asd

app.py

@@ -118,6 +118,85 @@
 # # 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
@@ -134,7 +213,7 @@ 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="tommyliphys/ai-detector-distilbert")
+pipe = pipeline("text-classification", model="andreas122001/roberta-mixed-detector")
 
 # Define the function to get predictions
 def get_prediction(text):
@@ -195,4 +274,4 @@ interface = gr.Interface(
 )
 
 # Launch the Gradio app
-interface.launch(debug=True)
+interface.launch(debug=True)