given_notA)
     fig_venn = plot_venn(prior_A, prob_B_given_A, prob_B_given_notA)
-    return fig_venn, f"P(B): {prob_B:.4f}", f"P(A|B): {posterior_A_given_B:.4f}"
+    return fig_venn, f"P(B): {prob_B:.2f}", f"P(A|B): {posterior_A_given_B:.2f}"
 
 # Función para múltiples eventos
 def bayes_multiple_events(priors, likelihoods):
@@ -44,14 +44,24 @@ def plot_sets_multiple_events(priors, likelihoods, posteriors):
     if num_events == 2:
         # Diagrama de Venn para 2 eventos
         fig, ax = plt.subplots()
-        venn = venn2(subsets=(priors[0], priors[1], likelihoods[0] * priors[0]), set_labels=('A1', 'A2'), ax=ax)
+        venn = venn2(subsets=(priors[0], priors[1], likelihoods[0] * priors[0]), set_labels=('Evento A1', 'Evento A2'), ax=ax)
+        venn.get_label_by_id('10').set_text(f'P(A1) = {priors[0]:.2f}')
+        venn.get_label_by_id('01').set_text(f'P(A2) = {priors[1]:.2f}')
+        venn.get_label_by_id('11').set_text(f'P(B|A1) = {likelihoods[0]:.2f}')
         ax.set_title('Diagrama de Venn para 2 Eventos')
         return fig
 
     elif num_events == 3:
         # Diagrama de Venn para 3 eventos
         fig, ax = plt.subplots()
-        venn = venn3(subsets=(priors[0], priors[1], likelihoods[0] * priors[0], priors[2], likelihoods[1] * priors[1], likelihoods[2] * priors[2], likelihoods[0] * likelihoods[1] * priors[0]), set_labels=('A1', 'A2', 'A3'), ax=ax)
+        venn = venn3(subsets=(priors[0], priors[1], likelihoods[0] * priors[0], priors[2], likelihoods[1] * priors[1], likelihoods[2] * priors[2], likelihoods[0] * likelihoods[1] * priors[0]), set_labels=('Evento A1', 'Evento A2', 'Evento A3'), ax=ax)
+        venn.get_label_by_id('100').set_text(f'P(A1) = {priors[0]:.2f}')
+        venn.get_label_by_id('010').set_text(f'P(A2) = {priors[1]:.2f}')
+        venn.get_label_by_id('001').set_text(f'P(A3) = {priors[2]:.2f}')
+        venn.get_label_by_id('110').set_text(f'P(B|A1 ∩ A2) = {likelihoods[0] * likelihoods[1]:.2f}')
+        venn.get_label_by_id('011').set_text(f'P(B|A2 ∩ A3) = {likelihoods[1] * likelihoods[2]:.2f}')
+        venn.get_label_by_id('101').set_text(f'P(B|A1 ∩ A3) = {likelihoods[0] * likelihoods[2]:.2f}')
+        venn.get_label_by_id('111').set_text(f'P(B|A1 ∩ A2 ∩ A3) = {likelihoods[0] * likelihoods[1] * likelihoods[2]:.2f}')
         ax.set_title('Diagrama de Venn para 3 Eventos')
         return fig
 
@@ -60,6 +70,8 @@ def plot_sets_multiple_events(priors, likelihoods, posteriors):
         events = [f'A{i+1}' for i in range(num_events)]
         fig, ax = plt.subplots()
         ax.bar(events, posteriors, color='purple')
+        for i, posterior in enumerate(posteriors):
+            ax.text(i, posterior / 2, f'P(A{i+1}|B) = {posterior:.2f}', ha='center', va='center', color='white')
         ax.set_ylabel('Probabilidad Posterior')
         ax.set_title('Resultados del Teorema de Bayes para Múltiples Eventos')
         return fig
@@ -72,8 +84,8 @@ def bayes_app_multiple_events(priors, likelihoods):
     posteriors, prob_B = bayes_multiple_events(priors, likelihoods)
     fig = plot_sets_multiple_events(priors, likelihoods, posteriors)
     
-    resultados = "\n".join([f"P(A{i+1}|B): {posterior:.4f}" for i, posterior in enumerate(posteriors)])
-    return fig, f"P(B): {prob_B:.4f}", resultados
+    resultados = "\n".join([f"P(A{i+1}|B): {posterior:.2f}" for i, posterior in enumerate(posteriors)])
+    return fig, f"P(B): {prob_B:.2f}", resultados
 
 # Crear la interfaz de Gradio con dos pestañas
 with gr.Blocks() as demo:
@@ -92,6 +104,7 @@ with gr.Blocks() as demo:
         btn = gr.Button("Calcular")
         btn.click(fn=bayes_app_two_events, inputs=[prior_A, prob_B_given_A, prob_B_given_notA], outputs=[venn_plot, prob_B, prob_A_given_B])
 
+
     with gr.Tab("Múltiples Eventos"):
         gr.Markdown("## Calculadora del Teorema de Bayes para Múltiples Eventos")
         gr.Markdown("Introduce las probabilidades de los eventos iniciales (Priors) y las probabilidades condicionales de B dado cada evento (Likelihoods) separadas por comas. Ejemplo: '0.2, 0.3, 0.5' para tres eventos.")