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Update app.py
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app.py
CHANGED
@@ -178,77 +178,35 @@ class PromptRefiner:
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}
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Transform content while maintaining clear visual separation between elements.
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},
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{
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"role": "user",
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"content": prompt
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}
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]
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''' messages = [
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{
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"role": "system",
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"content": """You are a professional markdown formatting expert. Transform any content into well-structured documentation following these precise rules:
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1. Document Structure:
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- Start with # for main title
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- Use ## for major sections
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- Use ### for subsections
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- Add > blockquote for key summaries or important notes
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- Separate major sections with ---
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2. Content Types:
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Technical:
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- Use ```language for code blocks
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- Format inline code with `backticks`
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- Use $$ $$ for math equations
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- Create tables for comparisons or data
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- Use bullet points for features/characteristics
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Narrative:
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- Format dialogue with proper quotations
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- Use *italics* for emphasis
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- Keep paragraphs focused and concise
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Instructional:
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- Use numbered lists for steps
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- Bold key terms with **emphasis**
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- Add examples in code blocks
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- Use tables for parameter explanations
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3. Visual Organization:
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- Maximum 3 levels of headers
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- Short paragraphs (3-5 sentences)
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- Consistent spacing between sections
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- Clear hierarchy in information
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- Strategic use of line breaks
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4. Special Elements:
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- Tables for structured comparisons
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- Fenced code blocks with language specification
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- Blockquotes for summaries/key points
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- Lists only when necessary
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- LaTeX for mathematical notation
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Transform the content while maintaining clarity, professionalism, and readability. Focus on creating a logical flow that enhances understanding."""
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},
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{
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"role": "user",
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"content": prompt
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}
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]
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model=model,
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messages=messages,
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max_tokens=3000,
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temperature=0.8
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)
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except Exception as e:
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class GradioInterface:
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def __init__(self, prompt_refiner: PromptRefiner,custom_css):
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@@ -352,7 +310,8 @@ class GradioInterface:
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apply_button.click(
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fn=self.apply_prompts,
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inputs=[prompt_text, refined_prompt, apply_model],
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outputs=[original_output, refined_output]
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)
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def refine_prompt(self, prompt: str, meta_prompt_choice: str) -> tuple:
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def apply_prompts(self, original_prompt: str, refined_prompt: str, model: str):
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original_output = self.prompt_refiner.apply_prompt(original_prompt, model)
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refined_output = self.prompt_refiner.apply_prompt(refined_prompt, model)
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def launch(self, share=False):
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self.interface.launch(share=share)
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"
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}
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Transform content while maintaining clear visual separation between elements."""
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},
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{
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"role": "user",
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"content": prompt
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}
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]
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# Use streaming for the response
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response_stream = self.client.text_generation(
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model=model,
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messages=messages,
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max_tokens=3000,
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temperature=0.8,
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stream=True
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)
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# Initialize an empty string to store the complete response
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full_response = ""
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# Process the stream
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for response_chunk in response_stream:
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if hasattr(response_chunk, 'token'):
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chunk_text = response_chunk.token.text
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full_response += chunk_text
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yield full_response.replace('\n\n', '\n').strip()
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except Exception as e:
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yield f"Error: {str(e)}"
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class GradioInterface:
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def __init__(self, prompt_refiner: PromptRefiner,custom_css):
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apply_button.click(
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fn=self.apply_prompts,
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inputs=[prompt_text, refined_prompt, apply_model],
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outputs=[original_output, refined_output],
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streaming=True
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)
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def refine_prompt(self, prompt: str, meta_prompt_choice: str) -> tuple:
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def apply_prompts(self, original_prompt: str, refined_prompt: str, model: str):
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original_output = self.prompt_refiner.apply_prompt(original_prompt, model)
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refined_output = self.prompt_refiner.apply_prompt(refined_prompt, model)
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# Create generators for both outputs
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for original, refined in zip(original_output, refined_output):
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yield original, refined
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def launch(self, share=False):
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self.interface.launch(share=share)
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