Update src/recommendationSystem/utils/common.py

src/recommendationSystem/utils/common.py

@@ -1,112 +1,112 @@
-# --------------------------------- CUSTOM EXCEPTION CLASS --------------------------------------------
-
-# Importing Libraries
-import sys  
-
-
-# Defining structure of exception message
-def error_message_detail(error:Exception,error_detail:sys):
-    _,_,exec_tb = error_detail.exc_info()
-    file_name = exec_tb.tb_frame.f_code.co_filename
-    line_number = exec_tb.tb_lineno
-    error_message = f"Error occured in python script name [{file_name}] " \
-        f"line number [{line_number}] error message [{error}]"
-    return error_message
-
-# Getting the exception message from sys
-class CustomException(Exception):
-    def __init__(self, error_message:Exception, error_detail:sys):
-        super().__init__(str(error_message))
-        self.error_message = error_message_detail(error_message,error_detail=error_detail)
-
-    def __str__(self):
-        return self.error_message
-    
-
-
-
-
-# --------------------------------- PREPROCESSING THE TEXT IN THE DATAFRAME --------------------------------------------
-
-# Importing Libraries
-from nltk.stem.porter import PorterStemmer
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-
-
-
-def removing_blank_lines(text):
-    return text.replace('\n'," ")
-
-def removing_pre_suff_ix(text):
-    y = []
-    
-    for i in text.split():
-        y.append(PorterStemmer().stem(i))
-    
-    return " ".join(y)
-
-def converting_into_vectors(text):
-    vec = CountVectorizer(max_features=5000,stop_words='english').fit_transform(text).toarray()
-    return vec
-
-def finding_similarity(vec):
-    similarity = cosine_similarity(vec)
-    return similarity
-
-# --------------------------------- SAVING FILES --------------------------------------------
-
-import os
-import dill
-
-from recommendationSystem.logging import logger
-
-
-def save_object(file_path,obj):
-    try:
-        dir_path = os.path.dirname(file_path)
-        os.makedirs(dir_path,exist_ok=True)
-
-        with open(file_path,'wb') as file_obj:
-            dill.dump(obj,file_obj)
-
-    except Exception as e:
-        raise CustomException(e,sys)
-    
-
-# --------------------------------- Prediction File --------------------------------------------
-
-import numpy as np
-
-def recommend(data,matrix,anime):
-    anime_index = data[data.title == anime].index[0]
-    distances = np.around(matrix[anime_index],2)
-    anime_list = sorted(list(enumerate(distances)),reverse=True,key=lambda x:x[1])[0:10]
-
-    recommended_anime = []
-    recommended_anime_poster = []
-    recommended_anime_link = []
-    recommended_similarity_score = []
-
-    for i in anime_list:
-        # anime
-        recommended_anime.append(data.iloc[i[0]].title)
-        # posters
-        recommended_anime_poster.append(data.iloc[i[0]].image)
-        # links
-        recommended_anime_link.append(data.iloc[i[0]].links)
-        # score
-        recommended_similarity_score.append(f'{np.around(i[1]*100,2)} %')
-    return recommended_anime, recommended_anime_poster, recommended_anime_link, recommended_similarity_score
-
-# --------------------------------- Find Story of the anime --------------------------------------------
-
-import pandas as pd
-
-def find_anime(label):
-    dataframe_path = os.path.join("artifact","data.csv")
-    df = pd.read_csv(dataframe_path)
-    story = '\n'.join(df[df.name == label].sypnopsis.to_list())
-    return story
-
-
+# --------------------------------- CUSTOM EXCEPTION CLASS --------------------------------------------
+
+# Importing Libraries
+import sys  
+
+
+# Defining structure of exception message
+def error_message_detail(error:Exception,error_detail:sys):
+    _,_,exec_tb = error_detail.exc_info()
+    file_name = exec_tb.tb_frame.f_code.co_filename
+    line_number = exec_tb.tb_lineno
+    error_message = f"Error occured in python script name [{file_name}] " \
+        f"line number [{line_number}] error message [{error}]"
+    return error_message
+
+# Getting the exception message from sys
+class CustomException(Exception):
+    def __init__(self, error_message:Exception, error_detail:sys):
+        super().__init__(str(error_message))
+        self.error_message = error_message_detail(error_message,error_detail=error_detail)
+
+    def __str__(self):
+        return self.error_message
+    
+
+
+
+
+# --------------------------------- PREPROCESSING THE TEXT IN THE DATAFRAME --------------------------------------------
+
+# Importing Libraries
+from nltk.stem.porter import PorterStemmer
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+
+
+
+def removing_blank_lines(text):
+    return text.replace('\n'," ")
+
+def removing_pre_suff_ix(text):
+    y = []
+    
+    for i in text.split():
+        y.append(PorterStemmer().stem(i))
+    
+    return " ".join(y)
+
+def converting_into_vectors(text):
+    vec = CountVectorizer(max_features=5000,stop_words='english').fit_transform(text).toarray()
+    return vec
+
+def finding_similarity(vec):
+    similarity = cosine_similarity(vec)
+    return similarity
+
+# --------------------------------- SAVING FILES --------------------------------------------
+
+import os
+import dill
+
+from recommendationSystem.logging import logger
+
+
+def save_object(file_path,obj):
+    try:
+        dir_path = os.path.dirname(file_path)
+        os.makedirs(dir_path,exist_ok=True)
+
+        with open(file_path,'wb') as file_obj:
+            dill.dump(obj,file_obj)
+
+    except Exception as e:
+        raise CustomException(e,sys)
+    
+
+# --------------------------------- Prediction File --------------------------------------------
+
+import numpy as np
+
+def recommend(data,matrix,anime):
+    anime_index = data[data.title == anime].index[0]
+    distances = np.around(matrix[anime_index],2)
+    anime_list = sorted(list(enumerate(distances)),reverse=True,key=lambda x:x[1])[0:10]
+
+    recommended_anime = []
+    recommended_anime_poster = []
+    recommended_anime_link = []
+    recommended_similarity_score = []
+
+    for i in anime_list:
+        # anime
+        recommended_anime.append(data.iloc[i[0]].title)
+        # posters
+        recommended_anime_poster.append(data.iloc[i[0]].image)
+        # links
+        recommended_anime_link.append(data.iloc[i[0]].links)
+        # score
+        recommended_similarity_score.append(f'{np.around(i[1]*100,2)} %')
+    return recommended_anime, recommended_anime_poster, recommended_anime_link, recommended_similarity_score
+
+# --------------------------------- Find Story of the anime --------------------------------------------
+
+import pandas as pd
+
+def find_anime(label):
+    dataframe_path = os.path.join("/tmp/artifact","data.csv")
+    df = pd.read_csv(dataframe_path)
+    story = '\n'.join(df[df.name == label].sypnopsis.to_list())
+    return story
+
+