asd

app.py

@@ -1,8 +1,59 @@
-# # # import dependencies
+# # # # import dependencies
+# # # import gradio as gr
+# # # from openai import OpenAI
+# # # import os
+# # # import re
+
+# # # # 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"
+
+# # # # function to humanize the text
+# # # def humanize_text(AI_text):
+# # #   """Humanizes the provided AI text using the fine-tuned model."""
+# # #   response = completion = client.chat.completions.create(
+# # #   model=finetuned_model,
+# # #   temperature = 0.86,
+# # #   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": 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"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}"}
+# # #   ]
+# # #   )
+
+# # #   humanized_text = response.choices[0].message.content.strip()
+
+# # #   return humanized_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
 # # import re
+# # from transformers import pipeline
 
 # # # define the openai key
 # # api_key = "sk-proj-UCoZZMs4MyfyHwXdHjT8T3BlbkFJjYkSZyPfIPNqXfXwoekm"
@@ -10,44 +61,62 @@
 # # # 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 humanize the text
 # # def humanize_text(AI_text):
-# #   """Humanizes the provided AI text using the fine-tuned model."""
-# #   response = completion = client.chat.completions.create(
-# #   model=finetuned_model,
-# #   temperature = 0.86,
-# #   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": 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"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}"}
-# #   ]
-# #   )
-
-# #   humanized_text = response.choices[0].message.content.strip()
-
-# #   return humanized_text
+# #     """Humanizes the provided AI text using the fine-tuned model."""
+# #     humanized_text = AI_text
+# #     attempts = 0
+# #     max_attempts = 5
+
+# #     while attempts < max_attempts:
+# #         response = client.chat.completions.create(
+# #             model=finetuned_model,
+# #             temperature=0.85,
+# #             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'] != 'AI':
+# #             break
+        
+# #         attempts += 1
 
+# #     return humanized_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.",
+# #     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)
+# # interface.launch(debug=True)
 
 # import gradio as gr
 # from openai import OpenAI
@@ -56,10 +125,10 @@
 # from transformers import pipeline
 
 # # define the openai key
-# api_key = "sk-proj-UCoZZMs4MyfyHwXdHjT8T3BlbkFJjYkSZyPfIPNqXfXwoekm"
+# api_key = "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"
 
 # # make an instance of the openai client
-# client = OpenAI(api_key = api_key)
+# client = OpenAI(api_key=api_key)
 
 # # finetuned model instance
 # finetuned_model = "ft:gpt-3.5-turbo-0125:personal::9qGC8cwZ"
@@ -71,17 +140,25 @@
 # 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 = 5
+#     max_attempts = 10
 
 #     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.
@@ -99,12 +176,14 @@
 #         # Check if the humanized text is still detected as AI
 #         prediction = get_prediction(humanized_text)
         
-#         if prediction['label'] != 'AI':
+#         if prediction['label'] == 'human' and prediction['score'] > 0.9:
 #             break
         
 #         attempts += 1
 
-#     return humanized_text
+#     # Clean the humanized text
+#     cleaned_text = clean_text(humanized_text)
+#     return cleaned_text
 
 # # Gradio interface definition
 # interface = gr.Interface(
@@ -122,7 +201,6 @@ import gradio as gr
 from openai import OpenAI
 import os
 import re
-from transformers import pipeline
 
 # define the openai key
 api_key = "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"
@@ -133,13 +211,6 @@ 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
@@ -173,12 +244,6 @@ def humanize_text(AI_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
@@ -195,4 +260,4 @@ interface = gr.Interface(
 )
 
 # Launch the Gradio app
-interface.launch(debug=True)
+interface.launch(debug=True)