AI/chatbot/multipal_col.py

import pandas as pd
import yaml
import re

# Read the data from the CSV file
df = pd.read_csv(r"D:\livecode\chatbot\AI Support Questionaire - Sheet1 (2).csv", encoding='latin')


def remove_special_characters(df):
    for column in df.columns:
        df[column] = df[column].apply(lambda x: re.sub(r'[^a-zA-Z0-9->]', ' ', str(x)))
    return df

df_cleaned = remove_special_characters(df)
uniqueid = df['UniqueId'].tolist()
# Answers = df['Answers'].tolist()

question_cols = df.filter(regex='Question').columns
answer_cols = df.filter(regex='Answers').columns


#appending intents
import ruamel.yaml as yaml

# Load the domain YAML file
with open(r'D:\\livecode\\chatbot\\traildata\\domain.yml', 'r') as f:
    domain = yaml.safe_load(f)
    for i in uniqueid:
        domain['intents'].append(i)
        with open(r'D:\\livecode\\chatbot\\traildata\\domain.yml', 'w') as f:
            yaml.dump(domain, f, default_flow_style=False, allow_unicode=True)



question_cols = df.filter(regex='Question').columns
answer_cols = df.filter(regex='Answer').columns

# for i in question_cols:
#     df[i] = df[i].replace('\n', '$$', regex=True)
#    # df[i] = df[i].str.strip()


# for i in answer_cols:
#     df[i] = df[i].replace('\n', '$$', regex=True)
#    # df[i] = df[i].str.strip()



from ruamel.yaml import YAML

# Create a YAML object that preserves the formatting of the original YAML file
yaml = YAML()
yaml.preserve_quotes = True
yaml.indent(mapping=2, sequence=4, offset=2)

# Read in the existing domain file
with open(r'D:\\livecode\\chatbot\\traildata\\domain.yml', 'r', encoding='utf-8') as file:
    domain = yaml.load(file)

# Generate the new YAML code for each row of data
new_content = {}
for index, row in df.iterrows():
    intent_name = row['UniqueId']
    examples = [row[col] for col in answer_cols ]
    separator = "_"
    for counter, example in enumerate(examples, start=1):
        #example=example.replace(':','')
        response_name = f"utter_{intent_name}{separator}{counter}"
        response_text =f"- text: {example}"
        if 'responses' not in new_content:
            new_content['responses'] = {}
        new_content['responses'][response_name] = yaml.load(response_text)

# Update the `responses` section of the domain dictionary with the new content
domain['responses'].update(new_content['responses'])

# Write the updated domain file back to disk, preserving the formatting of the original file
with open(r'D:\\livecode\\chatbot\\traildata\\domain.yml', 'w',encoding='utf-8') as file:
    yaml.dump(domain, file)



#appending multipal action for each rule
with open(r"D:\\livecode\\chatbot\\traildata\\rules.yml", 'a') as f:
    for index, row in df.iterrows():
        intent_name = row['UniqueId']
        examples = [row[col] for col in answer_cols]
        separator = "_"
        steps = {"intent": intent_name}
        gg=[]
        for counter, example in enumerate(examples, start=1):
            unique_id = f"{intent_name}{separator}{counter}"
            gg.append({"action": f"utter_{unique_id}"})

        output_str = "  \n  ".join([f"- action: {question['action'][:-1]}{i}" for i, question in enumerate(gg, start=1)])

        f.write(f""" 
- rule: Ticket{intent_name}
  steps:
  - intent: {steps['intent']}
  {output_str}
  - action: action_service
  
""")





with open(r'D:\\livecode\\chatbot\\traildata\\nlu.yml', "a",encoding='utf-8') as file:
    for index, row in df.iterrows():
        intent_name = row['UniqueId']
        examples = [row[col] for col in question_cols]
        file.write(f"""
- intent: {intent_name}
  examples: |
""")
        for example in examples:
            file.write(f"    - \"{example}\"\n")
        file.write("\n")



# filename = r"C:\Users\Bizgaze\Desktop\fileupdation\trail_update\data\nlu.yml"
# string_to_remove = '  - "nan"'

# with open(filename, "r") as f:
#     text = f.read()

# modified_text = text.replace(string_to_remove, "")

# with open(filename, "w") as f:
#     f.write(modified_text)

# filename = r"C:\Users\Bizgaze\Desktop\fileupdation\trail_update\data\domain.yml"
# string_to_remove = '  - "nan"'

# with open(filename, "r") as f:
#     text = f.read()

# modified_text = text.replace(string_to_remove, "")

# with open(filename, "w") as f:
#     f.write(modified_text)

# filename = r"C:\Users\Bizgaze\Desktop\fileupdation\trail_update\data\rules.yml"
# string_to_remove = '  - "nan"'

# with open(filename, "r") as f:
#     text = f.read()

# modified_text = text.replace(string_to_remove, "")

# with open(filename, "w") as f:
#     f.write(modified_text)