asd

app.py

@@ -118,85 +118,6 @@
 # # Launch the Gradio app
 # interface.launch(debug=True)
 
-# import gradio as gr
-# from openai import OpenAI
-# import os
-# import re
-# from transformers import pipeline
-
-# # define the openai key
-# api_key = "sk-proj-UCoZZMs4MyfyHwXdHjT8T3BlbkFJjYkSZyPfIPNqXfXwoekm"
-
-# # make an instance of the openai client
-# client = OpenAI(api_key=api_key)
-
-# # finetuned model instance
-# finetuned_model = "ft:gpt-3.5-turbo-0125:personal::9qGC8cwZ"
-
-# # Load the AI detection model
-# pipe = pipeline("text-classification", model="tommyliphys/ai-detector-distilbert")
-
-# # Define the function to get predictions
-# def get_prediction(text):
-#     return pipe(text)[0]
-
-# # Function to clean the text
-# def clean_text(text):
-#     # Remove double asterisks
-#     text = re.sub(r'\*\*', '', text)
-#     # Remove double hash symbols
-#     text = re.sub(r'##', '', text)
-#     return text
-
-# # function to humanize the text
-# def humanize_text(AI_text):
-#     """Humanizes the provided AI text using the fine-tuned model."""
-#     humanized_text = AI_text
-#     attempts = 0
-#     max_attempts = 10
-
-#     while attempts < max_attempts:
-#         response = client.chat.completions.create(
-#             model=finetuned_model,
-#             temperature=0.90,
-#             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 TEXT SHOULD HAVE THE SAME FORMAT.
-#                 THE HEADINGS AND THE BULLETS IN THE INPUT SHOULD REMAIN IN PLACE"""},
-#                 {"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"},
-#                 {"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}"}
-#             ]
-#         )
-
-#         humanized_text = response.choices[0].message.content.strip()
-        
-#         # Check if the humanized text is still detected as AI
-#         prediction = get_prediction(humanized_text)
-        
-#         if prediction['label'] == 'human' and prediction['score'] > 0.9:
-#             break
-        
-#         attempts += 1
-
-#     # Clean the humanized text
-#     cleaned_text = clean_text(humanized_text)
-#     return cleaned_text
-
-# # Gradio interface definition
-# interface = gr.Interface(
-#     fn=humanize_text,
-#     inputs="textbox",
-#     outputs="textbox",
-#     title="AI Text Humanizer: NoaiGPT.com Demo",
-#     description="Enter AI-generated text and get a human-written version.",
-# )
-
-# # Launch the Gradio app
-# interface.launch(debug=True)
-
 import gradio as gr
 from openai import OpenAI
 import os
@@ -213,7 +134,7 @@ client = OpenAI(api_key=api_key)
 finetuned_model = "ft:gpt-3.5-turbo-0125:personal::9qGC8cwZ"
 
 # Load the AI detection model
-pipe = pipeline("text-classification", model="andreas122001/roberta-mixed-detector")
+pipe = pipeline("text-classification", model="tommyliphys/ai-detector-distilbert")
 
 # Define the function to get predictions
 def get_prediction(text):
@@ -237,7 +158,7 @@ def humanize_text(AI_text):
     while attempts < max_attempts:
         response = client.chat.completions.create(
             model=finetuned_model,
-            temperature=0.85,
+            temperature=0.90,
             messages=[
                 {"role": "system", "content": """
                 You are a text humanizer.
@@ -254,10 +175,8 @@ def humanize_text(AI_text):
         
         # Check if the humanized text is still detected as AI
         prediction = get_prediction(humanized_text)
-        print(prediction)
-
         
-        if prediction['label'] == 'human-produced' and prediction['score'] > 0.8:
+        if prediction['label'] == 'human' and prediction['score'] > 0.9:
             break
         
         attempts += 1
@@ -276,4 +195,4 @@ interface = gr.Interface(
 )
 
 # Launch the Gradio app
-interface.launch(debug=True)
+interface.launch(debug=True)