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app.py
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# import dependencies
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import gradio as gr
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from openai import OpenAI
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import os
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import re
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# define the openai key
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api_key = "sk-proj-UCoZZMs4MyfyHwXdHjT8T3BlbkFJjYkSZyPfIPNqXfXwoekm"
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# make an instance of the openai client
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client = OpenAI(api_key = api_key)
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# finetuned model instance
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finetuned_model = "ft:gpt-3.5-turbo-0125:personal::9qGC8cwZ"
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# function to humanize the text
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def humanize_text(AI_text):
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"""Humanizes the provided AI text using the fine-tuned model."""
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response = completion = client.chat.completions.create(
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model=finetuned_model,
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temperature = 0.86,
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messages=[
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{"role": "system", "content": """
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You are a text humanizer.
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You humanize AI generated text.
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The text must appear like humanly written.
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THE INPUT AND THE OUTPUT TEXT SHOULD HAVE THE SAME FORMAT.
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THE HEADINGS AND THE BULLETS IN THE INPUT SHOULD REMAIN IN PLACE"""},
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{"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"},
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{"role": "user", "content": f"Humanize the text. Keep the output format i.e. the bullets and the headings as it is and dont use the list of words that are not permissible. \nTEXT: {AI_text}"}
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]
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)
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humanized_text = response.choices[0].message.content.strip()
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return humanized_text
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# Gradio interface definition
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interface = gr.Interface(
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fn=humanize_text,
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inputs="textbox",
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outputs="textbox",
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title="AI Text Humanizer: NoaiGPT.com Demo",
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description="Enter AI-generated text and get a human-written version.",
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)
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# Launch the Gradio app
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interface.launch(debug = True)
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# import dependencies
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# import gradio as gr
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# from openai import OpenAI
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# import os
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# import re
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# from transformers import pipeline
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# # define the openai key
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# api_key = "sk-proj-UCoZZMs4MyfyHwXdHjT8T3BlbkFJjYkSZyPfIPNqXfXwoekm"
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@@ -62,59 +10,111 @@ interface.launch(debug = True)
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# # make an instance of the openai client
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# client = OpenAI(api_key = api_key)
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# # finetuned model instance
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# finetuned_model = "ft:gpt-3.5-turbo-0125:personal::9qGC8cwZ"
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# # Load the AI detection model
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# pipe = pipeline("text-classification", model="tommyliphys/ai-detector-distilbert")
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# # Define the function to get predictions
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# def get_prediction(text):
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# return pipe(text)[0]
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# # function to humanize the text
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# def humanize_text(AI_text):
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# )
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# humanized_text = response.choices[0].message.content.strip()
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# # Check if the humanized text is still detected as AI
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# prediction = get_prediction(humanized_text)
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# if prediction['label'] != 'AI' or prediction['score'] < 0.9:
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# break
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# attempts += 1
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# return humanized_text
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# # Gradio interface definition
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# interface = gr.Interface(
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# )
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# # Launch the Gradio app
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# interface.launch(debug=True)
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# # import dependencies
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# import gradio as gr
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# from openai import OpenAI
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# import os
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# import re
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6 |
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# # define the openai key
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# api_key = "sk-proj-UCoZZMs4MyfyHwXdHjT8T3BlbkFJjYkSZyPfIPNqXfXwoekm"
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# # make an instance of the openai client
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# client = OpenAI(api_key = api_key)
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# # finetuned model instance
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# finetuned_model = "ft:gpt-3.5-turbo-0125:personal::9qGC8cwZ"
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# # function to humanize the text
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# def humanize_text(AI_text):
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# """Humanizes the provided AI text using the fine-tuned model."""
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# response = completion = client.chat.completions.create(
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# model=finetuned_model,
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# temperature = 0.86,
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# messages=[
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# {"role": "system", "content": """
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# You are a text humanizer.
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# You humanize AI generated text.
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# The text must appear like humanly written.
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# THE INPUT AND THE OUTPUT TEXT SHOULD HAVE THE SAME FORMAT.
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# THE HEADINGS AND THE BULLETS IN THE INPUT SHOULD REMAIN IN PLACE"""},
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# {"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"},
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# {"role": "user", "content": f"Humanize the text. Keep the output format i.e. the bullets and the headings as it is and dont use the list of words that are not permissible. \nTEXT: {AI_text}"}
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# ]
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# )
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# humanized_text = response.choices[0].message.content.strip()
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# return humanized_text
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# # Gradio interface definition
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# interface = gr.Interface(
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# fn=humanize_text,
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# inputs="textbox",
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# outputs="textbox",
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# title="AI Text Humanizer: NoaiGPT.com Demo",
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# description="Enter AI-generated text and get a human-written version.",
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# )
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# # Launch the Gradio app
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# interface.launch(debug = True)
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import dependencies
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import gradio as gr
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from openai import OpenAI
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import os
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import re
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from transformers import pipeline
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+
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# define the openai key
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api_key = "sk-proj-UCoZZMs4MyfyHwXdHjT8T3BlbkFJjYkSZyPfIPNqXfXwoekm"
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+
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# make an instance of the openai client
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client = OpenAI(api_key = api_key)
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+
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# finetuned model instance
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finetuned_model = "ft:gpt-3.5-turbo-0125:personal::9qGC8cwZ"
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+
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# Load the AI detection model
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pipe = pipeline("text-classification", model="tommyliphys/ai-detector-distilbert")
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# Define the function to get predictions
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def get_prediction(text):
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return pipe(text)[0]
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# function to humanize the text
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def humanize_text(AI_text):
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"""Humanizes the provided AI text using the fine-tuned model."""
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humanized_text = AI_text
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attempts = 0
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max_attempts = 5
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while attempts < max_attempts:
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response = client.chat.completions.create(
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model=finetuned_model,
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temperature=1.0,
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messages=[
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{"role": "system", "content": """
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You are a text humanizer.
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You humanize AI generated text.
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The text must appear like humanly written.
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THE INPUT AND THE OUTPUT TEXT SHOULD HAVE THE SAME FORMAT.
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THE HEADINGS AND THE BULLETS IN THE INPUT SHOULD REMAIN IN PLACE"""},
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{"role": "user", "content": "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"},
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{"role": "user", "content": f"Humanize the text. Keep the output format i.e. the bullets and the headings as it is and dont use the list of words that are not permissible. \nTEXT: {humanized_text}"}
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]
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)
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humanized_text = response.choices[0].message.content.strip()
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# Check if the humanized text is still detected as AI
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prediction = get_prediction(humanized_text)
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if prediction['label'] != 'AI' or prediction['score'] < 0.9:
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break
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attempts += 1
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return humanized_text
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# Gradio interface definition
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interface = gr.Interface(
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fn=humanize_text,
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inputs="textbox",
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outputs="textbox",
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title="AI Text Humanizer: NoaiGPT.com Demo",
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description="Enter AI-generated text and get a human-written version.",
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)
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# Launch the Gradio app
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interface.launch(debug=True)
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