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| import gradio as gr | |
| import matplotlib.pyplot as plt | |
| from matplotlib_venn import venn3 | |
| from io import BytesIO | |
| from PIL import Image | |
| import pandas as pd | |
| def validate_inputs(A, B, C, AB, AC, BC, ABC, U): | |
| errors = [] | |
| if A < AB + AC - ABC: | |
| errors.append("A no puede ser menor que la suma de AB y AC menos ABC.") | |
| if B < AB + BC - ABC: | |
| errors.append("B no puede ser menor que la suma de AB y BC menos ABC.") | |
| if C < AC + BC - ABC: | |
| errors.append("C no puede ser menor que la suma de AC y BC menos ABC.") | |
| if U < A + B + C - AB - AC - BC + ABC: | |
| errors.append("El conjunto universal U es menor que la suma total de los conjuntos y sus intersecciones.") | |
| return errors | |
| def suggest_intersections(A, B, C, U): | |
| max_AB = min(A, B, U - (A + B + C - A - B)) | |
| max_AC = min(A, C, U - (A + B + C - A - C)) | |
| max_BC = min(B, C, U - (A + B + C - B - C)) | |
| max_ABC = min(max_AB, max_AC, max_BC) | |
| min_AB = max(0, A + B + C - A - B - C + ABC - U) | |
| min_AC = max(0, A + C + B - A - C - B + ABC - U) | |
| min_BC = max(0, B + C + A - B - C - A + ABC - U) | |
| min_ABC = max(0, ABC - (A + B + C - AB - AC - BC + ABC)) | |
| suggestions = { | |
| "Mínimo valor sugerido para A ∩ B": min_AB, | |
| "Máximo valor sugerido para A ∩ B": max_AB, | |
| "Mínimo valor sugerido para A ∩ C": min_AC, | |
| "Máximo valor sugerido para A ∩ C": max_AC, | |
| "Mínimo valor sugerido para B ∩ C": min_BC, | |
| "Máximo valor sugerido para B ∩ C": max_BC, | |
| "Mínimo valor sugerido para A ∩ B ∩ C": min_ABC, | |
| "Máximo valor sugerido para A ∩ B ∩ C": max_ABC, | |
| } | |
| return suggestions | |
| def calculate_probabilities(A, B, C, AB, AC, BC, ABC, U): | |
| total = U if U > 0 else (A + B + C - AB - AC - BC + ABC) | |
| if total == 0: | |
| return { | |
| "P(A)": 0, | |
| "P(B)": 0, | |
| "P(C)": 0, | |
| "P(A ∩ B)": 0, | |
| "P(A ∩ C)": 0, | |
| "P(B ∩ C)": 0, | |
| "P(A ∩ B ∩ C)": 0, | |
| } | |
| P_A = A / total | |
| P_B = B / total | |
| P_C = C / total | |
| P_AB = AB / total | |
| P_AC = AC / total | |
| P_BC = BC / total | |
| P_ABC = ABC / total | |
| PA_given_B = P_AB / P_B if P_B > 0 else 0 | |
| PA_given_C = P_AC / P_C if P_C > 0 else 0 | |
| PB_given_C = P_BC / P_C if P_C > 0 else 0 | |
| formatted_probs = { | |
| "P(A)": f"{P_A:.2%} ({A}/{total})", | |
| "P(B)": f"{P_B:.2%} ({B}/{total})", | |
| "P(C)": f"{P_C:.2%} ({C}/{total})", | |
| "P(A ∩ B)": f"{P_AB:.2%} ({AB}/{total})", | |
| "P(A ∩ C)": f"{P_AC:.2%} ({AC}/{total})", | |
| "P(B ∩ C)": f"{P_BC:.2%} ({BC}/{total})", | |
| "P(A ∩ B ∩ C)": f"{P_ABC:.2%} ({ABC}/{total})", | |
| "P(A | B)": f"{PA_given_B:.2%}", | |
| "P(A | C)": f"{PA_given_C:.2%}", | |
| "P(B | C)": f"{PB_given_C:.2%}", | |
| "U (Universal Set)": total, | |
| "Complemento de A U B U C": U - (A + B + C - AB - AC - BC + ABC) | |
| } | |
| # Convert to DataFrame for better visualization | |
| df = pd.DataFrame(list(formatted_probs.items()), columns=["Descripción", "Valor"]) | |
| return df | |
| # This function should be integrated with the main part of the app or interface | |
| # It could be connected to a gradio UI, for example, to allow interactive use |