import os import json import re from huggingface_hub import InferenceClient import gradio as gr from pydantic import BaseModel, Field from typing import Optional, Literal from huggingface_hub.errors import HfHubHTTPError from custom_css import custom_css from variables import * class PromptInput(BaseModel): text: str = Field(..., description="The initial prompt text") meta_prompt_choice: Literal["star","done","physics","morphosis", "verse", "phor","bolism","math","arpe"] = Field(..., description="Choice of meta prompt strategy") class RefinementOutput(BaseModel): query_analysis: Optional[str] = None initial_prompt_evaluation: Optional[str] = None refined_prompt: Optional[str] = None explanation_of_refinements: Optional[str] = None raw_content: Optional[str] = None class PromptRefiner: def __init__(self, api_token: str): self.client = InferenceClient(token=api_token, timeout=300) self.meta_prompts = { "morphosis": original_meta_prompt, "verse": new_meta_prompt, "physics": metaprompt1, "bolism": loic_metaprompt, "done": metadone, "star": echo_prompt_refiner, "math": math_meta_prompt, "arpe": autoregressive_metaprompt } def refine_prompt(self, prompt_input: PromptInput) -> tuple: try: # Select meta prompt using dictionary instead of if-elif chain selected_meta_prompt = self.meta_prompts.get( prompt_input.meta_prompt_choice, advanced_meta_prompt ) messages = [ { "role": "system", "content": 'You are an expert at refining and extending prompts. Given a basic prompt, provide a more detailed.' }, { "role": "user", "content": selected_meta_prompt.replace("[Insert initial prompt here]", prompt_input.text) } ] response = self.client.chat_completion( model=prompt_refiner_model, messages=messages, max_tokens=3000, temperature=0.8 ) response_content = response.choices[0].message.content.strip() # Parse the response result = self._parse_response(response_content) return ( result.get('initial_prompt_evaluation', ''), result.get('refined_prompt', ''), result.get('explanation_of_refinements', ''), result ) except HfHubHTTPError as e: return ( "Error: Model timeout. Please try again later.", "The selected model is currently experiencing high traffic.", "The selected model is currently experiencing high traffic.", {} ) except Exception as e: return ( f"Error: {str(e)}", "", "An unexpected error occurred.", {} ) def _parse_response(self, response_content: str) -> dict: try: # Try to find JSON in response json_match = re.search(r'\s*(.*?)\s*', response_content, re.DOTALL) if json_match: json_str = json_match.group(1) json_str = re.sub(r'\n\s*', ' ', json_str) json_str = json_str.replace('"', '\\"') json_output = json.loads(f'"{json_str}"') if isinstance(json_output, str): json_output = json.loads(json_output) output={ key: value.replace('\\"', '"') if isinstance(value, str) else value for key, value in json_output.items() } output['response_content']=json_output # Clean up JSON values return output # Fallback to regex parsing if no JSON found output = {} for key in ["initial_prompt_evaluation", "refined_prompt", "explanation_of_refinements"]: pattern = rf'"{key}":\s*"(.*?)"(?:,|\}})' match = re.search(pattern, response_content, re.DOTALL) output[key] = match.group(1).replace('\\n', '\n').replace('\\"', '"') if match else "" output['response_content']=response_content return output except (json.JSONDecodeError, ValueError) as e: print(f"Error parsing response: {e}") print(f"Raw content: {response_content}") return { "initial_prompt_evaluation": "Error parsing response", "refined_prompt": "", "explanation_of_refinements": str(e), 'response_content':str(e) } def apply_prompt(self, prompt: str, model: str) -> str: try: messages = [ { "role": "system", "content": "You are a helpful assistant. Answer in stylized version with latex format or markdown if relevant. Separate your answer into logical sections using level 2 headers (##) for sections and bolding (**) for subsections. Incorporate a variety of lists, headers, and text to make the answer visually appealing" }, { "role": "user", "content": prompt } ] response = self.client.chat_completion( model=model, messages=messages, max_tokens=3000, temperature=0.8 ) output = response.choices[0].message.content.strip() return output.replace('\n\n', '\n').strip() except Exception as e: return f"Error: {str(e)}" class GradioInterface: def __init__(self, prompt_refiner: PromptRefiner,custom_css): self.prompt_refiner = prompt_refiner custom_css = custom_css with gr.Blocks(css=custom_css, theme=gr.themes.Default()) as self.interface: with gr.Column(elem_classes=["container", "title-container"]): gr.Markdown("# PROMPT++") gr.Markdown("### Automating Prompt Engineering by Refining your Prompts") gr.Markdown("Learn how to generate an improved version of your prompts.") with gr.Column(elem_classes=["container", "input-container"]): prompt_text = gr.Textbox( label="Type your prompt (or let it empty to see metaprompt)", # elem_classes="no-background", #elem_classes="container2", lines=5 ) meta_prompt_choice = gr.Radio( ["star","done","physics","morphosis", "verse", "phor","bolism","math","arpe"], label="Choose Meta Prompt", value="star", elem_classes=["no-background", "radio-group"] # elem_classes=[ "radio-group"] ) refine_button = gr.Button("Refine Prompt") # Option 1: Put Examples here (before Meta Prompt explanation) with gr.Row(elem_classes=["container2"]): with gr.Accordion("Examples", open=False): gr.Examples( examples=[ ["Write a story on the end of prompt engineering replaced by an Ai specialized in refining prompts.", "star"], ["Tell me about that guy who invented the light bulb", "physics"], ["Explain the universe.", "star"], ["What's the population of New York City and how tall is the Empire State Building and who was the first mayor?", "morphosis"], ["List American presidents.", "verse"], ["Explain why the experiment failed.", "morphosis"], ["Is nuclear energy good?", "verse"], ["How does a computer work?", "phor"], ["How to make money fast?", "done"], ["how can you prove IT0's lemma in stochastic calculus ?", "arpe"], ], inputs=[prompt_text, meta_prompt_choice] ) with gr.Accordion("Meta Prompt explanation", open=False): gr.Markdown(explanation_markdown) # Option 2: Or put Examples here (after the button) # with gr.Accordion("Examples", open=False): # gr.Examples(...) with gr.Column(elem_classes=["container", "analysis-container"]): gr.Markdown(' ') gr.Markdown("### Initial prompt analysis") analysis_evaluation = gr.Markdown() gr.Markdown("### Refined Prompt") refined_prompt = gr.Textbox( label="Refined Prompt", interactive=True, show_label=True, # Must be True for copy button to show show_copy_button=True, # Adds the copy button # elem_classes="no-background" ) gr.Markdown("### Explanation of Refinements") explanation_of_refinements = gr.Markdown() with gr.Column(elem_classes=["container", "model-container"]): # gr.Markdown("## See MetaPrompt Impact") with gr.Row(): apply_model = gr.Dropdown(models, value="meta-llama/Llama-3.1-8B-Instruct", label="Choose the Model", container=False, # This removes the container around the dropdown scale=1, # Controls the width relative to other components min_width=300 # Sets minimum width in pixels # elem_classes="no-background" ) apply_button = gr.Button("Apply MetaPrompt") # with gr.Column(elem_classes=["container", "results-container"]): gr.Markdown("### Prompts on choosen model") with gr.Tabs(): with gr.TabItem("Original Prompt Output"): original_output = gr.Markdown() with gr.TabItem("Refined Prompt Output"): refined_output = gr.Markdown() with gr.Accordion("Full Response JSON", open=False, visible=True): full_response_json = gr.JSON() refine_button.click( fn=self.refine_prompt, inputs=[prompt_text, meta_prompt_choice], outputs=[analysis_evaluation, refined_prompt, explanation_of_refinements, full_response_json] ) apply_button.click( fn=self.apply_prompts, inputs=[prompt_text, refined_prompt, apply_model], outputs=[original_output, refined_output] ) def refine_prompt(self, prompt: str, meta_prompt_choice: str) -> tuple: input_data = PromptInput(text=prompt, meta_prompt_choice=meta_prompt_choice) # Since result is a tuple with 4 elements based on the return value of prompt_refiner.refine_prompt initial_prompt_evaluation, refined_prompt, explanation_refinements, full_response = self.prompt_refiner.refine_prompt(input_data) analysis_evaluation = f"\n\n{initial_prompt_evaluation}" return ( analysis_evaluation, refined_prompt, explanation_refinements, full_response ) def apply_prompts(self, original_prompt: str, refined_prompt: str, model: str): original_output = self.prompt_refiner.apply_prompt(original_prompt, model) refined_output = self.prompt_refiner.apply_prompt(refined_prompt, model) return original_output, refined_output def launch(self, share=False): self.interface.launch(share=share) #explanation_markdown = "".join([f"- **{key}**: {value}\n" for key, value in metaprompt_explanations.items()]) ''' meta_info="" api_token = os.getenv('HF_API_TOKEN') if not api_token: raise ValueError("HF_API_TOKEN not found in environment variables") metadone = os.getenv('metadone') prompt_refiner_model = os.getenv('prompt_refiner_model') echo_prompt_refiner = os.getenv('echo_prompt_refiner') metaprompt1 = os.getenv('metaprompt1') loic_metaprompt = os.getenv('loic_metaprompt') openai_metaprompt = os.getenv('openai_metaprompt') original_meta_prompt = os.getenv('original_meta_prompt') new_meta_prompt = os.getenv('new_meta_prompt') advanced_meta_prompt = os.getenv('advanced_meta_prompt') math_meta_prompt = os.getenv('metamath') autoregressive_metaprompt = os.getenv('autoregressive_metaprompt') ''' if __name__ == '__main__': prompt_refiner = PromptRefiner(api_token) gradio_interface = GradioInterface(prompt_refiner,custom_css) gradio_interface.launch(share=True)