Rolled back the bayesian formulae to old formulae for average rating changes implemented

src/reviewsense_ecom/service/FeatureUpdater.py

@@ -19,6 +19,7 @@ class FeatureUpdater:
         existing_total_reviews = self.product_data.get("total_reviews", 0)
         current_overall_rating = self.product_data.get("overall_rating", 0.0)
 
+
         for feature, new_data in feature_data.items():
             if feature not in features:
                 features[feature] = {
@@ -30,37 +31,25 @@ class FeatureUpdater:
             current_feature = features[feature]
             existing_review_count = current_feature["review_count"]
 
-            M = 4
-
-            C = max(2, 10 - (existing_review_count // 20))  # Reduce prior influence faster
-
-            # if existing_review_count < 5:
-            #     C = 5
-            # elif existing_review_count < 50:
-            #     C = 10
-            # elif existing_review_count < 500:
-            #     C = 15
-            # else:
-            #     C = 20
 
             new_ratings = new_data.get("ratings", [])
             new_rating = sum(new_ratings) / len(new_ratings) if new_ratings else 0
             new_positive = new_data.get("positive_count", 0) or 0
             new_negative = new_data.get("negative_count", 0) or 0
 
-            # Apply the Bayesian Average Formula
             new_average_rating = (
-                                         (current_feature["average_rating"] * existing_review_count) + (
-                                         C * M) + new_rating
-                                 ) / (existing_review_count + C + 1) if existing_review_count > 0 else new_rating
+                                         (current_feature["average_rating"] * existing_review_count) + new_rating
+                                 ) / (existing_review_count + 1) if existing_review_count > 0 else new_rating
 
-            print(f"{new_average_rating}+--------------------------$$$----------------")
+
+            print(f"{new_average_rating}--------------------------$$$----------------")
 
             features[feature]["average_rating"] = new_average_rating
             features[feature]["review_count"] += 1
             features[feature]["positive_count"] += new_positive
             features[feature]["negative_count"] += new_negative
 
+
         ratings_distribution[str(new_overall_rating)] += 1
 
         updated_total_reviews = existing_total_reviews + 1
@@ -68,9 +57,10 @@ class FeatureUpdater:
                                      (current_overall_rating * existing_total_reviews) + new_overall_rating
                              ) / updated_total_reviews if existing_total_reviews > 0 else new_overall_rating
 
+
         self.product_data["features"] = features
         self.product_data["ratings_distribution"] = ratings_distribution
         self.product_data["overall_rating"] = round(new_overall_rating, 1)
         self.product_data["total_reviews"] = updated_total_reviews
 
-        return self.product_data
+        return self.product_data
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