example.py

import openai

def analyze_feedback(feedback, business_goal):
    """
    Analyze user feedback based on the selected business goal.

    Parameters:
    - feedback: String containing user feedback.
    - business_goal: String specifying the selected business goal.

    Returns:
    - Boolean indicating whether the feedback is valid or not.
    """

    # Construct the prompt for the LLM
    prompt = f"Analyze this({feedback}) user feedback and determine its relevance on the basis of [{business_goal}]. " \
             "If the feedback is not valid, just say 'invalid'; otherwise say 'valid'."

    # Call the LLM API (Assuming using OpenAI's API as an example)
    response = openai.Completion.create(
        engine="text-davinci-003",  # Replace with the appropriate model engine
        prompt=prompt,
        max_tokens=10,
        temperature=0.3
    )

    # Extract the result from the LLM response
    validity = response.choices[0].text.strip()

    # Return True if the feedback is valid, otherwise False
    return "valid" in validity.lower()


def identify_relevant_section(feedback, content):
    """
    Identify the most relevant section of the content based on user feedback.

    Parameters:
    - feedback: String containing user feedback.
    - content: String containing the entire content.

    Returns:
    - String of the most relevant section of the content.
    """

    # Construct the prompt for the LLM with entity recognition
    prompt = f"Analyze this({feedback}) user feedback and determine which part of this({content}) content" \
             "it is referring to specifically. Extract the entity or phrase that connects the feedback to the content."

    # Call the LLM API (Assuming using OpenAI's API as an example)
    response = openai.Completion.create(
        engine="text-davinci-003",  # Replace with the appropriate model engine
        prompt=prompt,
        max_tokens=50,
        temperature=0.3
    )

    # Extract the relevant section from the LLM response
    extracted_entity = response.choices[0].text.strip()

    return extracted_entity


def revise_content_based_on_feedback(content, feedback, business_value):
    """
    Revise content based on user feedback in accordance with a specified business value.

    Parameters:
    - content: String containing the original content.
    - feedback: String containing user feedback.
    - business_value: String specifying the business value that revisions should adhere to.

    Returns:
    - String of the revised content.
    """

    # Construct the prompt for the LLM
    prompt = f"Please revise this({content}) on the basis of this({feedback}) in accordance with this({business_value}) value."

    # Call the LLM API (Assuming using OpenAI's API as an example)
    response = openai.Completion.create(
        engine="text-davinci-003",  # Replace with the appropriate model engine
        prompt=prompt,
        max_tokens=300,  # Adjust token limit based on the expected length of revisions
        temperature=0.3
    )

    # Extract the revised content from the LLM response
    revised_content = response.choices[0].text.strip()

    return revised_content


def process_feedback(content, feedback, business_goal):
    """
    Process a user comment to determine relevance, identify the section it refers to, and revise the content.

    Parameters:
    - content: String containing the entire content of the page.
    - feedback: String containing the user feedback.
    - business_goal: String specifying the business goal.

    Returns:
    - String containing the revised content if relevant, otherwise a message indicating no action was taken.
    """

    # Step 1: Analyze the feedback for relevance
    is_relevant = analyze_feedback(feedback, business_goal)

    if not is_relevant:
        return "Feedback is not relevant to the business goal. No revision made."

    # Step 2: Identify the most relevant section of the content
    relevant_section = identify_relevant_section(feedback, content)

    # Step 3: Revise the identified section based on the feedback and business goal
    revised_section = revise_content_based_on_feedback(relevant_section, feedback, business_goal)

    # Return the revised content or section
    return revised_section


# Example usage
page_content = """
Our product is designed for efficiency and reliability, ensuring seamless integration with your existing systems.
We offer a wide range of services to meet your needs, with a focus on customer satisfaction.
The pricing model is designed to be flexible and accommodate businesses of all sizes.
"""

user_feedback = "The integration process is too complex and needs simplification."
business_goal = "improving ease of integration"

# Process the feedback and revise the content if relevant
revised_content = process_feedback(page_content, user_feedback, business_goal)

print(revised_content)