import logging import math from . import rapid_api # Asegúrate de que el módulo esté configurado correctamente # Configura el logger logger = logging.getLogger(__name__) logging.basicConfig(level=logging.DEBUG) # Cambia a INFO o WARNING en producción class ReviewAnalyzer: def __init__(self, asin): self.asin = asin self.total_reviews = 0 self.stars = {} self.muestras_por_estrella = {} self.best_reviews = [] self.worst_reviews = [] logger.info(f"ReviewAnalyzer initialized for ASIN: {asin}") def fetch_reviews_data(self): try: logger.info(f"Fetching reviews data for ASIN: {self.asin}") data_from_api = rapid_api.getProductReviews(self.asin, 1, 'US', 'ALL', 'TOP_REVIEWS') self.total_reviews = data_from_api['data']['total_reviews'] logger.debug(f"Total reviews: {self.total_reviews}") productDetails = rapid_api.getProductDetails(self.asin, 'US') logger.debug(f"Product details raw data: {productDetails}") # Manejar la ausencia de distribución de calificaciones if 'rating_distribution' in productDetails['data']: self.stars = productDetails['data']['rating_distribution'] logger.info(f"Rating distribution: {self.stars}") else: logger.warning("No rating distribution found. Generating default distribution.") # Crear una distribución de calificaciones predeterminada si no existe self.stars = { '5': '20%', # Distribucion predeterminada '4': '20%', '3': '20%', '2': '20%', '1': '20%' } return True # Indica que la recolección de datos fue exitosa except Exception as e: logger.error(f"Error fetching reviews data: {e}", exc_info=True) return False # Indica que hubo un problema def calculate_sample(self): logger.info("Calculating review sample size") # Si no hay reviews totales, usar un valor predeterminado if self.total_reviews == 0: self.total_reviews = 100 # Valor predeterminado muestra = self.calcular_muestra(self.total_reviews) logger.debug(f"Calculated sample size: {muestra}") nombre_estrella_mapping = { "5": "5_STARS", "4": "4_STARS", "3": "3_STARS", "2": "2_STARS", "1": "1_STARS" } for star, percentage in self.stars.items(): try: nombre_estrella = nombre_estrella_mapping.get(star, "") porcentaje_decimal = float(percentage.strip('%')) / 100 muestras_estrella = max(1, round(muestra * porcentaje_decimal)) # Asegurar al menos 1 muestra self.muestras_por_estrella[nombre_estrella] = muestras_estrella logger.debug(f"Star: {star}, Percentage: {percentage}, Sample: {muestras_estrella}") except Exception as e: logger.error(f"Error processing star data: {star}, percentage: {percentage}", exc_info=True) def filter_reviews(self): logger.info("Filtering reviews based on calculated samples") # Si no hay muestras por estrella, generar muestras predeterminadas if not self.muestras_por_estrella: logger.warning("No samples per star. Generating default samples.") self.muestras_por_estrella = { '5_STARS': 1, '4_STARS': 1, '1_STARS': 1, '2_STARS': 1 } # Eliminar muestras de 3 estrellas si es necesario if '3_STARS' in self.muestras_por_estrella: del self.muestras_por_estrella['3_STARS'] for estrella, muestras in self.muestras_por_estrella.items(): try: logger.info(f"Fetching reviews for star: {estrella}") # Ajustar número de páginas si es necesario paginas = max(1, (muestras if muestras >= 10 else 10) // 10 + 1) for pagina in range(1, paginas + 1): try: data = rapid_api.getProductReviews(self.asin, str(pagina), "US", estrella, "TOP_REVIEWS") # Validar la existencia de reviews if 'data' in data and 'reviews' in data['data']: reviews = data['data']['reviews'] for review in reviews: if estrella in ['5_STARS', '4_STARS']: self.best_reviews.append(review.get('review_comment', 'Sin comentario')) elif estrella in ['1_STARS', '2_STARS']: self.worst_reviews.append(review.get('review_comment', 'Sin comentario')) logger.debug(f"Collected {len(reviews)} reviews for {estrella}") else: logger.warning(f"No reviews found for star: {estrella}, page: {pagina}") except Exception as e: logger.error(f"Error fetching reviews for star: {estrella}, page: {pagina}: {e}") except Exception as e: logger.error(f"Error in review filtering for {estrella}: {e}") # Si no hay reviews, agregar comentarios predeterminados if not self.best_reviews: self.best_reviews = ["No se encontraron reseñas positivas"] if not self.worst_reviews: self.worst_reviews = ["No se encontraron reseñas negativas"] def calcular_muestra(self, N): margen_error = 0.05 Z = 1.96 p = 0.5 n = (N * Z ** 2 * p * (1 - p)) / ((N - 1) * margen_error ** 2 + Z ** 2 * p * (1 - p)) return math.ceil(n) def analyze_reviews(self): logger.info(f"Starting review analysis for ASIN: {self.asin}") # Intentar obtener datos de reviews, continuar incluso si falla try: self.fetch_reviews_data() except Exception as e: logger.error(f"Error fetching reviews data: {e}") # Calcular muestra, manejar caso de error try: self.calculate_sample() except Exception as e: logger.error(f"Error calculating sample: {e}") # Usar valores predeterminados si falla self.total_reviews = 100 self.stars = {'5': '20%', '4': '20%', '3': '20%', '2': '20%', '1': '20%'} # Filtrar reviews, manejar caso de error try: self.filter_reviews() except Exception as e: logger.error(f"Error filtering reviews: {e}") # Usar comentarios predeterminados si falla self.best_reviews = ["No se encontraron reseñas positivas"] self.worst_reviews = ["No se encontraron reseñas negativas"] logger.info( f"Review analysis completed: {len(self.best_reviews)} best reviews, {len(self.worst_reviews)} worst reviews") return self.best_reviews, self.worst_reviews