# import dependencies
import gradio as gr
from openai import OpenAI
import os
import random
import string

# define the openai key
api_key = os.getenv("OPENAI_API_KEY")

# make an instance of the openai client
client = OpenAI(api_key = api_key)

# finetuned model instance
finetuned_model = "gpt-3.5-turbo-0125:personal::9rN1lICj"







# text processing functions
def random_capitalize(word):
    if word.isalpha() and random.random() < 0.1:
        return word.capitalize()
    return word

def random_remove_punctuation(text):
    if random.random() < 0.2:
        text = list(text)
        indices = [i for i, c in enumerate(text) if c in string.punctuation]
        if indices:
            remove_indices = random.sample(indices, min(3, len(indices)))
            for idx in sorted(remove_indices, reverse=True):
                text.pop(idx)
        return ''.join(text)
    return text

def random_double_period(text):
    if random.random() < 0.2:
        text = text.replace('.', '..', 3)
    return text

def random_double_space(text):
    if random.random() < 0.2:
        words = text.split()
        for _ in range(min(3, len(words) - 1)):
            idx = random.randint(0, len(words) - 2)
            words[idx] += '  '
        return ' '.join(words)
    return text

def random_replace_comma_space(text, period_replace_percentage=0.33):

  # Count occurrences
  comma_occurrences = text.count(", ")
  period_occurrences = text.count(". ")

  # Replacements
  replace_count_comma = max(1, comma_occurrences // 3)
  replace_count_period = max(1, period_occurrences // 3)

  # Find indices
  comma_indices = [i for i in range(len(text)) if text.startswith(", ", i)]
  period_indices = [i for i in range(len(text)) if text.startswith(". ", i)]

  # Sample indices
  replace_indices_comma = random.sample(comma_indices, min(replace_count_comma, len(comma_indices)))
  replace_indices_period = random.sample(period_indices, min(replace_count_period, len(period_indices)))

  # Apply replacements
  for idx in sorted(replace_indices_comma + replace_indices_period, reverse=True):
    if text.startswith(", ", idx):
      text = text[:idx] + " ," + text[idx + 2:]
    if text.startswith(". ", idx):
      text = text[:idx] + " ." + text[idx + 2:]

  return text

def transform_paragraph(paragraph):
    words = paragraph.split()
    if len(words) > 12:
        words = [random_capitalize(word) for word in words]

        transformed_paragraph = ' '.join(words)
        transformed_paragraph = random_remove_punctuation(transformed_paragraph)
        transformed_paragraph = random_double_period(transformed_paragraph)
        transformed_paragraph = random_double_space(transformed_paragraph)
        transformed_paragraph = random_replace_comma_space(transformed_paragraph)
    else:
        transformed_paragraph = paragraph

    transformed_paragraph = transformed_paragraph.replace("#", "*")
    transformed_paragraph = transformed_paragraph.replace("*", "")
    # transformed_paragraph = transformed_paragraph.replace(", ", " ,")

    return transformed_paragraph

def transform_text(text):
    paragraphs = text.split('\n')
    transformed_paragraphs = [transform_paragraph(paragraph) for paragraph in paragraphs]
    return '\n'.join(transformed_paragraphs)

# function to humanize text
def humanize_text(AI_text):
    """Humanizes the provided AI text using the fine-tuned model."""
    response = client.chat.completions.create(
        model=finetuned_model,
        temperature = 0.87,
        messages=[
            {"role": "system", "content": """
            You are a text humanizer.
            You humanize AI generated text.
            The text must appear like humanly written.
            THE INPUT AND THE OUTPUT HEADINGS MUST BE SAME. NO HEADING SHOULD BE MISSED.
            NAMES LIKE NOVEL NAME SHOULD REMAIN INTACT WITHOUT ANY CHANGE.
            THE INPUT AND THE OUTPUT SHOULD HAVE SAME WORD COUNT.
            THE OUTPUT SENTENCES MUST NOT BE SIMPLE. THEY SHOULD BE COMPOUND, COMPLEX, OR COMPOUND COMPLEX. 
            ABOVE ALL, THE GRAMMAR AND THE SENSE OF THE SENTENCES MUST BE TOP NOTCH - DO NOT COMPROMISE ON THAT."""},
            {"role": "system", "content": "YOU ARE TEXT HUMANIZER BUT YOU DO NOT REDUCE THE LENGTH OF THE SENTENCES. YOUR OUTPUT SENTENCES ARE OF EXACTLY THE SAME LENGTH AS THE INPUT"},
            {"role": "user", "content": f"THE LANGUAGE OF THE INPUT AND THE OUTPUT MUST BE SAME. THE SENTENCES SHOULD NOT BE SHORT LENGTH - THEY SHOULD BE SAME AS IN THE INPUT. ALSO THE PARAGRAPHS SHOULD NOT BE SHORT EITHER - PARAGRAPHS MUST HAVE THE SAME LENGTH"},
            {"role": "user", "content": f"THE GRAMMAR AND THE QUALITY OF THE SENTENCES MUST BE TOP NOTCH - EASY TO UNDERSTAND AND NO GRAMMATICAL ERRORS."},
            {"role": "user", "content": "Use as many conjunctions and punctuations to make the sentence long. COMPOUND, COMPLEX, OR COMPOUND COMPLEX sentences are required"},
            {"role": "user", "content": f"Humanize the text. Keep the output format i.e. the bullets and the headings as it is. THE GRAMMAR MUST BE TOP NOTCH WITH NO ERRORS AND EASY TO UNDERSTAND!!!!. \nTEXT: {AI_text}"}
        ]
    )

    return response.choices[0].message.content.strip()



def main_function(AI_text):
    humanized_text = humanize_text(AI_text)
    humanized_text= transform_text(humanized_text)
    return humanized_text




# Gradio interface definition
interface = gr.Interface(
  fn=main_function,
  inputs="textbox",
  outputs="textbox",
  title="AI Text Humanizer",
  description="Enter AI-generated text and get a human-written version.",
)


# Launch the Gradio app
interface.launch(debug = True)






# import gradio as gr

# # Function to handle text submission
# def contact_info(text):
#     return "Contact farhan.sid1111@gmail.com for Humanizer Application service"

# # Gradio interface definition
# interface = gr.Interface(
#     fn=contact_info,
#     inputs="textbox",
#     outputs="text",
#     title="AI TEXT HUMANIZER",
#     description="Enter AI text and get its humanizer equivalent"
# )

# # Launch the Gradio app
# if __name__ == "__main__":
#     interface.launch()