app.py

import gradio as gr
import openai
import os
import re
from transformers import pipeline

title = "System Prompt Depersonalizer"
description = """
This app transforms personalized system prompts into generalized versions that can be shared with a wider audience.
You can use either OpenAI's API (requires API key) or Hugging Face's models (free).
"""

# Validate OpenAI API key format
def validate_api_key(api_key):
    if not api_key:
        return False
    # Check if it starts with "sk-" and has appropriate length
    return bool(re.match(r'^sk-[A-Za-z0-9]{32,}$', api_key))

# Define depersonalization function using OpenAI (v1.0+ syntax)
def depersonalize_prompt_openai(prompt, api_key):
    if not validate_api_key(api_key):
        return "Error: Invalid API key format. OpenAI API keys should start with 'sk-' followed by at least 32 characters."
    
    try:
        client = openai.OpenAI(api_key=api_key)
        response = client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[
                {"role": "system", "content": """
You are an AI assistant specializing in transforming personalized system prompts into generalized versions that can be shared with and used by a wider audience. Your task is to identify and remove personal elements while preserving the core functionality and purpose of the original prompt.

Task Breakdown:
1. Analyze the Original Prompt
   - Identify personalized elements such as:
     * Names (e.g., Daniel Rosehill)
     * Specific hardware or software configurations
     * Location-specific references
     * Unique use cases or workflows
     * Personal preferences or requirements

2. Generalize the Content
   - Replace personal references with generic alternatives
   - Broaden specific technical requirements when appropriate
   - Maintain the core functionality and purpose
   - Preserve the overall structure and flow of instructions

3. Maintain Quality
   - Preserve clear instructions and constraints
   - Keep specialized knowledge and capabilities
   - Ensure the prompt remains coherent and effective
   - Retain unique value propositions of the original

Output Format:
Provide only the depersonalized system prompt in Markdown format inside a code block. Do not include any other commentary or explanation.
"""},
                {"role": "user", "content": prompt}
            ],
            max_tokens=1200
        )
        return response.choices[0].message.content
    except Exception as e:
        error_msg = str(e)
        if "API key" in error_msg.lower() or "authentication" in error_msg.lower():
            return "Error: Your API key was rejected by OpenAI. Please check that you've entered a valid API key."
        else:
            return f"Error: {error_msg}"

# Define depersonalization function using Hugging Face models
def depersonalize_prompt_hf(prompt):
    try:
        # Use a text generation pipeline with a suitable model
        generator = pipeline('text2text-generation', model='google/flan-t5-base')
        
        # Create a prompt that instructs the model to depersonalize
        instruction = """
        Transform this personalized system prompt into a generalized version by removing personal elements
        (names, specific hardware/software, locations, unique use cases, personal preferences)
        while preserving the core functionality and purpose:
        """
        
        full_prompt = instruction + "\n\n" + prompt
        
        # Generate the depersonalized version
        result = generator(full_prompt, max_length=1024, do_sample=False)
        
        return result[0]['generated_text']
    except Exception as e:
        return f"Error with Hugging Face model: {str(e)}"

# Function to route to the appropriate depersonalization method
def depersonalize_prompt(prompt, api_key, use_openai):
    if use_openai:
        if not api_key.strip():
            return "Error: OpenAI API key is required when using OpenAI. Please enter your API key or switch to Hugging Face."
        return depersonalize_prompt_openai(prompt, api_key)
    else:
        return depersonalize_prompt_hf(prompt)

# Build Gradio UI
with gr.Blocks() as demo:
    gr.Markdown(f"# {title}")
    gr.Markdown(description)
    
    with gr.Row():
        use_openai = gr.Checkbox(label="Use OpenAI (requires API key)", value=True)
    
    api_key_input = gr.Textbox(
        label="OpenAI API Key",
        placeholder="sk-...",
        type="password",
        visible=True
    )
    
    input_prompt = gr.Textbox(
        label="Personalized System Prompt",
        placeholder="Paste your personalized system prompt here...",
        lines=10
    )
    
    output_prompt = gr.Textbox(
        label="Depersonalized System Prompt",
        lines=10,
        interactive=True
    )
    
    with gr.Row():
        run_btn = gr.Button("Depersonalize")
        copy_btn = gr.Button("Copy Result")
    
    # Update API key input visibility based on checkbox
    def update_api_key_visibility(use_openai):
        return gr.update(visible=use_openai)
    
    use_openai.change(
        fn=update_api_key_visibility,
        inputs=[use_openai],
        outputs=[api_key_input]
    )
    
    run_btn.click(
        fn=depersonalize_prompt,
        inputs=[input_prompt, api_key_input, use_openai],
        outputs=output_prompt
    )
    
    copy_btn.click(
        fn=lambda x: x,
        inputs=[output_prompt],
        outputs=[],
        js="navigator.clipboard.writeText(args[0]); alert('Copied!');"
    )

demo.launch()