import pandas as pd
import numpy as np
import json
import re
from fuzzywuzzy import process

class FMEADataPipeline:

    def __init__(self,catalog_profile,fmea,catalog_code,threshold):
        self.catalog_profile = catalog_profile
        self.fmea = fmea
        self.catalog_code = catalog_code
        self.threshold = threshold
        self.json_cp = None
        self.cp = None
        self.object_part = None
        self.symptom = None
        self.damage = None
        self.cause = None
        self.ERS_filename = None
        self.code_group = None
        self.fmea_code = {}
        self.new_fmea = None
    
    def read_catalog_profile(self):

        self.cp = pd.read_json(self.catalog_profile['cp'],orient='split')
        self.object_part = pd.read_json(self.catalog_profile['object part'],orient='split')
        self.symptom = pd.read_json(self.catalog_profile['symptom'],orient='split')
        self.damage = pd.read_json(self.catalog_profile['damage'],orient='split')
        self.cause = pd.read_json(self.catalog_profile['cause'],orient='split') 

    def build_connector(self):
        self.code_group = self.cp[self.cp['Catalog profile']==self.catalog_code][['Catalog','Code group']]
        self.fmea_code = {'fmea code': ['Component','Failure Mode','Failure Mechanism','Failure Cause'],
                          'Catalog':['B-Object Part','D-Symptom','C-Damage','5-Cause']}
        self.fmea_code = pd.DataFrame(self.fmea_code)
        self.code_group = pd.merge(self.code_group,self.fmea_code,how='left',on='Catalog')
        self.fmea['Catalog Profile (SAP)'] = self.catalog_code

    def split_columns_rows(self):
        columns_to_split = ['Failure Mode', 'Failure Mechanism', 'Failure Cause','Failure Effect']
        separated_rows = []

        # Iterate over each row in the DataFrame
        for _, row in self.fmea.iterrows():
            # Process each column to split by various numbering formats (e.g., '1.', '1)', '1-', etc.)
            split_values = []
            for col in columns_to_split:
                # Split by numbering patterns and strip whitespace
                if isinstance(row[col], str) and row[col]:
                    values = [item.strip() for item in re.split(r'\d+[\)\.-]\s*|[a-zA-Z]+[\)\.-]\s*', row[col]) if item.strip()]
                else:
                    values = [row[col]]  # Keep non-string values as is
                split_values.append(values)

            # Check the maximum length of split values across columns to split
            max_length = max(len(values) for values in split_values)

            # Ensure all split columns have equal length by repeating the last value if needed
            for i, values in enumerate(split_values):
                if len(values) < max_length:
                    split_values[i] = values + [values[-1]] * (max_length - len(values))

            # Create new rows for each split value
            for i in range(max_length):
                new_row = row.copy()
                for col, values in zip(columns_to_split, split_values):
                    new_row[col] = values[i]
                separated_rows.append(new_row)

        # rewrite previous fmea
        self.fmea = pd.DataFrame(separated_rows)

    def bracket_remover(self):
        columns = ['Component','Failure Mode', 'Failure Mechanism', 'Failure Cause','Failure Effect']

        for col in columns:
            if col in self.fmea.columns:
                self.fmea[col] = self.fmea[col].str.replace(r"\s*\(.*?\)", "", regex=True).str.strip()    
    
    def column_matcher_21Jan(self):
        for code, sap in zip(self.fmea_code['fmea code'], [self.object_part, self.symptom, self.damage, self.cause]):
            # Find the matching code group for the current FMEA code
            matching_code_group = self.code_group[self.code_group['fmea code'] == code]['Code group']
            if matching_code_group.empty:
                continue  # Skip if no matching code group is found

            # Get the first matching code group value
            matching_code_group_value = matching_code_group.values[0]

            # Extract the catalog profile and its group for prioritization
            catalog_profile = self.code_group[self.code_group['fmea code'] == code]['Catalog'].values[0]
            catalog_group = catalog_profile[1]  # Second character of the catalog code

            # Filter SAP table for each priority level
            # 1. Catalog Profile
            profile_sap = sap[sap['Code group'] == catalog_profile]
            s_profile = profile_sap['Short text'].tolist()
            m_profile = self.fmea[code].apply(lambda x: process.extract(x, s_profile, limit=1))
            m2_profile = m_profile.apply(lambda x: ', '.join([i[0] for i in x if i[1] >= self.threshold]))

            # 2. Catalog Group (excluding the catalog profile)
            group_sap = sap[(sap['Code group'].str[1] == catalog_group) & (sap['Code group'] != catalog_profile)]
            s_group = group_sap['Short text'].tolist()
            m_group = self.fmea[code].apply(lambda x: process.extract(x, s_group, limit=1))
            m2_group = m_group.apply(lambda x: ', '.join([i[0] for i in x if i[1] >= self.threshold]))

            # 3. Entire SAP catalog
            s_all = sap['Short text'].tolist()
            m_all = self.fmea[code].apply(lambda x: process.extract(x, s_all, limit=1))
            m2_all = m_all.apply(lambda x: ''.join([i[0] for i in x if i[1] >= self.threshold]))

            # Add "_secondary" flag to m2_all values
            m2_all_flagged = m2_all.apply(lambda x: f"{x}_secondary" if x else x)

            # Merge prioritized matches: Profile > Group > All
            merged_m2 = m2_profile.combine(m2_group, lambda x, y: x if x else y)
            merged_m2 = merged_m2.combine(m2_all_flagged, lambda x, y: x if x else y)

            # Create mapping dictionaries
            mapping_dict_code = sap.set_index('Short text')['Code'].to_dict()
            mapping_dict_short_text = sap.set_index('Code')['Short text'].to_dict()
            catalog_code_dict = sap.set_index('Short text')['Code group'].to_dict()

            # Create the new column name based on catalog
            name = catalog_profile

            # Apply the mapping for the catalog column
            self.fmea[name] = merged_m2.apply(
                lambda x: mapping_dict_code.get(x.replace("_secondary", "")) if x else None
            )

            # Construct the description column with catalog code source
            self.fmea[f"{name}_description"] = merged_m2.apply(
                lambda x: (
                    f"{x.replace('_secondary', '')} ({catalog_code_dict.get(x.replace('_secondary', ''), 'Unknown')})"
                    if "_secondary" in x else
                    mapping_dict_short_text.get(mapping_dict_code.get(x), x)
                )
            )
        return self.fmea

            
    def column_arranger(self):
        catalog_profile = self.fmea.pop('Catalog Profile (SAP)')
        b_object_part = self.fmea.pop('B-Object Part')
        d_symptom = self.fmea.pop('D-Symptom')
        c_damage = self.fmea.pop('C-Damage')
        cause_5 = self.fmea.pop('5-Cause')

        b_object_part_desc = self.fmea.pop('B-Object Part_description')
        d_symptom_desc = self.fmea.pop('D-Symptom_description')
        c_damage_desc = self.fmea.pop('C-Damage_description')
        cause_5_desc = self.fmea.pop('5-Cause_description')

        self.fmea.insert(1,catalog_profile.name,catalog_profile)
        self.fmea.insert(3,b_object_part.name,b_object_part)
        self.fmea.insert(4,b_object_part_desc.name,b_object_part_desc)
        self.fmea.insert(6,d_symptom.name,d_symptom)
        self.fmea.insert(7,d_symptom_desc.name,d_symptom_desc)
        self.fmea.insert(9,c_damage.name,c_damage)
        self.fmea.insert(10,c_damage_desc.name,c_damage_desc)
        self.fmea.insert(12,cause_5.name,cause_5)
        self.fmea.insert(13,cause_5_desc.name,cause_5_desc)
        
        print('Completed column arranger function')
        self.fmea.to_excel('processed_fmea.xlsx', index=False)
        return self.fmea

    def process_and_split_excel(self):
        new_rows = []
        columns = ['Proposed Task', 'Task Type', 'Frequency', 'Action Party', 'TA (Y/N)']
        clean_columns = ['Frequency', 'Action Party', 'TA (Y/N)']  # Columns to clean bullet points

        # Regex to remove bullet points or numbering in the clean columns
        bullet_pattern = r'^\s*[\da-zA-Z]+[)\.\-•]?\s*'  # To clean bullets for specific columns

        for _, row in self.fmea.iterrows():
            cell_value = row.loc[columns[0]]

            if isinstance(cell_value, str):
                # Split on newline characters (\n)
                points = [point.strip() for point in cell_value.split('\n') if point.strip()]
                for idx, point in enumerate(points):
                    new_row = row.copy()
                    for column in columns:
                        column_value = row[column]
                        if isinstance(column_value, str):
                            # Split column by newline and align them
                            column_points = [p.strip() for p in column_value.split('\n') if p.strip()]
                            new_value = column_points[idx] if idx < len(column_points) else np.nan
                            # Clean bullet points for specific columns
                            if column in clean_columns:
                                new_value = re.sub(bullet_pattern, '', new_value).strip() if isinstance(new_value, str) else new_value
                            new_row[column] = new_value
                        else:
                            new_row[column] = np.nan if idx > 0 else column_value
                    new_rows.append(new_row)
            else:
                # If the value is not a string, add the row without modification
                new_rows.append(row)

        # Create a new DataFrame with processed rows
        self.new_fmea = pd.DataFrame(new_rows)
        self.new_fmea.to_excel('processed_fmea.xlsx', index=False)
        return self.new_fmea
    
    def process_and_split_excel_2(self):
        columns_to_split = ['Proposed Task', 'Task Type', 'Frequency', 'Action Party', 'TA (Y/N)']
        clean_columns = ['Proposed Task', 'Task Type','Frequency', 'Action Party', 'TA (Y/N)']  # Columns to clean bullet points
        bullet_pattern = r'^\s*(?:\d+[\)\.\-•]\s*|[a-zA-Z]\))'  # Regex to clean bullets
        
        separated_rows = []
        
        for _, row in self.fmea.iterrows():
            split_values = []
            
            for col in columns_to_split:
                if isinstance(row[col], str) and row[col]:
                    values = [item.strip() for item in row[col].split('\n') if item.strip()]
                    if col in clean_columns:
                        values = [re.sub(bullet_pattern, '', v).strip() for v in values]
                else:
                    values = [row[col]]  # Keep non-string values as is
                split_values.append(values)
            
            # Determine the maximum number of splits across all columns
            max_length = max(len(values) for values in split_values)
            
            # Ensure all columns have the same number of values by repeating the last value
            for i, values in enumerate(split_values):
                if len(values) < max_length:
                    split_values[i] = values + [values[-1]] * (max_length - len(values))
            
            # Create new rows for each split value
            for i in range(max_length):
                new_row = row.copy()
                for col, values in zip(columns_to_split, split_values):
                    new_row[col] = values[i]
                separated_rows.append(new_row)
        
        # Create a new DataFrame with processed rows
        self.new_fmea = pd.DataFrame(separated_rows)
        self.new_fmea.to_excel('processed_fmea.xlsx', index=False)
        return self.new_fmea